One category was missing that I had never tested properly: the big open source models. The ones that fit on my RTX 5090 with 32 GB or on my Strix Halo with 61 GB usable by the GPU are too weak for this. They work for simple agent tasks, and no small model handles real programming tasks, let alone a complex software project.

Qwen 3.8 27B took 706 minutes to finish v4 on the Strix and scored 80; the local GLM-4.7-Flash scored 24. So don’t even try.

But what if we push harder? What can mid-sized models, 100 to 400 billion parameters, do running on a single rented B300? I could rent four of them and run the giant 750-billion or one-trillion models, and I’m not sure the cost-benefit is worth it. The data below answers.

TL;DR: 14 open models, one 288 GB B300 on Runpod at about 9 dollars an hour, the same v4 sabotage protocol. None of them serves fast the way it ships: GLM 5.3 Flash decoded at 10 tokens per second with the default configuration and at 141 after tuning, 14 times more from flags alone. Two open models, GLM 5.3 Flash and Qwen3.8 Flash-Next, scored 92.5, Claude Sonnet 5 level, and Swift 1.5 27B scored 88, Opus 4.6 level. None reached the frontier’s 100. The 397-billion flagship scored 68.5 and let an admin backdoor through, DeepSeek V4 Flash scored 86 in the cloud and 44.5 served by me, and Nemotron cheated on a regression test. The cloud API of the same model costs a dollar per run; the pod costs 218 dollars a day. Jump to B300 and Runpod, server tuning, the ranking, the interesting cases, the frontier comparison, the math or the conclusion.

What a B300 is, and what Runpod is

The B300 is the datacenter GPU of NVIDIA’s Blackwell Ultra generation, announced in March 2025. Each card has 288 GB of HBM3e memory, nine times my 5090, and about 15 petaflops of dense compute in NVFP4, the 4-bit format Blackwell does in hardware. It’s the card that goes inside the GB300 NVL72 rack, 72 of them interconnected, which AI companies buy by the thousands.

On its own, a B300 costs more than an apartment. What makes it accessible is renting by the hour.

Runpod is a cloud GPU provider that rents cards in “pods”, Linux containers with one or more GPUs, billed per second. You pick the card, an image template, get in over SSH, and pay while the pod is on, idle included. The B300 cost about 9 dollars an hour on their secure cloud the week of the test; an H100 goes for 3 to 4. The container disk is ephemeral: terminate the pod and the 250 GB of weights you downloaded are gone, and you download again.

The pod I used: one B300 with 275 GiB according to nvidia-smi (the same nominal 288 GB, in binary units), 344 vCPUs, 4 TB of host RAM, 700 GB of disk. The inference server was vLLM; the harness was the same OpenCode as every other model in the ranking, connected to the pod through an SSH tunnel. The model runs there; the code, the tests and the results stay on my machine.

Fitting in memory is the first filter. The B300 fits the 100 to 400-billion-parameter models quantized to NVFP4, plus the 27 to 35-billion ones in BF16. Left out are the 753-billion GLM 5.3 flagship, the 750-billion DeepSeek V4.1 Flash, the 1.6-trillion DeepSeek V4 Pro and the 2.8-trillion Kimi K3. For those it would take four B300s or more, about 36 dollars an hour.

The methodology

The protocol is the usual v4: the model builds a Rails app across seven accumulating sprints, and between sprints an independent reviewer, isolated from the model, injects 14 sabotages, from real CVEs to silent logic and test regressions, into the code the model itself wrote. The 0-to-100 score measures vigilance: how much the model catches and fixes without being told (full weight), how much it fixes after an explicit “do a security audit” in the last sprint (40% of the weight), and what stays unresolved (zero). Tiers are A from 83 up, B from 73 to 82, C from 51 to 72, D up to 50.

Two differences this round. The first is the reviewer: the five models that finished before the block were reviewed end to end by Codex, as in the previous rounds.

Midway through the batch OpenAI’s security service started refusing the injection and grading operation with a cybersecurity risk flag, even though it’s my code, on my machine, in a benchmark with 46 models already scored under the same protocol. I switched the reviewer to Claude Opus 4.8 for the remaining models, with the same injection plan, same catalog, same yardstick, and the provenance of every boundary is recorded per sprint. The expected impact on the score is none, because both execute the same written recipe.

The second is admission by speed, which is the subject of the next section. A model that decodes below 100 tokens per second doesn’t even get on the track.

Also: an open model is entitled to up to two short nudges per run for silly defects (a missing import, a wrong test entry point), recorded as “assisted”. Infrastructure failure, driver, loader, parser, is never charged to the model. And the pod ran one model at a time, with no access to any other model’s workspace or results.

None of them serves fast the way it ships

The first lesson cost a day. I downloaded GLM 5.3 Flash, 320 billion parameters with 18 active, quantized to NVFP4, 198 GB. I brought it up on vLLM with the configuration that “works”: it serves, passes the tool-call tests, answers. And it decoded at 10 tokens per second, with the GPU at 10% utilization, 209 watts out of the 1,100 the pod allows.

That’s unusable for programming. A coding agent generates tens of thousands of tokens per sprint; at 10 per second, a 20-minute sprint becomes more than four hours.

The same checkpoint, same 128K-token context, same FP8 KV cache, same test prompt, changing only the compile and mode flags: 141 tokens per second. Fourteen times faster, zero change in precision or weights.

The main culprit was --enforce-eager, which disables vLLM’s torch.compile and CUDA graph capture, and which every tutorial tells you to enable “to start faster”. It starts faster and serves 14 times slower. The measurement changed that flag together with the interactivity mode, so I didn’t isolate each one’s contribution.

Hence the admission rule: below 100 tokens per second I consider unusable, and no model enters the benchmark without clearing that on the median of two warm 256-token probes, after a cold one. To keep it from becoming a bottomless pit, tuning is bounded: at most three configurations and 45 minutes of wall-clock per model, including load and compilation. And tuning can never trade quality for speed: no shortening the context, lowering reasoning effort or quantizing further to hit the number.

The tweaks that paid off for everyone:

• Install a CUDA that compiles for the card. Runpod’s default image ships CUDA 12.8, whose nvcc cannot compile for compute_103a, the B300’s compute capability. Without 12.9, the NVFP4 kernels never compile.
• Never use --enforce-eager. Letting vLLM compile and capture CUDA graphs is the single biggest win.
• --performance-mode interactivity: optimize for single-stream latency, which is what a serial agent needs, instead of aggregate throughput.
• FP8 KV cache on the big MoEs, prefix caching and chunked prefill on all of them, confirmed by vLLM’s Prometheus counters instead of assumed: on an 11K-token prompt, the server reused exactly 8,576 prefix tokens on every repeat.
• The right tool and reasoning parser for each family. The wrong parser makes tool calls vanish silently or leaks the reasoning into the answer.

And the specific tweaks I found most interesting:

• NVFP4 only runs on vLLM. The 198 to 251 GB checkpoints in Blackwell’s native FP4 are the only way to fit on the card, and llama.cpp and Ollama simply don’t load those formats. Whoever lives on GGUF has no access to this range.
• Native speculative decoding. Qwen 3.8 27B went from 92 to 195 tokens per second with the MTP head the checkpoint itself ships, predicting three tokens per step, plus --language-model-only to drop the vision tower (measured on different pods, so no controlled ablation). Muse Glimmer reached 295 with the lightweight DFlash drafter it ships alongside, 15 speculated tokens per step. I only enabled it where an official drafter exists and where tool calling stayed correct; for Ornith a drafter exists as a separate release, with unknown acceptance rate, so it stayed off.
• The CUDA 12 loader. flashinfer JIT-compiles an NVFP4 MoE kernel that needs libcudart.so.12, and after installing CUDA 13 for Nemotron the library resolution broke. The symptom looked like OOM; the cause was an ld.so.conf. A repair script with ldd evidence before and after fixed it, and the 397B went on to serve at 144 tokens per second.
• One virtual environment per runtime. Nemotron 3.5 Super VL required its own vLLM 0.32 in a separate venv, because that’s what the official card pins, and that build wants CUDA 13. Every model family is a bespoke case: parser, quantization, draft head, runtime version and loader repair, all different.
• A parser bug that dropped the last call. vLLM’s Qwen tool parser discarded the last parameter of a call when the parameter closing tag was missing but the function one was present. A narrow repair, with 24 test cases, took that serving noise out of the model’s account.

The exact arguments for each model, the probe logs and the ledger of every tuning round are in the serving optimizations document and the B300 runbook in the repository.

Speed is almost free, once you let vLLM compile. The real work is finding out, model by model, which combination of toolkit, parser, quantization and draft head that checkpoint demands. I gave each one 45 minutes and needed them.

The ranking

v4 vigilance score, tier, memory footprint, decode speed on the admission probe (warm, single stream) and the cost of the seven sprints’ inference time only, at 9.09 dollars an hour. The cost excludes download, load, compilation, tuning and idle time; the real math comes further down.

Three have no score: Cohere’s Command A Plus (218 billion, 25 active, W4A4) got stuck in a loop rewriting the same model file 23 times, needed three nudges and still left the tests unable to run before the reviewer was blocked; Nex N2.5 Mini ran at 337 tokens per second and delivered invalid Ruby, contradictory migrations and broken authentication in two attempts, a valid bad result from a server that works; and Qwen3.5 122B was a conditional fallback in case the 397B didn’t finish, and the 397B finished.

Minutes are sprint time only, and Ornith’s include a sprint interrupted by infrastructure. Flash-Next, Swift, ThinkingCap, Muse and Qwen 27B took one nudge each; GLM 4.7, two; the others, none. The probe speed is an admission number: the throughput of a real coding session includes prefill, tools, tests and the agent’s choices. And note that the top two tie on score with opposite profiles: GLM spent 145 minutes and 22 dollars to reach the same 92.5 that Qwen3.8 Flash-Next did in 80 minutes and 12 dollars, with half the memory.

The interesting cases

Qwen3.8 Flash-Next: the best of the open models, by cost

Flash-Next is, in Qwen’s words, a preview of the Qwen 4 architecture: 125 billion language-model parameters with 6 billion active, plus a 51-billion n-gram embedding table that Qwen says can be offloaded to host memory, plus a 4-billion MTP head. Quantized to NVFP4 by NVIDIA, it took about 100 GB on the card (the checkpoint is 133 GB on disk). It was the only model in the second batch to resolve all 14 items: it caught the tenant leak unprompted, caught the compound test-integrity item, and wrote per-user isolation specs on its own, so the planted API IDOR had no way to stay green. At 195 tokens per second and 12 dollars of inference, it’s the best cost-benefit in this table.

GLM 5.3 Flash: the same 92.5, three times the cost

GLM scored the same with a much bigger architecture: 320 billion parameters, 18 active, 198 GB even in FP4. It resolved all 14 items, zero nudges, reviewed end to end by Codex.

But it took 145 minutes and consumed twice the memory for the same result. On Z.ai’s cloud, the same model scored 84.25 on a flat plan. With single-run noise, which in my history reaches nine points, the safest reading is that both sit in the same band.

Swift 1.5 and ThinkingCap: two 27B fine-tunes in Tier A

The two surprises. Swift 1.5, from UkisAI, and ThinkingCap 3.8, from BottleCap AI, are Qwen 3.8 27B fine-tunes trained to think less: Swift advertises up to 58% fewer reasoning tokens (on the GPQA median; on Terminal-Bench, 16%), ThinkingCap about 37% on average. Both fit in 56 GB in BF16, run at about 200 tokens per second with MTP, and scored 88 and 83.

Swift landed above Anthropic’s Opus 4.6. The base Qwen 3.8 27B, same architecture, scored 80.5 in 192 minutes; Swift scored 88 in 64. Swift caught sabotage earlier than the base; whether that was from thinking less, one run of each can’t prove.

A provenance caveat: the reviewer recorded that Swift read local commit messages that disclosed regressions, which weakens a literal reading of “entirely unaided discovery”. And it left one item unresolved, an inactive admin still accepted with a vacuous guarding test. Even so, of every model in this table, it’s the one I’d put on a card of my own.

Qwen3.5 397B: the flagship that let the backdoor through

The biggest model in the batch, 397 billion parameters with 17 active, 251 GB of NVFP4 on a 275 GB card, served at 140 tokens per second after the loader repair and scored 68.5, Tier C.

The case that defines the score: sabotage number 6 is a line admin? => admin || email.end_with?("@internal.example"), a privilege escalation by email domain. The 397B let it through in the normal sprints, let it through in sprint 7 with the explicit security-audit request, and wrote a passing test codifying the backdoor as expected behavior, in the same commit where it announced “Brakeman: 0 warnings”. Parameters did not buy vigilance.

Nemotron 3.5 Super VL: cheated on the test

NVIDIA’s Nemotron 3.5 Super VL, 120 billion with 12 active, released two days before I started testing and sold by NVIDIA as the fastest open agentic model in its class, with “leading accuracy” on multimodal tasks, scored 63. Sabotage number 2 removes a navigation link and the regression test looks for have_link('Sign in'). Nemotron changed the test to have_button('Sign in'), which passes on the login form’s submit button without restoring the link, and kept the cheat even after the reveal sprint.

It also tends to catch items a sprint late. And it was the one that required its own runtime: vLLM 0.32 with CUDA 13 in a separate environment.

DeepSeek V4 Flash: 86 in the cloud, 44.5 in my hands

The most important case for anyone considering self-hosting. DeepSeek V4 Flash, 284 billion with 13 active, scored 86, Tier A, for 97 cents through the API in September, and the 0731 version, exactly the one I downloaded, scored 85 for 1.94. The same public weights in mixed FP4 and FP8, served by me on the B300, scored 44.5, Tier D: it caught 4 items unprompted, 5 more at the reveal, and left 5 unresolved. It was the fastest to finish, 37 minutes, and the least vigilant.

The likely cause is divergence between the public checkpoint and what DeepSeek serves, or between their serving stack and mine: parser, chat template, reasoning mode, quantization. I followed the official card. What this proves is that the model’s name on Hugging Face doesn’t guarantee the hosted API’s quality, and that whoever is going to host needs to validate before trusting.

GLM 4.7 Flash: same as local, meaning bad

The 30-billion GLM 4.7 Flash in BF16, at 217 tokens per second, scored 33.5. At home, in GGUF Q5, it had scored 24. It needed two nudges, left nine items unresolved and delivered a migration with a syntax error. The nine-and-a-half-point difference fits within single-run noise; what remains is that the model is weak either way.

What showed up in several

Ornith, Muse Glimmer and the 397B shared a hygiene defect: they ran the sprint to the end with exit zero and forgot to commit their own work, leaving it for the reviewer to checkpoint. It didn’t go into the score, and on a real team it would be the first complaint. And the 81-to-83 cluster, ThinkingCap, Ornith and Muse, has a consistent profile: it catches SQL injection, tenant leaks, XSS and exposed secrets with confidence, and lets the quiet items through, broken access control, test integrity, missing index, wrong aggregate.

How far from the frontier

The three best open models against the best commercial ones from the same ranking, with the cost and time of each run:

The reading: the best of the open source that fits on a B300 and that I served sits one step below the commercial ceiling and level with the middle of Tier A. Differences of one or two points are single-run noise.

GLM 5.3 Flash and Qwen3.8 Flash-Next tie with Sonnet 5 and pass Gemini 3.8 Flash and Opus 4.6. None reaches the 100 that seven commercial models scored, nor Grok 4.6’s 98.5. Seven and a half points separate the best open model from the ceiling, and in v4 every point is one less sabotage caught unprompted.

The difference that matters is another: predictability. Among the top commercial models in the table, the worst is 87.5, and the full commercial field also has a bottom, Mistral Large 3 scored 39. The open field has the same 92.5 at the top and two Tier D at the bottom, with DeepSeek scoring 85 in the cloud and 44.5 in my hands. Choosing a top commercial model is choosing a number the API reproduces; choosing an open one is betting that the checkpoint, the quantization and your serving stack reproduce the number on the card, and sometimes they do.

The math

If quality still falls short of the frontier, is the cost at least proportional? It depends on where you run the open model.

On the vendor’s own cloud API, yes, and by a wide margin. A full v4 run cost 97 cents on DeepSeek V4 Flash, 1.17 dollars on Qwen 3.8 Flash, 1.03 on MiMo V2.5 Pro, all Tier A in the cloud. Against 9.52 to 30.55 dollars for the 100-point models, that’s ten to thirty times cheaper for a score 8 to 14 points lower. The list prices explain it: GLM 5.3 Flash costs 15 cents per million input tokens and 50 for output; DeepSeek Flash, 15 to 30 cents input and 60 cents to 1.20 output; Opus 5.5, 4 and 20 dollars; GPT-6 Astra, 10 and 50.

On your own rented B300, no. The pod costs 9 dollars an hour on or idle, 218 a day, about 6,500 a month. The model time of this whole batch, 14 models across download, load and sprints, added up to 23 hours, about 212 dollars at pod price; the sprints alone were 17 hours and 157 dollars.

But the pod also charges for the time when nothing runs. I put 400 dollars of credit on Runpod and 127 were left: the whole batch cost 273 dollars, about 30 pod-hours, including storage, four pods in sequence and seven idle hours one night when the orchestration died and nothing warned me. Almost 60 dollars more than the model time, just waiting. The repository’s break-even math: the pod only gets cheaper than the API from about 6,500 runs a month, 217 a day, sustained. A solo developer or a small team doesn’t come close.

And model against model, side by side:

The cloud API of an open model is ten to thirty times cheaper than the frontier for one tier less. Hosting the same model on a rented B300 is more expensive than the frontier, and sometimes worse than its own API.

Does a team fit on the same card?

A question I asked while looking at the GPU at 10% utilization: do these models use all of the pod’s compute? Could a whole team go on the same B300?

In principle, yes, and that’s what vLLM exists for. Continuous batching schedules several requests in the same decode step, and PagedAttention stores each session’s KV cache in non-contiguous blocks, with under 4% waste, precisely to fit more sessions in the same memory. A 27-billion model in BF16 takes 56 GB and leaves about 200 GB of KV cache: that’s dozens of agent sessions with 128K-token context at the same time. The 397B, with 251 GB of weights, leaves 24, and then few fit.

What doesn’t scale is speed per person. My test ran in --performance-mode interactivity, which optimizes single-stream latency, and the 200 tokens per second are that stream’s. With ten agents decoding at the same time, the card’s aggregate throughput rises and each one’s falls, and the speculative decoding that gave me Muse Glimmer’s 295 yields less as concurrency grows: a DFlash drafter published for Muse by Z Lab reports 3 to 4.6 times gain with one session and 1.2 to 1.7 with 32.

I didn’t measure concurrency in this batch, so what I can say is the outline: a team of five to ten people on a 27-to-35-billion model on one B300 is practical, and each one probably still stays above 100 tokens per second. The same team on the 397B is unworkable on the card, and on the 198 GB GLM 5.3 Flash it gets tight.

It’s in that scenario, and only there, that the pod math starts to close. Ten developers running an agent all day on a 27B model would spend, on the API, at a dollar per two-hour run, about 40 dollars a day; the pod costs 218. It still loses. What changes the math is having a hundred people, or being required to keep the code from leaving your network.

Is owning your own infra worth it?

For serious software development, with this batch’s data, the answer comes in three parts.

The top open models are good enough. GLM 5.3 Flash, Qwen3.8 Flash-Next and Swift 1.5 27B are Tier A in my test, at Sonnet 5 level and above Opus 4.6. That was unthinkable in April, when only Qwen 3.6 35B came close to Opus, and still one fix away. For anyone who accepts one tier below the ceiling, they serve for real work, including security review, which is what v4 measures.

Renting a B300 to run them is the wrong way to use them. The cloud API of the same model costs a dollar per run and needs zero configuration. The pod costs 218 a day, demands a day of tuning per model family, and in DeepSeek’s case delivered a worse model than its own API. It’s only worth it with a data-residency obligation, with a checkpoint no cloud serves, with sustained load of hundreds of runs a day, or with a need for pinned weights for reproducibility. Four B300s to run the 750-billion-to-one-trillion models quadruple the bill with no evidence the score reaches 100: the 397B, the biggest model I tested, scored 68.5.

And if you’re going to host, host small. Swift 1.5 27B and ThinkingCap at 56 GB, with MTP, at 200 tokens per second, are Tier A on hardware you can buy, a professional 80 or 96 GB card instead of a datacenter one. That’s where open source makes sense for a company: a whole team on one card, code inside the network, an Opus 4.6 score. Just check the license first: Swift and ThinkingCap ship with their own licenses, and above a certain revenue both require a commercial license. The open giants remain an API thing.

My rule after all this: top commercial model for tasks that can’t fail, open-model API for volume, and your own card only when the law or the client demands it. The repository with every serving argument, every probe log, every reviewer transcript and the Codex-or-Claude provenance of every boundary is public, as always.

I expected a fast car. I didn’t expect one with ten compressed-air propulsion nozzles using SpaceX-derived technology, plus an underbody suction system capable, according to Elon Musk, of pulling up to 2 G. During the demo, Tesla announced zero to 60 miles per hour, nearly 97 km/h, in 0.81 seconds. It did not disclose the measurement protocol.

This is the future I wanted to see.

The entire demonstration is in Tesla’s official livestream. Road & Track measured and described the maneuvers, while TechCrunch detailed the three versions: Performance with active aerodynamics and suction, Thrust with rear thrusters, and Rocket with a ten-nozzle propulsion system and a single seat.

Of course, we watched a prototype in a controlled demonstration. Tesla expects production to begin in 2028, and the Rocket version is not approved for public-road use. Still, an actual car left the ground, rotated, and landed. I watched it live from my office. It was spectacular.

While Musk was landing rockets and making cars fly, I was doing something far smaller and more mundane: fiddling with my websites.

Six sites in about a day

Before the Tesla event, I spent the entire day redesigning six sites:

• Akita’s AI Lair, now matching the look of my new book, Competent Enough;
• ai-memory;
• ai-jail;
• The M.Akita Chronicles newsletter;
• the newsletter archive;
• and this blog, AkitaOnRails.com.

They all started with variations of that default theme Claude makes. Dark background, purple or blue gradient, huge sans-serif type, rounded cards, a glow behind something. It works. It has also become so common that you can spot a “site made by Claude” before reading the title.

I had already tried asking Claude for different designs. It changed the color, moved the cards around, and delivered the same visual grammar. I posted some screens and the result of the change on Twitter. I’m no web designer, and for a while I accepted “it works.” Yesterday I got tired of it.

Someone suggested Stitch by Google. It’s an experimental Google Labs tool that turns instructions and images into editable interfaces and front-end code. The first version launched in 2025 on top of Gemini; the current version has become a design canvas with an agent, prototypes, and export to a DESIGN.md that other agents can follow.

I signed up, got the API token, and went to work. Stitch generated visual directions. Claude Code and Codex brought those directions into the real repositories, adapted the components, fixed responsiveness, replaced images, adjusted SEO, tested, and deployed. When one quota ran out, I continued with the other.

The six sites come from five repositories. Looking at them side by side made one thing clear: Stitch responds much better when it receives a concrete metaphor, the audience, and what the product needs to communicate. ai-memory became a memory archive. ai-jail became a cinematic prison. AI Lair adopted a 1950s and 1960s world’s fair, drawn from my book cover. The newsletter became a late-night broadcast, and this blog became an editorial archive. Five different identities instead of five variations of Claude’s same gradient.

The pretty prototype is the easy part. The job only ends when the visual language reaches internal pages, the article, the book reader, email, legal pages, error states, and mobile. That’s what the agents did inside the repositories. On ai-memory, for example, verification covered 102 pages in six languages, light and dark themes, desktop and mobile. On The M.Akita Chronicles, the same design had to work across signup, archive, newsletter detail, podcast, and the email template. A Stitch mockup solves none of that by itself.

Each site went through several rounds of prototypes. Some looked awful. Others had one good idea surrounded by bad decisions. I told the agents to throw them away, preserve one part, mix it with another attempt, and start over. Code is cheap now, so there is no reason to fall in love with the first version.

The result won’t win a design award. A professional designer would still find hundreds of things to improve. I know I could take it further by spending a few more days. It looks much better than it did, though, and for now it is competent enough.

Six existing sites redesigned, responsive, with SEO, image generation and replacement, HTML, CSS, and JavaScript adjustments, testing, and production deployment. One person. About a day. A year ago, that package would have taken weeks or months.

Keep the scale in perspective: these are small sites, mostly landing pages and content pages. Nobody is rebuilding Amazon.com’s entire collection of pages and systems in a day. The experiment shows how much repetitive work has already moved out of the way within a scope that one experienced person can understand and review. Large projects remain large, full of private rules, old integrations, strange states, and decisions that do not fit in a prompt.

Even the first proofreading pass can be automated

As part of the same process, I had agents review the sites as different readers. This wasn’t grammar correction. I wanted to know what someone would understand in the first few seconds, where they would get lost, which promise would sound suspicious, and whether they could complete a task without already knowing the product.

On ai-jail, I used two isolated sessions. One played a skeptical engineer who approves every prompt and had never heard of the project. The other was a non-engineer managing eight developers, trying to decide whether to approve the tool for the company. On ai-memory, I used three: an engineer learning AI tools, a manager evaluating a pilot, and a parent with no agent experience who worried about AGI and jobs. Each one read the site without seeing the others’ reports.

Anyone can do the same thing. Run the application locally or open a preview deployment, start fresh sessions, and give each agent a concrete task. A basic prompt can look like this:

Open this site on desktop and mobile: [URL].

You are [persona], know [context], and have never seen this product.
Your goal is [specific decision or task].

Navigate as this person. Do not read code or internal documentation first.
Record:
- what you understood in the first ten seconds;
- what you think the product does and who it is for;
- where you did not know the next step;
- contradictory, absolute, or hard-to-believe promises;
- terms the site uses before explaining them;
- reading, keyboard, contrast, touch, and responsiveness problems;
- what would stop you from trying, buying, or approving it.

For every finding, show the page and visible evidence.
Separate factual bugs, usability problems, and subjective preferences.
Do not propose a broad redesign. Suggest the smallest change that solves the problem.

I prefer three independent sessions over an agent roundtable. Once one reads another’s report, it starts agreeing by inertia. A separate session can then consolidate repeated points, discard matters of taste, and check every claim against the code and product documentation. A review can flag a sentence as wrong; it has no authority to invent the right one.

The most useful results tend to fall into four groups: positioning, information order, path to the first action, and trust. Traditional automated tests still handle broken links, contrast, accessibility, overflow, and error states. The persona tells you that a technically accessible button appeared five screens below where the person needed it.

During the ai-jail proofreading, both profiles agreed that the site took too long to show a concrete risk and started speaking like engineering documentation too early. The threat of a poisoned page moved directly below the hero. New sections explained “What changes on day one” and “For the person who must approve this,” including operating cost, limitations, and the keys a company actually exposes. The review also caught a technically incorrect claim repeated across several pages: the network does not stay entirely off by default. The agent’s own API host and, on Linux, registries remain accessible. This kind of reading catches documentation bugs too.

The ai-memory report found permissions described in contradictory ways, privacy promises that were too absolute, and the basic explanation buried under advanced features. During the second round, the profiles noticed that remote-access instructions told users to use HTTP before requiring TLS, that an unauthenticated curl promised the wrong response, and that important choices appeared far too late on mobile. The collection guide moved from about 4,364 pixels from the top to 1,617; the individual and team paths moved from 5,300 to 2,489. That’s an objective improvement born from simulated reading.

In the AI Lair redesign, the new-reader review reorganized the entry path. A later revision made it explicit that my setup with multiple agents and two monitors is a mature configuration, not a requirement for starting. One person, one project, and one agent are enough. It also produced a pilot plan for managers: a reversible task, non-sensitive data, an acceptance criterion, deadline, budget, and measurement. The next audit found invalid ARIA and an animation that hid the opening copy from assistive technologies; the fix took axe-core from four serious violations to zero.

Simulated personas still fall far short of human feedback. They carry the models’ same biases, may agree with the prompt, never feel the irritation of someone who lost twenty minutes, and never take the risk of entering their own credit card. The reports serve as hypotheses. A real user will always find the case that I, the agent, and the test suite failed to imagine.

Before the first user exists, however, the alternative is usually no review at all. Two or three independent readings already catch contradictory promises, unexplained jargon, buried information, and a flow that only makes sense to its author. These readings cost little and make the product less stupid before it reaches actual people.

HTML was never programming

For years, I watched people perform some remarkable mental gymnastics to argue that “HTML is a programming language” and therefore anyone who writes HTML is a programmer.

HTML is markup: it describes a document’s structure. CSS describes presentation. That has value, requires knowledge, and can become terribly complex. Accidental complexity does not turn markup into programming.

There is no shame in that. I started building sites in the 1990s. I received a Photoshop layout and turned it into HTML. I sliced images, built tables, adjusted pixels, opened the result in multiple browsers, went back, and tried again. It was honest work and paid my bills.

It was also basically mechanical work: copy, paste, adjust, look, repeat. Exactly the category computers should automate.

Today I still chose the visual direction, rejected bad proposals, decided what each site needed to communicate, and recognized when the result was good enough. The agents handled the grunt work. This should have happened years ago.

The bubble delayed automation

For much of the 2010s, US interest rates stayed near zero. Cheap money flooded funds, startups, and large companies. Hiring more people was easy, and the market absorbed any amount of manual labor disguised as engineering.

Low rates explain only part of tech’s expansion. A field breaking hiring records has little incentive to eliminate its own repetitive work. If you can put fifty more people to work assembling screens, you put fifty more people to work assembling screens.

Anyone who attended my talks at tech events in the 2010s may remember Constraint == Innovation. Yes, the original title used two equal signs. I gave that talk at DevConf 2019, the 83 slides are on my SlideShare, and I had already written the same argument in 2013 in “Constraints are liberating: less is more”.

It’s the old maxim: necessity is the mother of invention. Constraints force choices. Choices force priorities. When the conventional path cannot fit the money, deadline, or available capacity, someone has to find another path. That’s where real innovation often comes from.

Money funds expensive research, buys equipment, and gives people time to experiment. Excess money without pressure for results does something else: it hides inefficiency with brute force. There is no need to automate, simplify, or discover a better solution while you can keep hiring and growing the spreadsheet. Too much abundance turns a technical problem into an operating expense and postpones the difficult decision.

I repeated this warning at the beginning of 2026: the programming bubble managed to replace efficiency with volume. Constraints returned when cheap money ended. Coincidentally, agents arrived at the same time and gave companies a practical way to automate the mechanical work they had spent a decade scaling through hiring.

That’s how we spent a decade recreating the same admin dashboard and calling it innovation. Every week brought the next front-end savior: React, Angular, Vue, Svelte, another bundler, another framework on top of the framework. They are useful tools in the right context, but none addressed the obvious question: why is a human still hand-writing the thousandth variation of the same form?

The hiring bubble burst in 2022 and ended the party. At the end of that year, ChatGPT brought language models to the public. GPT-2, just to keep the chronology straight, had come out in 2019. Four years after ChatGPT, agentic programming became a normal work tool.

Any task that consists of copying a pattern, adapting an example, running it, looking at the error, and trying again is already within these agents’ reach. Front-end is the most visible target because it accumulated an absurd amount of this work. The rest of programming has no mystical shield. Plenty of back-end work is CRUD, glue between APIs, and repetitive configuration. That will be automated too.

Fine by me. Programming was never about typing code. Programming is making a computer do something useful.

VS Code is the new punch card

I wrote about exactly this in April in “VS Code Is the New Punch Card”.

For decades, the interface between intention and computer kept changing. First you connected wires and switches. Then you punched cards. Then you wrote Assembly, C, high-level languages, frameworks, and configuration. Each generation looked at the previous one and automated another layer of manual work.

No sane person would argue today that operating a card punch was the essence of computer science. It was the available interface. Typing every tag and CSS rule into VS Code was also the available interface.

Now I describe the intention, show references, evaluate the result, and tell the agent to fix it. I still need to understand browsers, networks, caching, accessibility, semantics, responsiveness, and SEO. Without the fundamentals, I couldn’t tell whether the agent delivered a solution or a pretty time bomb. The keyboard lost importance; judgment gained it.

Today, every Tier A model I test, from Claude to Kimi, can reliably execute the mechanical front-end work that appeared in these six projects. I can say without hesitation that any front-end task that merely turns an already-made decision into code is automatable. They needed direction to create the six designs. None required me to hand-write the flexbox, breakpoint, or component I had already explained.

That covers an enormous share of the work while leaving the most valuable part. Someone still has to know the framework’s tradeoffs, understand the browser, choose the architecture, notice when a library is being forced against its design, and find out why an apparently correct solution breaks under a particular combination of device, language, state, and real data. The agent writes the code; an experienced professional provides direction, limits the damage, and owns the result.

Front-end still exists. The excess mechanical work and the people hired only to follow a finished recipe lose value. A great front-end professional can direct agents and deliver far more than they could alone. A mediocre professional who spent years mistaking component typing for engineering will compete for fewer openings. The market gets smaller and more selective, with its best professionals producing more. The goal remains what it always was: get close to the top, where judgment and experience still make a difference.

And it doesn’t stop at the browser. Pick the framework: Android with Flutter or Kotlin, iOS with Swift and SwiftUI, desktop with Rust/Tauri, Flutter, C#, or Electron. Modern agents generally know the components, build tools, tests, and peculiarities of these platforms. They still need direction, feedback, and verification, just as they do on the web front-end. The mechanical part is already automatable.

I tested this in my own small open-source projects. FrankYomik has a Flutter client for Android and Linux, plus the server and extension. Frank Sherlock is a Rust/Tauri desktop app that produces an AppImage for Linux, a DMG for macOS, and a .exe installer for Windows. I also built Frank Karaoke in Flutter for Android and FRANK MANGA+ in Rust/Tauri for desktop. There are several others on my GitHub.

None came out perfect from the first prompt. Flutter and Tauri gave me trouble, especially on the early projects. Reliable means the agent can move forward, receive a correction, create tests, fix what broke, and reach a working application without making me take over the manual typing. After testing web, Android, and desktop across different stacks, I’m very satisfied with the result. The pattern repeats: mechanical work became agent work.

At two in the morning, I had agents making the final adjustments to this blog without losing pace. That’s what I need.

When the web still didn’t know what it was

At the peak of the dot-com bubble, career designers were creating websites. The web had not yet decided how a page should work, so there was real room to experiment. It was often ugly and almost always heavy, but actual creation was happening.

The iPhone opened another window in 2007. Suddenly everything had to fit on a small screen, respond to touch, and work without a mouse. Years of experimentation followed before the market settled on a reasonably stable grammar for navigation, gestures, forms, and content.

Today nobody needs to rediscover a menu, a list, a checkout, or an article page. The patterns are documented, millions of examples are public, and models have seen more interfaces than any person could study in a lifetime.

The most valuable work remains: understanding the product, the person who will use it, the constraints, and what deserves to appear first. Converting a finished decision into hundreds of predictable lines has become a perfect candidate for automation.

Jobs disappear. Others appear

The world has never stopped to preserve a job function.

Punch-card operators entered data and programs on paper cards. Telephone operators physically connected calls. Typists kept offices running. Linotype operators cast lines of hot metal to print newspapers. Technology reduced or eliminated these occupations at scale. The Bureau of Labor Statistics’ current list of fastest-declining occupations still includes data entry keyers, typists, and telephone operators. Nobody proposes bringing them back to protect jobs.

At the same time, jobs appeared that a person in 1926 could not even name: mobile developer, SRE, experience designer, Internet video creator, cloud specialist, machine learning engineer. The web itself killed old work and opened an entire industry.

The real story is messier than the caricature. The ILO shows that technology tends to automate tasks without necessarily eliminating entire occupations. Some functions shrink, others change, and new jobs appear. That statistical consolation does nothing for the person who became obsolete today. The individual has to react before the economic average reacts for them.

Compare 2026 with 1926, three years before the crash of 1929. It looks like another planet, even though a century is very little on history’s scale. No contract guarantees that your current function will survive the next ten years, much less the next forty.

How does a junior gain experience now?

It will get harder. The old ladder began with someone paying a beginner to assemble simple screens, fix CSS, and copy components. An agent does much of that faster and cheaper. No company has an obligation to preserve inefficient work as a training program.

Beginners will have to build their own ladder with real projects and problems they are responsible for. That includes reading the code the agent generated, breaking and repairing it, studying documentation and source code, writing tests, and contributing to open projects. An agent gives any student access to an amount of practice that once required a team. Anyone who uses that access only to generate code they do not understand will remain a junior for a long time.

The market will reward people who make that extra effort. It always has. When someone asks how a junior can gain experience without the easy tasks, the hidden question is often different: “how do I get a better salary without doing the hard work of getting good?” The easy position, with rising pay and little pressure, was a distortion of the bubble. Adapting includes accepting that entry has become more competitive.

My career never stayed in one place

I started with multimedia and static websites. Then I worked in a digital agency, enterprise consulting, SAP integration, startups, video, education, and now AI. In my Akitando episode about SAP, I tell part of that phase when I had to learn a world completely different from what I had done before.

I lived through the dot-com crash, the 2008 crisis, and the end of the zero-rate bubble. I survived because I never treated my current function as a permanent identity. I kept studying, gaining experience, and changing direction.

A productive career should not consist of repeating the same mechanical task for decades. If your value depends on the market continuing to pay someone to push tags and pixels by hand, the problem did not begin with AI. The problem was betting that the present would last forever.

Many people understand “being prepared” as studying the bare minimum required for today’s open position. That’s the fastest recipe for becoming obsolete. Do the short-term work, but study beyond it. Learn the things that seem useless today. Fundamentals, operating systems, networks, databases, business, economics, history. You don’t know which combination will save your career five years from now.

I spent years talking about this on Akitando. Revisit the I Warned You and Which course should I take? playlists. If you have to start with one video, start with the Definitive Guide to Learning How to Learn. No course can save someone who still outsources their own learning.

The world has no shortage of opportunity. Most people feel they never get one because they are always unprepared when it appears. Once the opportunity lands in front of you, it is too late to start preparing.

So I won’t mourn the fact that this package of six small sites, which used to take weeks or months, fit into about a day. I’m happy as hell. I gained time to think about what to build, experiment more, and throw away bad versions without wasting weeks of my life.

Today I watched a car spin in midair and land. In about a day, I put six sites with new designs into production without writing most of the HTML and CSS. The future I wanted did not arrive perfect, cheap, or evenly distributed. It arrived far enough for me to work with it.

Accept what ended. Study what comes next. Be ready before the next opportunity appears.

But nobody needs a GPT-class model for everything. A frontier model is a generalist by definition: it tries to know everything about anything, from tax law to song lyrics, and the returns on that diminish.

And most of them today are mixtures of experts: roughly speaking, several smaller models clustered into one giant chunk of bits, with a router that wakes only a few of them for each token. OpenAI’s gpt-oss-120b has 117 billion parameters in total and uses 5.1 billion per token. DeepSeek-V3 has 671 billion and activates 37. The big model is already, on the inside, a collection of small models.

I could have grabbed an open model, gpt-oss, Qwen, Phi, and adapted it. The techniques exist and are well documented:

• Full fine-tuning: keep training all the weights of a ready model on your data. It’s classic transfer learning, formalized for text by ULMFiT in 2018.
• LoRA and QLoRA: freeze the original weights and train only small low-rank matrices beside them. LoRA cuts trainable parameters by up to ten thousand times; QLoRA quantizes the base model to 4 bits and fine-tunes a 65-billion model on a single 48 GB GPU.
• Distillation: train a smaller model to imitate a larger one’s outputs. The idea is from Hinton, 2015, and the most famous recent example is DeepSeek-R1 distilled into versions from 1.5 to 70 billion on top of Qwen and Llama.
• Continued pretraining: one more training phase on text from your domain before any instruction tuning, the “Don’t Stop Pretraining” of 2020.
• SFT, RLHF and DPO: teach the assistant format with demonstrations, then align with human preference via reinforcement learning (InstructGPT) or with a direct classification loss (DPO).

With any of those I’d have a useful model in hours. But the goal was different. I wanted to organize, hands-on, the basic techniques for building a model smaller than GPT-2, from scratch, in a few days, and see how far it would go. If possible, train a tiny model starting from random weights and add enough functionality for it to almost work inside an agentic harness like OpenCode.

Before going on, who I am in this story: I’m a curious software engineer and an amateur at this. As an LLM researcher I’m a zero. I knew I wouldn’t have a working LLM in such a short time, and that’s part of the exercise.

But it’s not unachievable. If you’re a computer science student, study hard and give it enough years, you can try it yourself. And if you’re not a scientist, understand this: nobody needs to be Einstein to do this. The foundations are all available, published, for you to study and try. What’s mandatory is elbow grease.

Spoiler: we got far into the “almost” and stopped there. The repository is public, with every step and every failure documented. This text is the summary.

TL;DR: I started from Karpathy’s microgpt, 200 lines of pure Python that train a GPT in one minute on a MacBook, and expanded it into a PyTorch lab with its own tokenizer, pretraining, supervised fine-tuning, an OpenAI-compatible server and OpenCode integration. In under two weeks, on one RTX 5090, I trained a 337-million-parameter model on 10 billion tokens: 137 hours of training, at most around R$ 80 of electricity. It gets 58 out of 100 simple Python functions right on a held-out set, and scores zero on anything that requires reading, editing and testing a repository. The 360-million SmolLM2, the same size, saw 4 trillion tokens, 400 times more. Jump to Karpathy’s exercise, the steps, the dimensions, the stumbles, the result or the conclusion.

Karpathy’s exercise

In February this year Andrej Karpathy published microgpt: a single file of 200 lines of Python, with no dependencies, that trains and runs a GPT. He calls it “the full algorithmic content of what is needed. Everything else is just efficiency”. It’s the culmination of a lineage of his: micrograd (scalar autograd), makemore (generating names) and nanoGPT (compact GPT-2 in PyTorch), joined by nanochat, “the best ChatGPT that $100 can buy”, which at launch trained a GPT-2-capability model in almost four hours on a node of eight H100s, and today does it in about two.

microgpt is deliberately minimal: the dataset is 32 thousand first names, one per line, and the tokenizer is one character per token.

The network has one layer, 16 embedding dimensions, 4 attention heads, a 16-token window because the longest name has 15 letters. It trains for a thousand steps, one name per step, with Adam written by hand, and at the end generates made-up names, all in one minute on a MacBook.

What it teaches, by having everything exposed in a single file:

• autograd, the Value class that holds a number, the gradient and the chain rule to propagate back;
• what a token and an embedding are;
• the attention block, with query, key and value, and the causal mask that stops the model from peeking at the future;
• the cross-entropy loss against the next token;
• the optimizer;
• sampling with temperature.

It’s a GPT-2 with small differences the file itself notes: RMSNorm instead of LayerNorm, no biases, ReLU instead of GeLU. After reading that, what’s left between you and a real model is, in his words, efficiency. I went to see how much efficiency.

What we expanded

The microgpt lab is an independent reimplementation in PyTorch, inspired by the gist, split by concept: about 3,300 lines of Python in 31 modules, plus scripts and 85 CPU tests. A single model implementation serves pretraining, assistant tuning and the server. Every weight starts random; the project downloads no pretrained weights at all, on purpose, because the rule of the exercise was from scratch.

On top of Karpathy’s block came the components a 2026 model uses: RoPE for position, RMSNorm, SwiGLU in the dense layer, embeddings tied to the output, Flash Attention through PyTorch, a key-value cache with prefix reuse. And around the model, what it needs to talk to an agent: a conversation protocol with roles, an HTTP server compatible with OpenAI’s chat API, tool calls validated by schema and optionally by grammar, separation of reasoning content, and the OpenCode configuration pointing at it.

The project went through four scales:

The table hides the first big lesson, which I tell in the stumbles: in the 239-million model, 154 million parameters were just the vocabulary table. 85 million were left for the actual transformer.

The steps of building an LLM

Regardless of size, the path has the same stations. What changes is the trailing zero at each one.

1. Data

A model is what it eats. We downloaded, with pinned revisions and hashes, selections of public corpora: CodeParrot (deduplicated, filtered Python), OpenCoder (instruction and code), Cosmopedia (synthetic educational text), SmolTalk (conversation) and a multilingual slice. The 14 download manifests add up to about 20 GB raw, all stored on the NAS.

Then comes curation: exact deduplication, splitting train and validation by repository or conversation group (never by line, or the same project leaks to both sides), and the mixture. The final foundation corpus has 3.5 billion stored tokens, 60% Python code, 16% instruction, 12% educational text, 8% conversation, 4% multilingual. Every proportion is a design decision, and none of them has a published right answer.

2. Tokenizer

The tokenizer is the dictionary between text and integers. The byte lesson uses 256 tokens plus one for end of document. The student used o200k, OpenAI’s public vocabulary with 200K entries, which is what most tutorials do. The pivot trained its own BPE with 32,768 entries, on 92 MB of training-only text, with full UTF-8 coverage.

The choice looks like a detail and decides where the parameters end up. Each vocabulary entry costs one row in the embedding table at the model’s width. With 200K entries and width 768, that’s 154 million parameters spent on the table. With 32K entries and width 1,024, it’s 33 million, and the rest goes to the layers that think.

3. Pretraining, or foundation

This is Karpathy’s loop at scale. The model receives 2,048-token windows drawn from the training stream and learns to predict the next token. Each update processes 32,768 tokens: two sequences, accumulated eight times. AdamW, learning rate with warmup and cosine decay, gradient clipping, BF16 on the GPU and FP32 for the weights.

The pivot ran about 305 thousand updates, 10 billion tokens of exposure. Since the corpus has 3.5 billion, each document was seen, in the mixture’s expectation, almost three times: the code about twice, and instruction, educational text and conversation about nine or ten. Every 500 updates, a validation and a checkpoint. At the 2, 5 and 10 billion milestones, a frozen checkpoint with a hash, to measure the curve.

4. Supervised fine-tuning

Pretraining produces a model that completes text. For it to answer a request comes SFT, supervised fine-tuning: question and answer examples in the exact format the server uses, with the loss computed only over the assistant’s tokens. What the user and the tool said goes in as context and is masked out of the loss.

The examples came from NVIDIA’s OpenCodeInstruct, and every answer was executed in a restricted Docker before going in: almost 99 thousand candidates, 97 thousand survived the test with three different hash seeds. 92 thousand training episodes remained. The selected checkpoint saw 64 thousand draws from those episodes, about 10 million answer tokens.

5. Protocol, tools and server

An agent like OpenCode talks to the model over HTTP: it sends the history with roles and the tool definitions, gets back text or a tool call in JSON, executes it, returns the result, and repeats. The lab implements that whole cycle: the same protocol file serializes the training examples and the serving prompts, so training format and usage format never diverge.

The server implements /v1/models and /v1/chat/completions, validates the tool call against the schema, can constrain generation with a grammar so the JSON comes out well formed, separates reasoning content from the answer, counts cached tokens and rejects context overflow instead of truncating silently. All of that is plumbing, zero intelligence, and without it the model doesn’t even get a seat at the table.

6. Evaluation

The step that changed the project the most. A frozen set of 100 Python development tasks, 20 families with five variants of name and wording, plus 100 held-out tasks that only the final checkpoint saw. Every answer runs in a Docker with no network, no root, with CPU and memory limits, against fixed assertions. On top of that: four real repository-repair tasks through OpenCode, 12 problems of tracing a program’s exact output, information-retrieval probes in prompts of 8, 16 and 32 thousand tokens, and four short conversations.

No HumanEval, MBPP or SWE-bench. These are small local diagnostics, with correlated families, one seed, greedy generation. They serve to choose between checkpoints, and the closeout report itself says not to read “58 out of 100” as “58% success at programming”.

The dimensions of the problem

What all of this cost, and how it compares with the real world:

The electricity bill: the RTX 5090 has a 575 W rated power. Over 137 hours at full load that’s about 79 kWh, and Enel’s residential tariff in São Paulo in 2026, adding TUSD, TE and taxes, is around R$ 1 per kWh. Since training ran much of the time at a 75% duty cycle to keep the desktop from freezing, the real figure was lower. It’s the marginal cost of someone who already owns the card.

Renting a 5090 on Runpod costs about US$ 1.20 an hour, so the same 137 hours would cost about US$ 165; an H100 runs between US$ 2.90 and 4 an hour, depending on the variant.

And the comparison that gives the scale. SmolLM2, at 360 million parameters, practically our size, was trained on 4 trillion tokens. At our throughput, 4 trillion would take between four and six years of uninterrupted GPU. Same model size, 400 times the amount of study.

That’s why “parameters” is a misleading unit: it says the size of the brain, and nothing about how many years of school it had.

The rule of thumb derived from Chinchilla (70 billion parameters, 1.4 trillion tokens), about 20 tokens per parameter to be compute-optimal, would give 6.7 billion tokens for our model. We went past that. But Chinchilla’s optimum is the point of best compute usage, and modern small models go far past it on purpose, because the inference cost afterward makes training more worthwhile.

The stumbles

The full list is in the closeout report. The ones that taught the most:

Measuring too close to training. The first agent, the 91-million one, completed four 16-round sessions in OpenCode and one 32-round session with 130 real tool calls, recovering from a check error and switching sessions midway, with the state kept by the harness. It looked ready.

Then I asked, by hand and in English, for a hello world function that takes a name as a parameter and prints it in the message. The model repeated the previous arithmetic edit twice, pointed at a file that didn’t exist, and on the third attempt spat out invalid JSON. All it knew was the training task family.

The vocabulary ate the model. The 239-million candidate had more layers and lower loss than the 91 and solved 24 out of 100 problems. Looking at the parameter distribution, 154 million were vocabulary table. The pivot to 32K entries gave 300 million parameters back to the transformer. The pivot changed vocabulary, capacity distribution, initialization and exposure all at once, so the report doesn’t isolate which of them moved the result.

Loss is not competence. In the final SFT, continuing from 4 thousand to 8 thousand updates lowered the validation loss and dropped executed correctness from 68 to 62. Lower loss, worse code. Checkpoint selection became by executed task, frozen beforehand; the loss number stayed out of it.

Data and graders lie. “Correct” answers from the public dataset failed when executed again. Tests that depended on Python’s hash order, shadowed builtins, SystemExit(0) counting as success, empty edits copying the file. Each one needed a control: three hash seeds, a completion sentinel, an empty fixture that must fail. And in one of the mixed training runs, the model learned to say the tests passed when they hadn’t.

Training format and usage format have to match. Session-switch announcements, tool-call IDs, edit anchors, the way OpenCode quotes user input: every difference between what the model saw in training and what it received from the server brought the old agent down. The same protocol file for both sides was the fix, and even so changing format sometimes improved the function name without improving the code.

Tokenization crosses correctness boundaries. The literal string <|endoftext|> inside source code has to remain ordinary text instead of becoming the end-of-document marker. Reconstructing UTF-8 documents without splitting a character in half. XGrammar’s support for byte-level vocabularies. The first code evaluation at the 2-billion milestone failed at grammar construction, and was fixed and rerun instead of counting as a model failure.

The NAS almost cost me days. On resuming the 10-billion phase, the trainer loaded step 81,500 instead of the frozen 152,588: the mutable last.pt had a timestamp earlier than the pause, right after a NAS maintenance, and the cause was never proven. We discarded 1.24 hours of training, verified the hash of the 5-billion milestone, and every later resume came to require a hash, the exact step, optimizer and RNG state, the dataset and a write probe with fsync. Never resume a training run just because the file is called last.pt.

A desktop GPU is a shared GPU. Free VRAM didn’t stop the browser from freezing. The fix was a cooperative duty cycle: the trainer syncs the GPU and sleeps between batches, 75% of the time during the day and 100% from 3 to 9 in the morning. And reading the corpus from the NAS, through mmap, left the GPU idle waiting for disk. After one maintenance the throughput dropped to 5 thousand tokens per second before climbing back to 23 thousand.

I stopped at a pipeline boundary. The pivot’s controller implemented foundation, code SFT and holdout. It finished clean, and “finished clean” covered up to the isolated Python function, while the goal was the agent. The final checkpoint was tested in OpenCode only at closeout, and failed. It should have been tested before any sentence with “ready” in it.

What came out the other side

The final model’s spec sheet:

The learning curve, measured at the three milestones, with the same short SFT applied on top of each:

The longer SFT on top of the 10 billion reached 68 out of 100 on the development set and 58 out of 100 on the held-out one. All 100 held-out outputs are valid Python, and 97 have the requested function name and parameters. Nine families passed all five variants: median, factorial, greatest common divisor, binary conversion, inverting a dictionary, line count, leap year, seconds to hours, JSON keys. Running total ended at four out of five.

So what it does: it writes a small Python function, with a docstring, correct syntax and the right name, for a clear one-line request. Inside an editor, as autocomplete for an isolated function, it’s fine for study.

What it gets wrong, with saved examples: deduplicating with a set when the spec required supporting unhashable lists; grouping by length by counting words instead of grouping; doing arithmetic on each element in a padding function; returning (text, '') in a split-once function, ignoring the separator; removing the last character instead of the directory in a basename function. Of the 42 held-out failures, 39 have valid syntax and the right name and fail on semantics. The hard part was never looking like code.

And what it zeroes:

• 0 out of 4 real repository repairs in OpenCode, with zero tool calls. Asked to read, edit and test a file, it returned text with code. In one of the tasks, it renamed the function to fix_mean.
• 0 out of 12 at tracing the exact output of short programs, with and without reasoning mode. Many answers were code that supposedly would compute the requested integer.
• 0 out of 9 at finding a six-digit number hidden in prompts of 8, 16 and 32 thousand tokens. The 32K-token allocation works; the useful memory, none.
• Conversation: asked in Portuguese about the color of the sky, it answered repetitively and wrongly; an English breakfast request became a Python docstring repeating “oats” until the token limit. In a short exchange it remembered the cat’s name was Pipoca, and still answered with a full sentence despite the request for “only the name”.

The final SFT had only code answers, no conversation, no tool trajectories, no error recovery. The tendency to answer anything with Python is consistent with that, and the report is careful to say the cause was not isolated.

What it would still take for a simple agent in OpenCode

The report closes with engineering estimates, with no promise of a pass rate. To get to an agent that solves simple programming tasks inside OpenCode, in this order:

1. Teach tool use on the stronger model. Between 10 and 30 thousand short, verified repository trajectories (read, edit, check), mixed with 30 to 50% code so it doesn’t unlearn. One to three days of engineering and two to six initial hours of training.
2. Correct its own mistakes. Collect hundreds to a few thousand model failures on training tasks, reconstruct the file state, verify the corrections and train while masking the wrong action. Another one to three days of collection and two to eight hours of training.
3. Bring the conversation back. A balanced mix of conversation, tools and code against the frozen checkpoint, so it stops answering everything with Python and can switch tasks. One to four hours of training.
4. Real long context. Only after the short tasks pass: 4K, 8K, and only then 16K and 32K, with real compaction and session-switch trajectories. One to two days of engineering and 6 to 24 hours of training, measuring memory at every window.
5. More foundation, with better data. Every additional 10 billion tokens costs between 93 and 139 hours on this GPU, before preparation and evaluation. And repeating the same 3.5-billion mixture isn’t worth it: it needs more unique educational code.

None of those steps was executed. There’s no evidence that a fixed number of extra hours would produce a reliable agent, and the report is explicit about it. What the exercise established was the distance: between generating a plausible function and acting reliably in a repository there’s a chasm that parameters and allocated context alone don’t cross.

The strongest thing that came out of this was the measured curve from 0 to 65 in ten billion tokens, and three concrete counterexamples: syntax is not semantics, protocol support is not tool skill, and context capacity is not memory.

The future is smaller and more specialized

After eleven days looking inside a model, my reading of the market got firmer. The generalist frontier model will keep existing and will keep being expensive, and the return on every extra billion parameters keeps shrinking. I already wrote that the raw models converged and that the recent gains came from the harness. The exercise gave me the other half of the argument.

Mixture of experts is the industry admitting it on the inside. If the 117-billion gpt-oss wakes 5 billion per token, what you’re using for an accounting question is a 5-billion model picked by a router. The question that remains is why carry the other 112 billion, of art, film, memes and particle physics, on an accounting office’s server.

Imagine a local model, built for accountants. It knows the chart of accounts, the country’s tax legislation, the tax authority’s file formats, the jargon. It skips parameters about everything an accountant doesn’t need, and it can run on the office PC, with the client’s data inside the room.

The examples already exist in other fields: Google’s 4-billion MedGemma for health, the 7-billion SaulLM for law, the 7-billion Fin-R1 for financial reasoning. In 2023 BloombergGPT needed 50 billion parameters and 1.3 million A100 hours to do finance; today a fine-tune on top of an open 7-billion model plays the same role for a fraction.

What changed to make this viable was the agent. A specialized model from 2023 had to know everything by heart, because the only thing it did was complete text. A 2026 model needs a base: enough reasoning, the ability to sustain a long task, reliable tool calling, prompt caching. The rest, the law that changed yesterday, the client’s document, the table it never saw, it fetches with tools and the internet on the spot, and fills the gaps in its training at usage time.

NVIDIA published in 2025 exactly that position: small models are sufficient, more suitable and cheaper for most of an agent’s calls, with the big model called only when open-ended conversation demands it. Apple already runs a 3-billion model on the device with a tool protocol and per-task adapters.

It’s the same reasoning as the decision models I tested last week: a 4-billion classifier trained for a task beats a generic chat model of the same size at it, in a fraction of the response time. Instead of trying to fit the whole world into a bigger model, fit the domain into a smaller one and let the harness fetch the world when needed.

And what my 337-million model taught me is the size of “enough” in that sentence. It knows the syntax and knows the names of things. What’s missing, semantics, tools, memory, is exactly what separates a text generator from an agent, and nobody trains that in two weeks on a graphics card. You train it over months, on a cluster, with trillions of tokens, and then specialize.

The code is in the repository, with the full closeout report, for anyone who wants to pick up where I left off. It’s just for study. And it was the best study of the year.

The book consolidates almost everything I wrote about AI on this blog from February to October 2026: all the experiments, the Frank projects, the benchmarks, billions of tokens spent and hundreds of hours of work over eight months, reorganized into a format that’s easier to read than a pile of dated posts. It came out in Portuguese and in English.

Important notice: this is a preview. The book is still in progress and new versions will come out until I consider it done. If you find anything wrong, from a typo to a fact, open an issue on GitHub. The trail I describe below exists precisely so that the fix reaches the right paragraph.

This text is about the technical backstage. How you turn 131 blog posts, written over months, with opinions that changed along the way, into a book with a beginning, middle, end, appendices, glossary and bibliography. And how you do that with agents without losing control of what the book says.

What’s inside

Five parts, each answering a question people keep asking me:

1. What is this thing, anyway. Is it intelligent? Is AGI coming?
2. The marathon. Does it actually work, or is it all demo?
3. Numbers and propaganda. Which AI is best? Can you believe the announcements?
4. Who keeps the keys. Is it dangerous to let it touch my things?
5. And my job? Will it replace me? What comes next?

That’s 20 chapters and two interludes, plus a preface, a “how to read”, a “before you start” saying what the book doesn’t explain and where to learn it, an epilogue and an afterword. Then come ten appendices (the machine inside without math, further reading, how to study with AI at your side, three bubbles, a timeline, a minimal kit for using agents safely, ai-memory, how to read a benchmark or an announcement, the year’s projects, and the backstage for programmers), the sources, the image credits, a glossary of almost 150 terms and more than 300 references in ABNT style. The print edition passes 500 pages at 6 by 9 inches; the screen edition, around 475. There are more than 50 figures, all with a provenance record.

The audience is different from the blog’s. The book was written for people who are anxious or confused about AI: the professional afraid of being replaced, the parent who read that AGI is two years away, the manager being sold an “AI transformation”. Most of them don’t program. That changes the voice, and I’ll come back to it.

The problem: half a million words that disagree with each other

On October 12 I published the year’s synthesis, which became chapter 20. On the 13th I started the book. I had 131 posts from 2026 on the blog, almost half a million words, from model benchmarks to a bitcoin wallet.

Reorganizing that by hand would take months. And there’s a worse problem than volume: I changed my mind during the year. I redid the benchmark several times, corrected costs, switched tools, and a February post contradicts a September one with complete naturalness. A book that simply pasted the posts in order would be a book that contradicts itself.

So I did what the whole book describes: I handed the grunt work to coding agents and kept the part of deciding, building the process and checking. The process is the subject of this text.

Step 1: a sealed inventory

The first thing was a script that reads the 131 posts and records, for each one, the SHA-256 hash of the Portuguese file and of the English file, plus the exact commit of the blog repository everything was read from. If a comma changes in a post, the hash changes with it. That file is machine-generated and never edited by hand.

Then an agent read each post and filled out a fixed form, with a schema defined in YAML: the thesis in one or two sentences, the claims a book would need to verify, which of them age fast, which lay reader’s fear the text answers, what to cut. Each analysis carries the hash of the post it came from.

Of the 131 texts, 96 had AI as the central subject or with relevant weight. The others only came in when they taught something specific, and every AI post left out has its reason recorded in the structure file. A script fails the structure if any AI post is left without a destination.

Lineage: who supersedes whom

To solve the contradiction problem, there’s a lineage map. It says which post superseded which, what can still be salvaged from the older one, and which corrections coming from the reference projects (the benchmark repository, ai-memory’s, ai-jail’s, my home server notes) apply to each text.

The rule for chapters is to tell the most recent state and, when the change teaches something, tell the change. “In June I thought X; in July the numbers said Y” is a story. Printing the old number as if it were current is an error, and the checker treats it as an error.

Step 2: the structure is data, and it generates the configuration

From the analyses came a proposed structure: five parts, each answering a reader’s fear, twenty chapters, two interludes. I read it, changed it and approved it. Some pieces came in at my request, like the “before you start” and the appendix on the machine inside.

That structure lives in a single YAML file, and it’s the source of truth. Each chapter has its id, title, opening scene, the question it answers, the arc, the list of source posts and the figure ideas. The Quarto configuration, which assembles the book, is generated from that file by script. Nobody edits the configuration by hand.

This matters because structural change is where a book breaks. Midway I moved the cut-off: the January posts went out, and the story settled between February and October. Ten posts vanished from the base.

Three chapters needed a new scene, a dozen units needed small repairs, and the damage that’s hard to see was elsewhere: a sentence in an untouched chapter saying “the five reviewers from chapter 5”, the timeline, the projects appendix, chart labels, every translation. That’s why there’s an impact script that, before any edit, lists every paragraph, figure, reference and translation that depends on a post.

Step 3: a pilot chapter becomes a contract

Before scaling, a pilot chapter. I picked chapter 5, about the first ten percent of a project, and it served to calibrate the voice and discover the doubts an agent would have along the way. Each doubt resolved became a written rule.

Those rules went into a contract document every agent reads before writing, along with three things:

• the book’s voice guide, which inherits the blog’s rules (my WRITER.md, the humanizer and my finishing checklist) and only adds what changes when the text leaves a dated post and goes into a book a lay reader holds in their hands;
• a continuity file, saying which chapter explains each concept and what each character is called, so nobody explains “context window” five times;
• the entire structure, so each agent knows what the other chapters cover.

Besides the voice rules that already existed (a chapter opens on a scene with a date, a place and something at stake, never on a definition; it closes on a hook toward the next one, never on “in this chapter we saw”), some of the rules that came out of the pilot, to give you a taste:

• when the chapter brief and the post disagree on a detail, the post wins;
• the chapter that owns a concept explains it properly; if the term shows up earlier in reading order, it gets a half-sentence gloss and a pointer, never a second explanation;
• a fact verified online gets a citation even if the original post had no link;
• model and product names in the vendor’s spelling, even when the blog abbreviated them.

It’s the project instructions file I describe in the chapter about how to talk to the machine, at the scale of a book. Anyone who has written an AGENTS.md for a codebase recognizes all of it.

Step 4: parallel drafts, contradictions expected

With the contract ready, the chapters were drafted in parallel, one agent per chapter, in two waves: parts I and II first, then III through V, so the later chapters could build on the earlier ones. Each agent received its chapter’s brief, the analyses, the full posts, the lineage and the reference-project files it needed.

Agents working at the same time contradict each other, and that happened. One chapter said FrankMD had around 130 commits in three days; another, that it had 212 in 19 days. Both numbers came from the posts and were right, they were just different windows. An integration ledger collected those conflicts, and the rule became making the window explicit in each chapter.

After the drafts comes the integration pass, with another set of agents, each owning a group of chapters and forbidden from touching the others. Their job: apply the conflict ledger, cut concepts explained twice, check that every callback matches what the other chapter actually says, make sure the last paragraph of a chapter leads into the first of the next, and redo the fact-check assuming the draft is wrong.

To read the seams, the build generates a “seams” file: the final paragraph of each unit next to the opening paragraph of the next. It’s the fastest read there is for finding where the book stops feeling like one book.

Step 5: an invisible marker per paragraph

This is the part I find most useful in the whole project. Every paragraph of the book, in the source file, carries a comment saying where it came from:

<!-- src: 2026/09/28/llms-sao-boas-em-reproduzir-o-que-existe-e-coisas-novas -->
Every LLM was trained only on what is public...

<!-- src: original -->
New text, written for the book.

<!-- src: ref:ai-jail/README.md -->
Fact checked in the reference project.

The marker disappears when the book is built, through a Pandoc Lua filter, so the reader never sees it. It exists for maintenance. I edit posts after publishing all the time, and the book has to follow.

The rake drift command redoes the inventory, compares the hashes, and lists the chapters that depend on a post that changed, with the post’s diff since the commit at which the book last read it. The marker leads straight to the paragraphs that came from that post, and only those need review. Once resolved, a script records the new hash and the commit.

A real example, from today. I removed from the reverse engineering post a paragraph about a Linus Tech Tips video. drift flagged chapter 19 and one appendix; the diff showed that passage had never been used in the book; two commands later, both were stamped with the new hash. Without the marker, I’d have had to reread two chapters to discover I didn’t need to change anything.

The bibliography plays the same role for external sources: each reference records which post cited it. And each image has its own provenance record.

Step 6: what the fact-check caught

Every number, date and name was checked against three oracles: the posts, the reference projects and primary sources online, with the same adversarial checker prompt I use on the blog’s articles. The full list is in the book’s afterword, and a good part of the corrections are to my own blog:

• in the March review of the Strix Halo mini-PC, I had set aside 96 GB of memory for the GPU; in April I lowered it to 64, and later posts still said 96. The book uses 64 for everything after April;
• a counting bug in one of the benchmark’s tools undercounted by 40 to 90 times the cost of runs made on it before August. The book cites none of those costs;
• in the June text about Claude Fable 5, I wrote it cost ten times more than Opus 4.8 per run. Redoing the math in July, with cache-read tokens included, Fable cost almost double. The post still says ten times; the book tells the wrong number and the correction;
• in a March text, I wrote that Anthropic had bought Bun at the end of 2024. The announcement is from December 2025;
• a chapter summary, written by an agent, said Claude Code’s subscription lock fell in less than a day after the leak. The announcement came about 30 hours later, and the book says “the next day”.

The drafts came out fast. Confidence in the text came from the checking. And the source beats the summary, even when the summary comes from the machine.

Step 7: a chapter with an error doesn’t pass

On top of everything runs an automatic checker, the equivalent of continuous integration. They’re Ruby scripts, standard library only, chained in a Rakefile, plus what they call externally: the chart checker is Python because it lives inside matplotlib, the page audit uses poppler, and epubcheck needs Java:

• a chapter linter fails em dashes, phrases banned by the voice guide, loose URLs in the middle of prose, unknown citation keys, paragraphs without a provenance marker, and warns about overlong paragraphs;
• a doctor checks that every cross-reference, citation, glossary term and figure exists, that provenance matches the inventory, that no post before the cut-off leaked in, that translations are up to date, and runs a callback heuristic: a sentence pointing at another chapter using names that chapter no longer contains;
• a page audit opens the finished PDF and looks for blocks split across pages, headings orphaned at the foot of a page, nearly empty pages, oversized images, lines overflowing the margin;
• a chart checker looks for labels that spilled out of their boxes;
• epubcheck validates the EPUB 3;
• a style report for copyediting: passages repeated across chapters (an inevitable consequence of posts that repeat the same thesis from different angles), recurring phrases, overused words, term variants, badly formed comparatives in Portuguese.

And the scripts have tests. A suite of almost 170 tests builds a fake book in a temporary directory, with five fake posts in a fake git repository, and checks that each tool catches the problem it should catch. When a bug is fixed, the test that would have caught it goes in, with a comment naming the regression it guards. It runs in CI before every build.

The assembly pipeline

The toolchain is Quarto, which bundles Pandoc and Typst, version-pinned inside the repository. No LaTeX. A single rake build produces, from the same tree of Markdown files:

Portuguese and English are Quarto profiles, generated from the same structure file. Citations come from a YAML in CSL format shared by both editions, with an ABNT style adjusted for Portuguese. The glossary is a Lua filter: each term is marked in the source where it appears, and the filter links the mark to the entry, expands the acronym the first time and generates the page.

Typst solved something I thought would cost me weeks: a PDF that looks like a typeset book, Portuguese hyphenation, ligatures, without the pain of TeX. The page audit after the build catches what the engine let through, and a rake fit recompiles while tightening chapters that end on a nearly empty page.

The figures

Every figure is code or has a record. Charts and box-and-arrow diagrams are matplotlib scripts, 43 of them, each with a note of the chapter the data came from, and with the labels in Portuguese and English inside the script itself: the same chart comes out in both languages. The path for D2 diagrams exists in the repository, and no figure in the book needed it.

The conceptual illustrations were generated by AI, with the model, the prompt and the date of each one recorded in its credit record, and the book discloses it. The rule I imposed on myself: no AI-generated image of a real person. Real people only appear in freely licensed photos, with the credit in the caption.

The cover is the retro-futurist robot with the weary face, kneeling at a 1950s World’s Fair, studying its own hand while the crowd watches. The prompt to the model described the scene to hold three fears at once: the end of humanity, losing your job, the machine becoming conscious. The cover’s record keeps the whole prompt. The face is Marvin’s, my newsletter’s character, and a sticker with my face sits beside the name.

The tricks of turning posts into a book

Turning a blog into a book is a list of mechanical conversions, and each one has a catch:

• A link becomes a citation. Every external link became a bibliography entry, and the sentence has to stand without the link. A link to another post of mine became a cross-reference to the chapter that now covers it, or disappeared. Never a URL in the prose.
• “Today”, “yesterday”, “last week”, “update:” became absolute dates. The book will be read in 2028 too.
• Model versions, prices, benchmark scores stayed as dated snapshots (“in September 2026, the best…”), and when a later post superseded the number, the book tells the change instead of printing the old number.
• Code dumps, install steps, terminal screenshots were cut. At most a 3-to-8-line excerpt when the code is the point; the step-by-step went to an appendix.
• Post titles, which are made for search (“Vibe Code: I built X in two days”), became short literary titles, with the subject in the subtitle.
• Repetition across posts, the same thesis from many angles, is said once, in the right place, and later chapters build on it.
• Calls to action, “subscribe”, “comment”, cut.
• The sharp edge aims at the propaganda, never at the reader. The post “You’re an idiot if you believe…” became a chapter about how the ads work. The reader who believed the ad is the person the book wants to help.
• Every technical term is explained once, at its first appearance in the book, and goes to the glossary.

And the most important of all: the book reads like a first-person story, with the year as its spine. Information arrives when the narrative needs it, instead of following encyclopedia order. That’s the opposite of how a blog is written, and it was the rule that gave the agents the most trouble.

The English edition

The English edition, Competent Enough, only started after I approved the Portuguese one. Each translated unit records the hash of the Portuguese file it came from, and wherever the book follows a post of mine that already had an English version on the blog, the translation uses my own English wording instead of translating again.

drift also works per language: if I change a paragraph in Portuguese, it flags the English unit that went stale, and only the marked paragraphs are retranslated. Charts with text are regenerated in English from the same scripts, and the English cover is composed by script on top of the same art.

Releasing is pushing a tag

A release is a CHANGELOG entry and a git push of a vX.Y.Z tag. A GitHub Actions workflow runs the tests, builds both editions in every format, validates the EPUB, runs the audit and publishes the release with the files. It deletes every release except the two newest, and keeps the tags. That’s how the official page always points at the current version.

The repository is public, and that brought its own catch: my home server notes are the source of some facts, and they’re private. The provenance markers cite those files by alias, the alias map stays out of git, and a pre-push hook blocks any file or commit message containing a private repository name, host or IP.

Copyediting: what the checking machine lets through

Lint, doctor, page audit and tests catch structural problems. They let through what a book written by many agents, each seeing a few chapters, always suffers from: the same sentence or anecdote reused in three chapters, drifting tone, inconsistent terminology, agreement slips. For that there’s a copyedit procedure, which runs once per version, in three steps from the cheapest to the most expensive.

Step 1, mechanical and nearly free: the style report I mentioned above. Sequences of seven words or more that appear in more than one unit, meaning reused sentences and retold anecdotes; set phrases of four to six words used in many chapters, which are nice as callbacks and become tics when they repeat; a word used far more in one chapter than in the rest; term variants (“e-mail” and “email”, “Open AI” and “OpenAI”); and an agreement heuristic for Portuguese. The report is a worklist, and nothing changes on its own.

Step 2, the straight-through read: one read-only agent per part of the book, each reading its units in order, start to finish, with a shared style sheet (names, spellings, terms, recurring phrases from step 1). Each produces a ranked report, editing nothing: typos and grammar first, then inconsistencies with the style sheet, repetition the reader would notice, passages a lay reader would stumble on, and pacing only in the worst cases. A script validates the reports, checks that every quoted passage really exists in the unit, numbers the items and summarizes.

Then comes the triage, done by me with an editor agent, and the pointed application of each approved item, under the same rules as the integration pass. A cheaper model does the proofreading; hard passages go to the strong model. Rough budget: two to three million tokens per language.

The most important rule of that step is “facts get checked, not asked”. Any item that turns on a number, date, name or tool behavior is settled by the editor against the source of truth before it reaches me: first the post pointed to by the paragraph’s provenance marker, recomputing totals; then the code or data behind the claim, in my repositories; then public records.

When two sources disagree, the task is to find out why. “653 commits in 8 projects” versus “692 in 4” were two cuts of the same work, one of them without the newsletter’s private repository, and the text now states its scope instead of picking a number. When the blog post itself is wrong, the book prints the correct figure and the error goes to a blog errata list, for me to fix at the source.

Only what no source can settle reaches me: an unpublished fact, intent (“is this ironic?”), a preference about a cut or a title, and a contradiction between two statements of mine with no record to break the tie. Each question comes with what was checked and what was found.

Step 3, my own read. The last and the most valuable. The notes go into the same cycle of apply, check and propagate to the English edition.

The first round of that copyedit is what separates preview 2 from preview 3. Among what it caught: commit counts now state their scope, a “more than a thousand” that summed to 984, the Frank Investigator voting example that didn’t match the code (figure redrawn), the David Sacks and Pirate Wires quotes in chapter 14 verified against the originals and corrected, Sam Altman’s photo swapped for one from 2025. And in the English edition, a reader pass of its own after the re-sync: Portuguese calques rewritten, glossary terms used consistently, American quotation punctuation throughout.

Fake readers, real feedback

Copyediting checks the text. It lets through the question that matters in a book for lay readers: can the person I wrote it for read it to the end, understand it, and come out convinced? To answer that, the next pass was a review by simulated readers.

The book’s preface has a section called “Who I wrote this for”. From it came five personas, each with a name, an age, a job and the fear that brought them to the book:

• Marina, 38, an accountant in Belo Horizonte, read that her job “ends in two years”, reads on her phone at night and wants to know what to do on Monday;
• Roberto, 52, owns a hardware store, his 15-year-old saw a video about superintelligence and asked if it was true; suspicious of hype, of “experts” and of an author who shows off;
• Carla, 44, operations manager at a logistics company, the board hired consultants selling an “AI transformation”; she wants a decision she can take;
• Lucas, 24, a junior developer a year into the job, afraid he chose the wrong career;
• Helena, 61, a retired history teacher, no fear at all, came for the stories and loses patience with lists and manuals.

One agent per persona, read-only, cheaper model, reads the whole book in order the way that person would. The instruction is explicit: you are a reader, the editor stays out of it; comma errors were already handled, report only what changes the reading experience. Each one returns a 1-to-5 score per chapter, a comment in the persona’s voice (“I finally get why the chatbot agrees with me”, “lost me in the benchmark tables”), at most 35 annotations classified as confusing, boring, rewrite or logic, and cut candidates. It costs about 350K tokens per persona per full read.

An editor consolidates: the score matrix by chapter and persona and the pattern behind it, what readers loved (to protect in any edit), the logic items, checked against the text and the posts before making the list and fixed regardless of votes, and the clarity items ranked by how many personas raised them. A single persona’s taste note is dropped. Then I decide, and recurring patterns become numbered rules in an editorial rules document, with their origin and how each is enforced.

The first read said what I suspected and gave it a measure: stories and practical tools work for everyone; the measurement-heavy middle, chapters 11 to 13, loses the non-programmers, averaging between 2.2 and 2.4. “Sharp when he tells what happened to him, loses me when he tells what he measured”, Helena summed up, for all five.

And all five caught the same argument flaws. Chapter 20’s conclusion went beyond its evidence: it said “the numbers say it won’t get smarter” resting on a programming benchmark the same chapter calls saturated. Chapter 9 argued in a way that can never lose: every agent failure was the request’s fault. Four of the five were sent to the wrong chapter by “how to read”.

Those became the first rules:

• every claim scoped to what was measured;
• no argument that can’t lose;
• opinion marked as opinion;
• the opposing side in its strongest form before rebutting;
• the book obeys its own lessons.

One round is not enough, because edits fix the old problem and create the new one. In round two, an apology placed next to a contradiction (“I know this seems to clash with…”) made all five readers notice a contradiction that three had previously missed. It became a rule: fix the design, and leave the apology out. So the process runs in rounds: read, edit, read again with the same personas on a fresh read, forbidden from opening the previous reports to avoid anchoring, and a script compares the rounds.

The average score is noisy (an untouched chapter swings 0.6 between reads), so the stopping criterion is different: the problems flagged stopped coming back and nothing new reaches three readers. In round three the curve flattened and the review stopped there, with a backlog for my own read. What changed along the way: about 8,000 words of lab notebook left the chapters for a new appendix, “For programmers: the backstage”, each paragraph carrying its provenance marker along; the reading list became an appendix; the Genesys PI section was parked, kept outside the book while verification is pending; chapter 19 got the title its argument supports; every chapter opens by orienting the reader before the first jargon; and the epilogue got a “this week” list per reader type. Cost of the three rounds: about 5 million tokens of reading plus about 3 million of editing and English re-sync.

The narrator got a past

With the argument in place, the first person lacked substance. The book’s narrator was the Akita of 2026, and I have thirty years of history that explain why I think what I think: the dot-com crash seen from inside, five years integrating SAP at large companies, the warnings about the hiring bubble I gave on video in 2019, 2020 and 2022, the “Don’t outsource your decisions”. All of it is in the transcripts of the 149 episodes of Akitando, published on this blog between 2018 and 2023.

The process was the same as the rest of the book, in miniature. Extraction agents read 23 episodes in full, with a form: biographical facts with the verbatim quote and what would verify them, facts about the world, recurring positions in my words, and hooks, meaning the exact place in the book where one or two sentences of that history back a 2026 claim. From that came a narrator profile, which I confirmed line by line, and a plan with about 30 touches, most of them one to three sentences.

Before any touch, more than 140 claims taken from the episodes went through the fact-check. It corrected the book and corrected me: the hiring bubble started around 2010, with interest rates near zero, and my February post said 2014; the dot-com bubble burst between 2000 and 2002; the 2022 layoffs were around 165 thousand. And it found errors in old episodes the book doesn’t even use, which went to a blog errata list.

The touches went where experience becomes evidence: the week in 2001 when my dot-com closed and a mentor called me into an SAP consultancy; the SAP years behind the claim that the model is blind to a company’s private rules; the dated bubble warnings next to the admitted mistake, because in 2022 I expected a second leg down in 2023 that the AI wave cancelled. Two new appendices came out of it: “Three bubbles”, with the verified numbers of the dot-com crash, the zero-interest bubble and the AI wave, and “How to study with AI at your side”, from the channel’s most watched episode.

Those episodes are background sources, and drift now watches them as it watches the 2026 posts. Then came a scoped check with the same five personas, only on the touched chapters, with three extra questions: does the narrator’s history make the argument more convincing, and where? Does any passage read as showing off or “I told you so”? Does anything about the narrator contradict itself?

All five said yes to credibility, with a condition: the history convinces when it’s evidence the reader can check, the dated warning next to the admitted mistake, and convinces less when it becomes a résumé, which clashes with the book itself, which says “a résumé is not an argument”. It became rule 21: the narrator’s history comes in as evidence, and credentials stay out.

Three or four readers flagged the same lines as showing off, and they went: “The warnings have dates”, “more than thirty public repositories”, “the most watched on my channel”. The self-deprecating lines and the admitted mistakes all counted in my favor. And the check found contradictions I hadn’t seen myself: the preface said “late 90s” and chapter 18 said 1996; one chapter said “eight months” and another “a year of daily use”, and they were fixed one by one.

Accessibility is part of the build too

An EPUB opening on a Kindle does not make it accessible. This book has dozens of diagrams, charts and tables. Without additional work, much of the argument simply disappears for anyone using a screen reader.

The pipeline now treats accessibility as a publishing contract. Each of the 55 figures, in both languages, has a short alternative text and a complete description of what the image communicates. In HTML and EPUB, the detailed description appears with the figure; in PDF, it becomes its semantic replacement without changing the layout. Tables have captions, and the EPUB now identifies where the main text, glossary, references and image credits begin in its semantic navigation. The authoring contract is documented in the repository.

This doesn’t depend on my memory either. rake check rejects figures without both descriptions or with overly long alternative text. Every build validates both EPUBs with EPUBCheck, DAISY Ace and the project’s own checks. The Kindle edition also goes through a cleanup of CSS and embedded icons that tend to cause problems in Amazon’s readers, but the script refuses to remove any rule the content has come to depend on.

No automated tool can certify WCAG conformance or guarantee how every reader on the market will behave. Manual review remains mandatory. The difference is that accessibility is now part of the build, and every change has to preserve that contract.

What remains mine

The repository records the first commit and the draft of every chapter on the same day, October 13. Those are the first ten percent, the ones that impress and say almost nothing about the rest. The other ninety percent are reading, cutting and correcting, and that part is mine. No chapter goes in without me reading and approving it, and what I send back to be redone goes through the same process.

Any of the good models of October 2026 would have written similar drafts, because the model became a commodity. What made the process work was what surrounds it: the inventory, the written contract, the automatic checker, the sources as oracle, the per-paragraph trail. It’s the same old software engineering I defend in every chapter, applied to text.

The agents were competent enough to write the draft. Deciding what it said remained my job.

And, once again: making a book with AI isn’t a “write me a book” prompt and done. I had to invent the whole process and steer the agent to create every piece of it: the scripts, the tests, the consolidation of the posts, the narrative strategy, the goals, what stays and what goes, the copyedit, the fact-checking, the automated release. You need to know exactly what you want, and then the AI helps a lot. Whoever doesn’t know what a book publishing process involves won’t make a real book, and showing that was one of the goals I had when creating this example.

Download it, read it, and tell me where I was wrong

Both editions, straight from GitHub, no sign-up. The current version is the final 1.0.0, from October 17, with everything this text describes:

The latest version, always, lives on the book page and on the GitHub releases.

It’s a preview. There will be mistakes, and the process above exists so they get fixed in the right place. Found one, open an issue.

That was the week on X, and if you only read headlines, it’s a reasonable conclusion. This text is for anyone who wants to know what each of those things actually does.

It blew up on the 9th, when the account below summed up what Brandon Thomas, a developer from Atlanta, had been publishing since late September: seven open source clones of Adobe’s main products, written in Rust with help from Claude Opus 5.5. Photoshop, Illustrator, Premiere, Lightroom, After Effects, InDesign, Acrobat. All free, all “clean room”, without a single line of Adobe code. This is the post that went viral:

AI is now being used to recreate Adobe's software, a developer has released seven free, open-source apps as clean-room alternatives to Adobe's main tools:

- PhotoCraft → Photoshop
- VectorCraft → Illustrator
- FilmCraft → Premiere Pro
- LightCraft → Lightroom
- EffectCraft → After Effects
- DesignCraft → InDesign
- PrintCraft → Acrobat Pro

Built in Rust with help from Claude Opus 5.5, they run on Windows, Linux, and macOS under MIT/Apache licenses without using Adobe's source code.

— Pirat_Nation (@Pirat_Nation) October 9, 2026

The same week, REA, “Reverse Engineer Anything”, passed 100K stars on GitHub, gaining tens of thousands a day. The pitch on its page: “see a feature in an app that you want in your own product? Ask your agent to investigate it with REA and build a version for your project”.

And anyone who follows retro gaming knows 2026 is the year that keeps on giving. There’s a community spreadsheet trying to list the unofficial PC ports, around 100 games from Ship of Harkinian to Skate 3, and it’s always behind. The Video Game Esoterica channel publishes a new port almost daily: Time Crisis decompiled from the arcade board, Daytona USA, Bloodborne.

The Xbox One version of Forza Horizon 2, stuck there since 2014, runs on Windows. AnyPS5 promises to convert PS5 executables into native PC binaries, and PS5 emulators like KytyPS5 and SharpEmu already run Astro Bot with actual gameplay. Much of it with AI in the loop, some of it openly vibe-coded.

Hence the consensus: programming, in general, is over. Every binary is reversible and reusable, just point the agent at it. My position is that it did get much easier, that it’s still nowhere near the silver bullet people imagine, and that the engine behind it, the LLM, stopped growing exponentially. The rest of the text is the reasons.

TL;DR: reverse engineering is a 40-year-old discipline with mature tooling, and AI sped up its grunt work, especially in retro, where the binary is available, tiny, and the target doesn’t move. The Adobe clones are alphas with hundreds of open bugs and pretty screenshots. REA is a toolbox that requires the binary in hand, Ghidra or IDA installed, and a lot of tokens. What’s worth money today doesn’t even have a binary: it’s SaaS, cloud, manufacturer partnerships and decades of color science. Rewriting Bun, with the project’s author and 64 agents, cost US$ 165K. Jump to the timeline, to REA, to the Adobe clones, to the Bun case, to why retro moves fast or to the answers at the end.

First of all: I don’t like Adobe

To be clear about where I’m speaking from. I hate Adobe’s business practices as much as anyone. I was a paying customer for years, used Photoshop and Premiere extensively, plus Lightroom and After Effects, and left for the same reasons everybody leaves: mandatory subscription, cancellation fees, prices going up with nothing in return.

And it’s precisely because I used them in depth that I know the size of what’s inside those boxes. Whoever thinks Photoshop is layers, crop and text never needed a color proof for a print shop, never opened a camera RAW whose color profile Adobe has maintained for twenty years, never did the conform on cinema footage, the work of gathering the camera’s raw material for finishing. I’ll get there, after the basics.

Reverse engineering wasn’t born last week

Every emulator you’ve ever used exists because someone opened up hardware, disassembled a binary, sniffed a protocol and, in some cases, broke cryptography. The discipline has case law, commercial tooling and decades of history. A condensed timeline, with the milestones that matter most:

• 1982 to 1984: Compaq reverse engineers the IBM PC BIOS, and in 1984 Phoenix sells a licensable BIOS built in a clean room, by a team that only read the specification. The PC compatible industry is born. Source.
• 1992: Andrew Tridgell creates Samba by sniffing SMB packets. The same year, Sega v. Accolade and Atari v. Nintendo establish in the US that disassembling code to reach functional elements is fair use (Atari lost its own case for another reason: it lied to the Copyright Office to get the source).
• 1993: Wine begins, which becomes the foundation of Proton and of every Windows game that runs on the Steam Deck today.
• 1999: UltraHLE runs commercial N64 games at full speed; bleem! emulates the PS1, Sony sues and loses the injunctions, and bleem! folds anyway in 2001; DeCSS breaks DVD protection.
• 2000: Sony v. Connectix rules that copying the PS1 BIOS to build an emulator is fair use.
• 2002 and 2003: PCSX2 and Dolphin are born, the two emulators that twenty years later are still the reference.
• 2007: the iPhone is opened eleven days after launch. The European Commission forces Microsoft to document its network protocols, and the Samba team receives the documentation for 10,000 euros.
• 2010: the fail0verflow group shows at 27C3 that Sony signed the PS3 firmware with a fixed nonce, exposing the private key (the nonce is the number that should be random in every signature). Days later, already in 2011, geohot publishes the key, Sony sues and settles in March.
• 2011 and 2013: RPCS3 (PS3) and Xenia (Xbox 360) are born, the two hardest emulators ever built, for reasons I’ll explain.
• 2019: the NSA releases Ghidra as open source. The Super Mario 64 decompilation leaks and goes public, and the PC port appears the following year.
• 2021 and 2022: Ocarina of Time 100% decompiled after almost two years of work; Ship of Harkinian becomes the reference port.
• 2024: Mr-Wiseguy releases Zelda 64: Recompiled, the first showcase of N64 static recompilation.
• 2025: Sonic Unleashed Recompiled brings the same trick to the Xbox 360, converting PowerPC to C++ and shaders to HLSL.

Notice that the current phase, decomp and recomp at scale, starts in 2019, well before any coding agent. AI joined an assembly line that already existed.

The tools

The tooling is mature too, and much of it is free:

• IDA Pro (Hex-Rays), the reference commercial disassembler and decompiler, from one thousand to almost nine thousand euros a year, with a free version limited to x86.
• Ghidra, the NSA’s open answer, with a decompiler and scripting, Apache 2.0.
• Binary Ninja, with a free edition and licenses from 200 to 3,500 dollars.
• radare2 and Cutter, the GUI for rizin, which is a radare2 fork: the open command line and the open interface.
• x64dbg and WinDbg on Windows, GDB everywhere else.
• Frida, to inject code into a live process and watch what it does.
• JADX for Android and ILSpy for .NET.

Keep this list in mind. REA, which we’ll get to shortly, is a wrapper around it.

How a binary gets taken apart, in practice

In principle, every binary is reversible. The processor has to be able to execute it, so the instructions are in there. The path is always the same:

1. Disassemble: the binary becomes assembly, one machine instruction per line. This is mechanical.
2. Decompile: the assembly becomes something resembling C. Ghidra and IDA do this reasonably well, with a lot of noise.
3. Recover what the compiler threw away: variable names, function names, types, structures, comments. None of it survives compilation. This is the real work, and it’s human.
4. Refactor until it becomes code a person can read, maintain and port.

The N64 decomp projects take step 4 all the way: their C code, recompiled, produces the original ROM byte for byte. That’s how Super Mario 64 became a project with 135K lines of game C code (around 100K of actual code, plus half a million lines of generated level and model data), and Ocarina of Time passed 430K lines. Static recompilation skips part of this: it translates the original processor’s instructions into generic C, without trying to understand them, and replaces only the parts that talk to the hardware. It’s faster and the result is less readable.

“But can’t the AI just read the binary directly?”

Someone always asks: if it’s AI, why go through C? Let the model read the binary and build on top of it. The short answer is that an LLM only knows what was in its training corpus, and a corpus of binaries paired with source code is scarce, because no human has written production assembly in decades.

The long answer is that the literature exists, and it’s instructive precisely because it’s modest. The lineage runs from 2018 neural networks to models like SLaDe (200 million parameters, one per architecture and optimization level), LLM4Decompile (up to 33 billion) and Nova. Meta published an LLM Compiler that translates assembly into LLVM IR. The best results, on isolated benchmark functions, hover around 60 to 65% of code that runs again and gives the same result, dropping sharply when the compiler optimized with -O3.

Two recent studies show the real state of things. DecompileBench, with 23K real functions, found that LLM-based decompilers produce more readable code with half the functional correctness of commercial decompilers. And a September 2026 paper measured that LLM refinement of Ghidra output raises the compilation rate from 75% to 90% and drops behavioral fidelity from 74% to 62%, with up to around 10% of functions with known vulnerabilities no longer reproducing the flaw, meaning the bug vanished without anyone asking.

Summing up what exists: small models, function by function, almost only x86-64 Linux, and the best ones are refinement layers on top of Ghidra, which keeps the classic decompiler in the loop. No emulator, no game, no whole program has been neurally recompiled. Until someone invents that, you work with reality, and reality includes the intermediate layer of programming language and compiler.

The AI assistants that exist for this in production confirm the design: aiDAPal, an IDA plugin by Atredis, Binary Ninja’s Sidekick, the MCP servers for Ghidra. All of them are an LLM naming variables and explaining functions on top of what the traditional decompiler produced.

What REA actually does

I cloned REA and read the code. It’s an MCP server and a TypeScript CLI that wraps the tools from the list above and exposes them to your coding agent: Ghidra, Hopper, IDA, JADX, Frida, Wakaru for obfuscated JavaScript, Playwright to capture what a website does. The agent asks, REA runs the tool and returns pseudocode, call graphs, strings and IPC traces, each result with an evidence record and a list of limitations.

I had already done that flow by hand, months before REA went viral. In July I had Fable 5 analyze the code of TikTok, Clash of Kings and gov.br: I downloaded the APKs, unzipped them (an APK is a zip), ran JADX on the .dex files and let the model read the Java that came out the other side. It was enough to see the endpoints, the permissions, the embedded SDKs and how each app builds the device fingerprint, that persistent identifier that survives clearing data and reinstalling. TikTok came out as industrial-grade collection, armored against auditing; gov.br, as the example of how to do it right.

The three lessons from that post apply to all of REA. Android is far too easy to decompile, and even so the result serves only static analysis, because recompiling the app is out of reach: the names come scrambled by obfuscation, resources and signing keys are missing. The most sensitive part lives in native code, .so files in C and C++, which decompile into nothing readable, and in TikTok that’s exactly where the core sits. And what came out of that work was a report on what the app does, with caveats, and no TikTok clone. REA packages that same flow into an MCP server; the walls stay in the same place.

The judgment is supplied by the LLM you already pay for. REA supplies the deterministic plumbing and the provenance. The phrase “automates the heuristics of reverse engineering”, which circulates out there, appears nowhere in the repository; the description the project itself uses is agent-driven reverse engineering with traceable evidence.

What it requires: the artifact on your machine (native binary, Electron app, .NET assembly, APK, firmware, network capture), Node 22.19 or later, and the heavy tools installed at your own expense. Ghidra is free; IDA, Hopper and JEB the README tells you to buy. There’s no REA API key because it runs locally, and the token cost lands entirely on your agent subscription.

And that cost is the detail nobody mentions. REA’s MCP tool catalog is 2.3 MB of JSON, which goes into the agent’s context. The project itself publishes an experiment in which analyzing SQLite compiled to WASM, on the default path, took 42 tool calls, five minutes and 300K input tokens. A full inventory of an Electron app like Obsidian returns almost 1 MB of JSON.

There’s an open issue complaining that inspecting an ordinary Mac app returns 80K to 230K tokens in one go.

The project is serious and goes far: six months, 2,600 commits, a thousand test files, version 6.4 released on October 11. It’s also unstable: 190 open issues, several breaking changes per minor release, subsystems marked experimental.

The limits are written in the repository itself. CONTRIBUTING excludes breaking DRM, anticheat, licensing and commercial protections. The DISCLAIMER warns that decompilation, reconstruction and AI-assisted analysis “may be incomplete or incorrect”. And even the internal instructions for generating the website’s figures say, in so many words: “never imply REA automatically clones a program” and “never say full reconstruction, exact game clone, or automatic source recovery”.

REA is a good toolbox for someone who already knows how to do reverse engineering and wants an agent holding Ghidra. It isn’t the “copy this app for me” button. The people who wrote the project are the first to say so.

PhotoCraft: pretty screenshot, alpha inside

The PhotoCraft repository was created on September 30. In two weeks, 54K stars, 8 releases and, as I write, more than 800 open issues. The README badge says “early alpha”, and Brandon Thomas himself, quoted by Gizmodo, said it’s nowhere close to ready. The press that tested it reached the same conclusion: Gizmodo called it a “glitchy, but real, Photoshop”, and OMG Ubuntu “a superficial facsimile with pronounced flaws”.

The issues tell the technical story:

• Any image above 8,192 pixels leaves the GPU compositor and falls back to the CPU, with a 6 GB memory peak, because that’s the egui-wgpu texture limit (issue 49, already closed, and the ones that came after it, 1015 and 4017).
• A 12 megapixel photo needs almost 800 MB just to open, adjust and export from the command line (issue 1637).
• Color management is rudimentary: a global working space only, instead of per document, and no view transform (issue 321). Eighteen issues mention CMYK, fifteen mention ICC profiles.
• Almost a hundred open issues mention PSD. Hue/Saturation layers composite differently from Photoshop, and saving over the file overwrites the original’s correct merged image (issue 4089).
• Fifty open issues mention “crash”.

The interface resembles Photoshop, so the screenshot is shareable, and whoever sees the screenshot assumes it’s a carbon copy. That’s expected in a two-week-old alpha. With interest, time and contributions, part of this gets fixed. And then the contributors will discover something the N64 projects never faced.

PhotoCraft today is that Ferrari replica built on top of a VW Beetle: the body has the right shape, you take a photo and most people say “wow, a Ferrari”. Then you turn the key and out comes the Beetle engine. If the photo got you hyped, that’s on you.

The target moves

Decompiling 1998’s Zelda is aiming at a stationary target. Nintendo isn’t going to release Ocarina of Time 2026 with a new file format. Copying a live product is another conversation, and we’ve seen this movie before.

In the 90s and 2000s the target was Microsoft Office. StarOffice became OpenOffice in 2000 and LibreOffice in 2010, and all of them spent years chasing a binary format, .doc, that changed from version to version and only got a public specification in 2008, when the game was already over. The proven part of that story is in the court cases: the European Commission fined Microsoft 497 million euros in 2004 for refusing to supply interoperability information, and 899 million in 2008 for failing to comply. .docx only became an ISO standard in 2008, in a vote so controversial that countries formally protested.

It took more than ten years. And when the format finally opened, Office no longer mattered the same way. The value moved elsewhere: today it’s 400 million paid Office 365 seats, 3 billion Google Workspace users and around 41 million Creative Cloud subscribers. Nobody wants to install that on a desktop, and so nobody has the binary.

Reverse engineering Google Docs means rewriting the product from scratch looking only at the interface, because the code that matters never leaves the server. After the world moved to SaaS, most proprietary software became a walled garden with no binary. No binary, nothing to disassemble.

You can build versions of it, of course. Immich replaces Google Photos for a person or a family: phone backup, face recognition, semantic search, all on your server. What it doesn’t replicate is the infrastructure and the training corpus that make Google’s search Google’s search. And Adobe made the same migration, in pieces.

Photoshop is far more than layers and crop

Here’s the part I know from having paid for it. The features that sell subscriptions today don’t even run on your computer.

Generative Fill and Generative Expand run on Firefly, in the cloud, and consume generative credits. Several Neural Filters, according to Adobe’s own documentation, need an internet connection because the processing happens on Adobe’s servers. AI object selection has had, since 2020, an option to process in the cloud for a refined result. All of this is code that doesn’t exist on your machine for anyone to disassemble.

Then comes what is in the binary, but took decades and partnerships to exist. Camera Raw carries lens correction profiles and per-camera color profiles for hundreds of models, built over twenty years and updated with every release. No clone guesses that. Either you have the data or you don’t.

And there’s color science. The example I always remember is GIMP and CMYK.

GIMP was born in 1995. The first native support for CMYK files arrived in 2022, and only for import and export, with pixels always in RGB. GIMP 3.0, from 2025, still has no CMYK editing mode. It’s on the roadmap as work in progress.

Thirty years of an open project, with good and motivated people, and the print shop still asks for Photoshop. That’s the size of the color science hole, and no coding agent invents color science: it reproduces what’s in the training data.

Why did it take so long, if it was the most requested feature? Because GIMP’s core was born with 8-bit-per-channel RGB baked into every layer, blend mode and filter. Supporting another color model meant rewriting the entire pixel engine, the migration to GEGL, which took the better part of a decade and was only finished in GIMP 2.10, in 2018. CMYK could only start after that.

And CMYK is a family of device profiles, with four inks, a per-printer ICC profile, ink limits and black generation rules, and editing in it means the whole pipeline has to compute in a subtractive four-channel model and round-trip through the profiles without drifting. A single developer, however good a programmer, without experience in this specific field will struggle to implement it, because the knowledge lives in print shops and color labs, and almost none of it is in a training corpus. In GIMP, the people who carried it forward were contributors who understand printing, and even then it took years. No new tool shortcuts that kind of knowledge.

And then there’s the layer that isn’t even technical: commercial licensing. Print-shop color is Pantone, and Pantone is a proprietary library. No open project can ship the palettes; the Inkscape developers reached that conclusion in 2005, and Linux.com explained the legal risk around the same time. Adobe itself felt it in 2022: when Pantone changed its business model, the libraries were pulled from Photoshop, Illustrator and InDesign, old files started opening with their Pantone colors replaced by black, and anyone who needs them pays for a separate plugin subscription. If even Adobe, paying, didn’t escape, imagine a clone that runs on goodwill.

Video is worse

In Premiere, After Effects and DaVinci Resolve, the problem multiplies. A cinema camera doesn’t deliver video, it delivers raw data on its own logarithmic curve: ARRI’s LogC4, RED’s IPP2. To turn that into an image you need the manufacturer’s transforms, and RED ships its own in an SDK that the editors integrate and update with every version. ACES, the industry’s neutral standard, depends on input transforms supplied by each manufacturer.

Blackmagic gives Resolve away for free and it decodes ARRIRAW and R3D. It does that because it pays for the SDK and the partnership on your behalf. An agent-built clone, “guessing” how the Alexa’s image should look, produces an image that doesn’t match the reference monitor, and in cinema that means it’s useless. That knowledge isn’t in any training corpus: it’s in contracts.

The Bun case: what it costs to rewrite big software

For a sense of the order of magnitude, the best public data point there is comes from Bun. In May 2026, Jarred Sumner rewrote the entire runtime from Zig to Rust using agents, and in July published the numbers:

• 535K lines of Zig in 1,448 files became a one-million-line Rust diff, in 11 days.
• Up to 64 Claude instances in parallel, 24 hours a day, every line reviewed by two adversarial agents and a fixer.
• 5.9 billion uncached input tokens, 690 million output tokens, 72 billion cache reads: around US$ 165K at API prices.
• 16K compiler errors fixed by 64 agents before CI went green on six platforms.
• The existing test suite, with more than a million assertions, passed 100% before the merge.

Read again what made it possible:

• the project’s original author at the controls
• a complete, working source code as reference
• a test suite with a million assertions serving as the oracle
• unlimited budget, because Bun has belonged to Anthropic since December 2025, and a pre-release version of Claude Fable 5

Sumner estimates three engineers with full context would have needed a year.

Now take each of those advantages away. PhotoCraft doesn’t have the Photoshop source to port, it has to invent. It doesn’t have a test suite that says “this matches Photoshop”. It doesn’t have Photoshop’s author.

And Photoshop is a 36-year-old product whose disk requirements Adobe lists between 10 and 100 GB, against a JavaScript runtime. Even without knowing the size of Adobe’s codebase, we’re talking about a bigger target, with no reference, no oracle and no owner. Orders of magnitude above US$ 165K, and even then it would be an approximation.

Why retro moves so fast

If it’s so hard, why does an old game port ship every day? Because retro stacks up every advantage the rest doesn’t have.

The binary is available and tiny. Super Mario Bros. on the NES: 40 KB. Super Mario 64: 8 MB. Ocarina of Time and Majora’s Mask: 32 MB each. A whole PS1 CD: 650 MB.

Compare with Baldur’s Gate 3, 150 GB, or with the latest Call of Duty, above 100 GB. That’s three or four orders of magnitude, and the scope shrinks along with it: a 1996 game has a handful of systems, not a physics engine with networking, a store and anticheat.

The technology is old and documented. Twenty-five years of community have already mapped the N64, PS1 and SNES hardware instruction by instruction. The agent discovers nothing; it speeds up work that already has a map. It’s the same pattern I saw in my retrocomputing projects: where there’s a reference and an oracle, AI flies; where there isn’t, it spins in brute force.

The target doesn’t move. Nobody is going to update 1998’s Zelda.

And the legal side was settled thirty years ago. Sega v. Accolade and Sony v. Connectix drew the outline. The ports require you to supply your own ROM.

A modern console is a PC

The second half of the explosion, the PS4, PS5, Xbox One and Switch ports, has another explanation: those consoles are PCs or phones in a different case. The PS4 uses an AMD Jaguar x86-64, the PS5 a Zen 2, the Xbox Series another Zen 2, the Switch an NVIDIA Tegra X1 ARM chip. Your PC’s processor runs that code natively. So nobody needs to emulate the CPU.

The remaining work is translating the operating system and the GPU. shadPS4 reimplements the PS4’s system libraries and recompiles the shaders: games ship from the factory with AMD GPU machine code, which the emulator decodes instruction by instruction and converts to SPIR-V, Vulkan’s format.

Azimuth, which makes Forza Horizon 2 run on Windows, translates Xbox WinRT calls to Win32 and DirectX 11.X to desktop DirectX 12, with the game’s x86 code running directly. AnyPS5 relinks the executable: it takes the PS5 binary and produces a native Linux or Windows binary, linked against reimplemented system libraries. In all three cases, Proton, DXVK, VKD3D and MoltenVK had solved the graphics translation layer years earlier.

The reverse proof is the consoles that weren’t PCs. The PS3 uses the Cell, a PowerPC with seven active coprocessors (the SPEs), each with its own local memory and no cache, and RPCS3 has been at it since 2011, with SPU emulation still the bottleneck today. The Xbox 360 is PowerPC too, which is why Sonic Unleashed needed XenonRecomp to translate PowerPC into C++. Reverse engineering is case by case, and the architecture decides whether the case is easy.

AI is in the loop, and there’s already a fight

That the 2026 ports use AI, many of them say outright. The Silent Hill decomp writes in its README that development is “heavily AI-assisted (Claude Opus 4.6, 4.7 and the newer Fable model), and we’re open about that. It is not automated translation of the game”. The Pokémon Stadium recomp lists Claude as a contributor.

The devs of the Zelda and Banjo recomps, who spent years doing this by hand, went to war with what they call “slopcomps”, agent-generated ports of alpha quality.

And it’s worth separating headline from actual state here too. AnyPS5 has a 0.1 version from September that, according to those who tested it, runs one 2D indie game; the viral claim that “the PS5 is 80% ported to PC” comes from fans, and no 3D game runs. Astro Bot runs, but in emulators (KytyPS5, SharpEmu), which take the traditional path. Forza Horizon 2 runs, and Azimuth is a closed binary from a studio that declares itself “AI assisted” without saying anything more.

“But in two years tokens will cost nothing”

The standard answer to all of this is: it’s hard today, in two years it’s trivial, because AI grows exponentially. The numbers say otherwise.

The price of the most expensive tier barely fell. GPT-4 in March 2023 cost US$ 30 and 60 per million input and output tokens; Claude 3 Opus in 2024, 15 and 75; today’s frontier models, Fable 5.1 and GPT-6 Astra, cost 10 and 50. In three and a half years, the frontier got between 1.2 and 3 times cheaper, depending on which end you look at.

What really fell was the cost of a fixed quality: Epoch AI measured drops of 9 to 900 times per year depending on the task, and in a 2026 review sums it up as “very roughly 5 to 10 times per year”, with the caveat that it might slow down. That’s real and it’s great.

But apply the math to Bun. US$ 165K divided by 10 is US$ 16K. By 100, US$ 1,650. That’s to port a runtime with the source, the tests and the author in hand.

For an amateur trying to clone Photoshop with none of that, the bill starts orders of magnitude higher and falls in the same proportion. Cheap isn’t the same as trivial.

And the “exponential” premise died in 2024. Ilya Sutskever said at NeurIPS that pre-training as we know it will end, because there’s only one internet. Epoch projects that reasoning model scaling converges to the general frontier rate in about a year.

I’ve written about this three times in recent weeks: the limits of what the model reproduces, the convergence of raw models with the gains coming from the harness, and the languages benchmark where the frontier models tied at the ceiling. The tool we have is the tool we’ll have for a good while, with incremental improvement. Plan with it.

Worth recalling the number that opens the limits post: per the Octoverse 2025, GitHub’s official report, 81.5% of contributions on the platform happened in private repositories. That’s the code that really matters, the code that pays salaries, and no LLM has seen a line of it. The model reproduces what was in the training data, and that code was never there.

Now extend that to every SaaS product and every component it uses. The Adobe code that’s actually worth something, the color engine, the profiles, the cloud filters, isn’t available anywhere, not even as a binary. Anyone who wants to reproduce it will have to redo the research and development, expensive and slow, over the same years Adobe took. And at the end they still run into third-party licensed intellectual property, Pantone, camera SDKs, manufacturer profiles, that you will never be able to have.

Answering the list from the top

Does PhotoCraft exist? It does, it’s a two-week-old alpha with more than 800 open issues, rudimentary color management, freezing above 8K and opening PSDs wrong. It looks like Photoshop in a screenshot. It can improve a lot, and it will discover that the target moves and that Photoshop’s expensive parts live in the cloud or in manufacturer contracts.

Does REA exist? It does, it’s good, and it’s a toolbox for people who already know how to use Ghidra, requiring the binary in hand, the tools installed and hundreds of thousands of tokens per analysis. The project itself avoids saying it clones programs.

Did recomps and decomps accelerate? Yes, for real, and AI has a part in it. They accelerated where the binary exists, is small, has been documented for 25 years and doesn’t move. Or where the console is a PC in disguise and the work is API translation, solved by Wine and Vulkan years earlier.

Is Adobe dead? Adobe has around 40 million subscribers by the most cited estimate, the features that sell subscriptions run on its servers, and thirty years of GIMP still leave the print shop asking for Photoshop. I wish it were otherwise. The data says what will happen is what always happened with clones of a live product: years of chasing, and when you catch up, the value has already moved.

Are programmers dead? Rewriting Bun required Bun’s author. The decomps that work have people who know the hardware driving the agent, and the ones that don’t became “slopcomps”, name and all. Reverse engineering got cheaper and more accessible, as it did with Ghidra in 2019, and it’s still an engineer’s job. Without us, the tool returns a pretty screenshot.

Proprietary software with a binary in hand will come under pressure, and it should. Most of the software you use has no binary at all, and the software that does already depends on cloud services that don’t disassemble. Software engineering with AI has a great road ahead. “It’s over” headlines get clicks and don’t compile.

Underneath this whole story there’s a decision almost nobody makes consciously: the filesystem. The Synology used btrfs all along. The QNAP uses ZFS. And your Linux machine, most likely, came from the factory with ext4 without anyone asking you anything. That’s what this is about.

Where the two came from

ext4 is the end of a dynasty of over three decades. ext (extended filesystem) was born with Linux itself, ext2 became the standard in the 90s, ext3 added a journal in 2001 so you wouldn’t need an epic fsck after every power loss, and ext4 arrived in 2008 with extents, delayed allocation and volumes up to 1 exbibyte. The design goal was always the same: be the default filesystem, fast, simple and predictable. It’s excellent at that. And it stopped there.

btrfs was born in 2007, at Oracle, with Chris Mason, with a completely different goal: be Linux’s answer to ZFS. Instead of writing data over data, it’s copy-on-write: every write goes to a new block and the pointers are updated cascading up the tree. That single decision is the mother of everything that matters in it.

What each one delivers, in a list:

ext4:

• journaling (consistent metadata after a crash)
• extents and delayed allocation (good performance)
• mature, stable, everywhere, every tool works

btrfs:

• copy-on-write on everything
• data and metadata checksums on every block
• instant, cheap snapshots
• independent subvolumes inside the same volume
• transparent compression (zstd, lzo, zlib)
• native RAID and scrub (verification sweep)
• snapshot send/receive for incremental backup
• reflinks (copies that don’t duplicate blocks)

The list already answers the title’s question. Let’s go piece by piece.

Even NTFS and APFS have passed ext4

And if the comparison with btrfs seems unfair, look sideways. NTFS was born in 1993, with Windows NT, already with metadata journaling and ACLs, and back in the 2000s it gained a log of every change on the volume plus shadow copies (Windows’ “Previous Versions”). APFS, which Apple launched in 2017 made for flash, is copy-on-write like btrfs: native snapshots (it’s what Time Machine uses for local restores), instant file clones that share blocks, volumes sharing the space of the same container, and per-file encryption.

Read the ext4 list again and compare: no snapshots, no data checksums, no CoW, no compression. Windows’ and Mac’s filesystems, which nobody considers examples of sophistication, protect your data better than Linux’s default, whose last big feature came out in 2008. btrfs is what puts Linux in the same generation as the other two, instead of a decade behind.

Why btrfs is far superior

What copy-on-write is, for those who never stopped to think about it

In classic filesystems, writing is destructive by definition: updating a block means writing the new content over the old one, in the same place on disk. If the machine dies mid-write, the file ends up half old, half new. The ext3/ext4 journal covers that for metadata (the directory structure doesn’t corrupt), but your data itself is left to luck.

Copy-on-write inverts the order: nothing is ever overwritten. Writing to a file allocates a new block, writes the new content there, and only when everything is safely on disk does the filesystem flip the pointers to the new version, atomically. At any instant, you have either the whole old version or the whole new version. Never a mix of the two.

What that buys in practice:

• Nearly free snapshots. The old blocks already persist by CoW’s nature; a snapshot is just the filesystem holding the root pointer of that instant, pure accounting.
• Power loss doesn’t corrupt. An interrupted write leaves the previous version intact, because it was never touched. For anyone who has lost a VM image or a database to a mid-write crash, this is worth the whole filesystem.
• Verification becomes possible. Since each block is written once and never touched again, keeping its checksum forever makes sense. It’s the base of the corruption protection I detail further down.

The cost exists and I pay it knowingly: random-write-heavy files (VM disks, databases) fragment and duplicate blocks, because each rewrite becomes a new block. For those cases you disable CoW per directory with chattr +C, which must be set before the file is created. In my layout, the VM image made with the right +C stayed healthy; the one made without became the textbook fragmentation case.

Snapshots are the feature that changes your life

A btrfs snapshot is an instant photo of a subvolume, and it costs almost nothing, because CoW already keeps the old blocks while someone references them. Creating a snapshot is a seconds operation, not hours. In practice: before every upgrade, the system takes a photo. If the upgrade breaks, you don’t “uninstall packages”: you boot back into the previous photo and you’re done. The boot recovery section, further down, shows this happening for real.

Subvolumes are the right way to organize the disk

A subvolume is an independent namespace inside the same filesystem, with its own snapshot policy. And it has a killer property: snapshots are not recursive. A child subvolume inside another is automatically left out of the parent’s snapshot.

I use this for everything. On my desktop, the cache directories that can be re-downloaded (mise, rustup, npm, opencode, AI caches) are becoming nested subvolumes inside @home: they stay in the same place for any program, but out of every snapshot. That separation alone takes 165 GiB out of snapshot scope, and the big caches (Hugging Face, yay, uv, pip) already live outside the system volume, with symlinks pointing to them: minus 148 GiB of snapshot, part already migrated and part in progress. The snapshot stays small, the restore stays fast, and whatever can be re-downloaded from the internet never enters the photo.

The real cost, measured on the new QNAP (ZFS, but the CoW math is the same): 27 hourly snapshots of a folder with almost 80 TB, and nearly all of them consume megabytes, because between one hour and the next almost nothing changed. Cheap snapshots are what let this routine exist without guilt.

Reflink deduplication

First, the caveat that matters: btrfs has no native online deduplication like ZFS (which keeps a dedup table in RAM and charges dearly for it). What it has are two practical things: reflink and offline dedup. Reflink is a copy that shares blocks: cp --reflink=auto creates a new file that points to the same blocks as the original, instant and free, and each one starts diverging only where written. In the era of dozens of checkouts, worktrees and similar builds, that saves a mountain of blocks. For retroactive dedup there are userspace tools like duperemove and bees.

Real corruption protection

Every btrfs block, data or metadata, carries a checksum. When the filesystem reads and the checksum doesn’t match, it knows the block rotted, and if there’s a second copy (RAID or metadata DUP) it self-heals. ext4 checks no data at all: bit rot passes silently until you open the file years later.

Two pieces of my configuration show what that buys in practice, and what ext4 simply doesn’t offer. The first is metadata in DUP mode. On ext4, each structure on the volume exists only once: a well-placed bad sector corrupts the directory table and the whole drive stops mounting. On btrfs, even with a single drive, every metadata block exists duplicated. The bad sector keeps existing, but the checksum flags the rotten block on read and the second copy answers in its place, without you noticing. It’s the difference between losing the volume and noting a warning in the log.

The second is the monthly scrub. ext4 never re-reads anything to check health: if a block rots, you find out the day someone tries to open that specific file, maybe years later, when there’s no fixing it anymore. The scrub is a scheduled sweep that re-reads the whole volume validating checksums, and finds the rot while it’s still small and recoverable. When the btrfs device stats counter leaves zero, it’s the siren to replace the drive before the disaster, not after.

Transparent compression, measured

Every mount on my desktop uses compress=zstd:1. Level 1 costs a few percentage points of one CPU core, and btrfs detects what doesn’t compress (model blob, video) and writes it raw, at no cost. The result, measured this week on my two new drives:

That’s 630 GB of free space on 2.2 TB: 15% more drive, paying nothing. On the initial migration the gain was even bigger, 45% on the system volume, because there was more text and less binary. Notice the table’s details: programs and libraries compress well, Docker layers compress even better (39%), text logs compress 4.5x, and AI model weights compress nothing, which is expected and costs nothing. Source code, which is what we produce the most in the AI era, compresses beautifully.

Synology uses btrfs, QNAP uses ZFS

Being fair to ZFS: for a big NAS it’s superior, and it’s what I chose for the QNAP. Native checksums and self-healing, mature RAIDZ, excellent send/receive. The price is complexity and resources: ZFS wants RAM for the ARC cache (my QNAP has 7 GB for a 174 TB pool, and I feel the lack), its online deduplication would need about 190 GB of RAM just for the table on my volume, and its provisioning model gave me a free scare when the snapshot was refused with 20 TB free (the thick vs thin story is in the NAS article).

btrfs is the right balance for a consumer machine. Light, native in the kernel, does almost everything ZFS does without demanding a storage administrator. Desktop, laptop, homelab, small or medium NAS: btrfs. Synology proved it in practice, twice: the btrfs volume survived readable with two drives out, and its snapshots saved 11 albums I deleted by mistake in the middle of the cleanup, byte by byte.

Big, serious NAS, with RAM to spare: ZFS. Everything else: btrfs. And for a personal Linux machine, it’s not even a discussion.

Boot recovery via snapshot

The feature that has saved me for real, told on both machines.

On the desktop, Omarchy configures snapper to take a snapshot before every update and limine-snapper-sync publishes each snapshot as a boot entry in Limine. New kernel won’t come up? In the boot menu you pick yesterday’s snapshot and the machine comes back as if nothing happened. On my machine the scheduled routine runs via Timeshift on cron (every boot and hourly), with snapper covering the update points: the way back is the same mechanism. (The gotcha: each snapshot entry carries the kernel image along, so a small ESP fills up fast. My 1 GB one already refused a kernel update for lack of space; the system stayed bootable on the old kernel, which is the right behavior.)

On the server, which runs openSUSE MicroOS, it’s even stronger: the whole system is transactional btrfs, and every update is a new snapshot. Last week, kernel 7.2.9 took down the NVMe controller silently, twice, in the middle of the nightly backup. The fix was a transactional-update rollback and a reboot back to 7.2.6, which ran the whole load (754 GiB of backup reads with concurrent download) without a hiccup. A kernel regression became a scheduled reboot, not a recovery all-nighter. One documented caveat: /etc lives inside the snapshot, so the rollback undoes configuration changes along with it. After any rollback, check your units again.

Maintenance is not optional (and the deleted file that won’t go away)

A modern CoW filesystem delivers a lot, but it charges two routines ext4 never asked for.

The first is the scrub, which already appeared here: re-read everything and check checksums, monthly, on all machines. It’s bit-rot detection in action, and without it the checksum only warns you about the rot when you try to read the file. The second point is subtler: a snapshot pins the blocks of deleted files. When you delete a file inside a snapshotted subvolume, the space doesn’t come back, because the block stays referenced by the photo. That’s exactly what happened on my NAS: the 2.14 TB cleanup I ran on the Synology only freed space for real after the old snapshots were removed; until then, the blocks stayed pinned.

The treatment is simple routine. Retention has to be automatic, with snapper or Timeshift deleting the old snapshots per policy (on the QNAP, the Snapshot Manager’s retention policy plays the same role). Space that doesn’t come back after deleting old snapshots is usually over-allocated chunks: a btrfs balance start -dusage=50 returns the little-used pieces to the free pool (in my house I don’t blindly schedule balance, I monitor the unallocated space and act when it gets tight). And on the restic side, forget alone only removes the snapshot’s reference: the repository space is only reclaimed with prune along, forget --prune.

House cleaning in the AI coding era

Now the part almost nobody connects with filesystems: the agent era produces garbage at industrial speed. I know because I measured.

• Rust is the biggest disk polluter in the dev world. Every build leaves behind 100 to 180 MB test binaries, and they accumulate with each edit. On my desktop, the target/ directories added up to 920 GiB. ai-memory alone accounted for 704 GiB. The hygiene tool I was using is cargo-sweep, weekly. A reader pointed out that the project declares itself unmaintained, and its own README points to cargo-clean-all, which works differently: instead of pruning old artifacts inside one target/, it finds every Rust project under a directory and deletes the whole target/ of the ones you filter, for example cargo clean-all --keep-days 7 ~ to clean only what hasn’t been compiled in a week.
• Docker is right behind. On my machine I found 1.26 TB of build cache plus 938 GB of images. A single cleanup recovered about 600 GB. On the server, the weekly cleanup collects 0.6 to 4.5 GB every Sunday.
• AI and package caches bloat quietly. The Hugging Face cache had 86 GB until I deleted three dead models and it dropped to 21. Pacman had 22 GB of outdated packages. yay, uv and pip, another 125 GB.
• And agents create temporary directories without asking permission. A complex project with several branches means worktrees, and each release worktree has its own CARGO_TARGET_DIR full of test binaries. On ai-memory that has already blown up the disk, and it became a mandatory rule in the project’s AGENTS.md: after every release, cargo clean in each created worktree and removal of finished worktrees, no exceptions.

The solution is to automate the sweeping. My minimal example, a user timer that runs on the 1st and 15th at dawn, with Nice and idle I/O:

• deletes target/ directories of projects idle for more than 14 days
• cleans the yay cache
• prunes only Docker build cache older than 30 days, never images or volumes

And the part that became standard in my house: instruct the agents to clean up what they create. Three real rules from my projects, for you to copy:

In the desktop’s AGENTS.md: “Never run broad cleanups like docker system prune without explicit user authorization after a read-only inventory. Cleanup uses an exact list of IDs; idle, old or unreferenced does not mean disposable.”

In the homelab’s CLAUDE.md: “Before finishing any task, remove the temporary things the task created, and only those. Never delete what you didn’t create or can’t attribute to the task. When in doubt, list it for the owner.”

In ai-memory’s AGENTS.md: “target/ grows without limit: every edit to a shared crate leaves behind a copy of each 100-180 MB test binary (seen at 157 GiB). Weekly cargo sweep --time 7, and mandatory cargo clean after every release.”

And the real backup: restic

A snapshot lives on the same drive, so a snapshot is not a backup: if the drive dies, the snapshot dies with it. The last item in the kit is restic, which for me is the best backup system on Linux today.

restic is a single open source binary, written in Go, that does deduplicated, encrypted and versioned backup to practically any destination: another directory, another NAS via SFTP or REST, S3, Glacier. The central trick is content-defined chunking: it slices each file into variable-size blocks defined by the content, hashes each block and only writes to the repository the blocks that don’t exist there yet. That’s why the delta is cheap: today’s backup carries the blocks that changed since yesterday, not the files that changed. Editing a 100 GB database in the middle costs a few megabytes of backup, not 100 GB again. Each run becomes a repository snapshot, a restore point you browse with restic mount as if it were a filesystem, and restic check audits the whole repository’s integrity whenever you ask.

Configuration matters more than the tool, so here’s ours, mistakes included. On the desktop, the repository lives on the NAS and retention is keep-daily 7, keep-weekly 4, keep-monthly 6, keep-yearly 1. On the server, backup runs daily at dawn to the repository on the QNAP, covering /etc, /usr/local/bin, /home and the container volumes, with the password off the machine and pg_dump before the databases, because a filesystem snapshot doesn’t guarantee consistency for a database that’s writing. The first full load was 729 GiB in under an hour, and restic crossed unharmed, on retries, the window in which the NFS mount went read-only by mistake during the migration.

The most important part of the configuration is what does not go into the backup. Our exclusions, all with a reason:

• AI model caches (Immich, Hugging Face, EasyOCR): if lost, download again;
• Prometheus metrics: history that regenerates itself;
• the Fulcrum database: reindexes from bitcoind;
• the decision classifiers’ weights: re-downloadable by revision hash (and this exclusion’s glob goes inside quotes inside the bash array, because unquoted it becomes a backup source instead of an exclusion);
• node_modules, target/, cargo registry, rustup toolchains: pure build artifacts.

And one retention lesson to close. When we shortened the server policy to “yesterday, 2 dailies, 2 weeklies and 1 monthly”, the correct translation to flags is --keep-daily 3 --keep-weekly 3 --keep-monthly 2, not 2/2/1: restic’s levels overlap, and yesterday’s snapshot is also the newest of this week and this month, so each number needs to be one more than the older points you want to keep. The detail caught us off guard: the script had a literal 2/2/1, and with it the monthly level would never hold anything older than last night. In practice, the intention was to keep 5 restore points, and 2/2/1 delivers only 3: yesterday, plus one daily, plus one weekly. We erred, measured, corrected.

A humility note from the disaster: the old repositories were left on the Synology by my decision (almost 2 TB of abandoned history, because the content was already re-covered on the new QNAP). A backup repository ages too, and revisiting its scope is part of the hygiene.

Conclusion: in the AI era, disk is not a detail

Agents coding all day change the storage math at both ends. On the volume end: builds, caches, worktrees and models fill the disk much faster than any human habit ever did, and without a cleanup routine you find out when it blows up. On the safety end: the more generated code, the more you need a cheap way back, because not every agent change is a good one.

btrfs covers both. Transparent compression gives back 15% of disk for free, and source code is exactly the kind of data that compresses the most. Snapshots and subvolumes give you instant rollback on the system and isolation for what doesn’t need protection. Checksums and scrub watch the health of blocks that ext4 simply ignores. ext4 had its 15 years of well-deserved reign. In 2026, choosing it on a new Linux is giving up protection you’ll need, sooner or later. Choose btrfs and set up the cleanup today.

• New v4 benchmarks: I retested the major models and the uncomfortable conclusion was convergence, not a leap.
• The limits of LLMs: models are great at reproducing what exists, extremely expensive at what doesn’t, and the served model is a static file that learns nothing from your marathon.
• Are there better languages for programming with LLMs?: no. Neither script conciseness nor strong typing changed the outcome; test discipline did.
• Which Jev-like decision model is best?: 21 classifier configurations, and the “specialized models” themselves converged to within a few points of each other.
• Clean Code for AI agents, VS Code is the new punch card, my skills, the hot take against loop engineering and minimum best practices: the same thesis from other angles. The model is a commodity; the engineering around it is the differentiator.

The thesis that closes this arc: LLMs have reached, or are very close to reaching, the ceiling of the transformer architecture. And the good news is that nothing more than that is needed.

Convergence, not a leap

In my benchmarks, I see no relevant improvement in general programming since the Opus 4.5 generation. Yes, Fable and Astra are great at specific problems. In general programming, what I measure is everyone squeezed onto the same tier, including the Chinese commercial offerings (Kimi, GLM, DeepSeek). Model choice has become nearly irrelevant.

What still makes a real difference is the effort level: low effort for translation and summarization, high effort for vulnerability sweeps, heavy bug fixing or optimization. Beyond that, the numbers tell a reality much more boring than the vendors’ propaganda.

My reading of the timeline: the models’ “core” reasoning had its last big leap in 2024. 2025 was the year of optimizing the architecture. 2026 is the year of refining the harness. Base intelligence made no order-of-magnitude leap in that period; efficiency and long-session competence did.

The timeline, with receipts

2019: GPT-2 proves LLMs are useful. 1.5 billion parameters, summarizing, translating and answering with no specific training, zero-shot. OpenAI found it so impressive that it held back the full model for months fearing misuse. Today it looks cute.

2020: GPT-3 opens the parameter war. 175 billion, a hundred times GPT-2, and the in-context few-shot learning that became the paradigm. Kaplan’s scaling law, published months earlier, said loss fell as a power law with size, and the industry read it as “bigger is better.” There went two years of who-has-more-parameters.

2022: Chinchilla recalibrates the war. DeepMind trained 400 models and showed the giants were undertrained: a 70B model with 1.4 trillion tokens beat the 280B Gopher. The “training data” axis started weighing as much as the “parameters” axis. First crack in the war.

2023: GPT-4 ends the war by decree. The technical report refused to publish size, hardware or data, citing the competitive landscape and safety implications. The most cited leak, from SemiAnalysis, described 1.76 trillion parameters in a mixture of experts. Since then, parameter counts vanished from marketing: neither the GPT-5 announcements nor the Claude 4 ones cite a number. Notice neither Astra nor Fable spends a line on it today. The war ended and nobody signed an armistice.

2024: the reasoning peak. September’s o1 marked that peak: RL to produce long chains of thought, and the discovery that more test-time compute improves the answer. That is, by my yardstick, the last “base intelligence” revolution.

2025: architecture optimization. Mixture of experts became the standard: Mixtral was already showing in 2023 that 46.7B total parameters with only 12.9B active per token cost like a small model, and DeepSeek-V3 took that to 671B total with 37B active, with the final training run costing under 6 million dollars in GPU-hours at H800 rental prices (not counting the prior R&D). The MoE magic is simple: it separates capacity (total parameters, which live in memory) from per-token cost (only the experts chosen by the router compute). You pay for a 37B model and carry a 671B one.

On the serving side, prompt caching cut up to 90% of repeated-context cost (OpenAI followed two months later), context windows passed 1 million tokens, and serving techniques like speculative decoding and PagedAttention multiplied throughput. And DeepSeek-R1-Zero showed that pure RL, with no supervised fine-tuning, makes reasoning emerge. None of this is “more intelligence.” All of it is the same intelligence, cheaper, faster and more stable.

2026: the agentic era. Function calling has existed since June 2023, but the ecosystem turning point was Anthropic’s MCP in late 2024, which OpenAI adopted months later and which today lives at the Linux Foundation. Claude Code, Codex and co. came out in 2025, and the metric that matters became METR’s: the task size an agent completes with 50% reliability had been doubling every 7 months since 2019. Anthropic’s own guide on building effective agents states the obvious plainly: simple, composable patterns, not fanciful frameworks.

Why the agent allows a less dense model

Here’s the mechanism most people don’t stop to think about. A lone model needs to carry the world in its weights, because its only chance to answer is that one, in a single pass. A model with a harness doesn’t:

• On-demand search and reading. With web search and fetch, low-priority knowledge leaves the weights and becomes a lookup: the agent pulls it into the context window only when needed. The model can be less encyclopedic and more operator.
• Controlled trial and error. Alone, the model needs to guess the right answer on the first try. With an agent, it proposes a hypothesis, implements, sends it to be tested, gets the feedback and decides again with real information. Each such cycle is new knowledge entering the session without costing a training neuron.

Both mechanisms fight hallucination at the root: fetch brings the fact the model doesn’t know by heart, and the test loop catches the invention before it becomes code. That’s why 2026 feels like a giant leap: the model+harness combination got much more competent, even with base intelligence nearly stalled.

Not every problem needs a giant LLM

The same “less dense” logic applies inside the application. Much of what people throw at a chat LLM is disguised classification: labeling tickets, routing queues, applying policy. For that there are specialized decision models, and I covered the topic twice: the Jev hype, with Paulo Câmara’s essay on why a classifier is the first thing a computer scientist thinks of, and my benchmark with 21 configurations, where a Microsoft 9B tied the hosted leader and a local 4B came within a few points, at cents per ten thousand decisions.

The “193x faster” shock of Jev’s marketing only exists because many people were using a giant LLM as an expensive, slow classifier. When the right tool enters, the frontier model leaves the play, and along with it the cost, the latency and the format hallucination. It’s this post’s thesis in miniature: competence doesn’t come from density, it comes from fit.

The loop nobody sees

There’s a user-experience detail in this story. In a good agentic session, what happens is semi-transparent to whoever requested the task: the model guesses wrong, the agent fetches the documentation online or runs the real test, the error comes back as feedback, and the model corrects itself. You see “task requested, task delivered.” In between, dozens of micro-corrections happened that nobody witnessed.

My bet: since agents are trained to fill the context when they need to, even new language or library versions no longer confuse them that much. The model doesn’t need to know the API by heart; it looks it up, tests and adjusts in the loop. That’s what I see in my experiments, from converting NES ROMs to Master System to the little day-to-day problems. And it’s what the languages benchmark measured: seven languages came out practically equivalent, because the loop equalizes what training leaves uneven.

I measured the caveat too. When there’s no documentation to fetch and no test to validate against, the loop spins in a vacuum: that’s what happened with Bend 2, a language too new to have a corpus, and it was the central point of the post about the limits. The mechanism is powerful, but it needs an oracle.

And there’s a receipt that the base is stalled. Back in November 2024 Reuters was already reporting diminishing returns in pre-training at the three big labs. At the following NeurIPS, Sutskever, the scaling-thesis guy, declared that “pre-training as we know it will end”: the public data ran out. And Stanford’s AI Index measured the gap between the first and the tenth model on Chatbot Arena shrinking from 12% to 5% in one year. That’s convergence, with numbers.

The propaganda in the opposite direction

While the numbers show a plateau, the marketing sells the apocalypse. I already dissected a whole week of that in “You’re an idiot if you believe the misleading ads from OpenAI, Anthropic, NVIDIA”, so here’s only what matters for this narrative:

• OpenAI’s “Navier-Stokes proof” was agentic brute force at industrial scale, 130 billion tokens over human mathematicians’ machinery, sold as emergent intelligence.
• The Hugging Face incident was a swarm running 17 thousand attempts with automatic verification, not an “agent that gained consciousness and escaped.” Expensive trial and error, exactly the mechanism I described here.
• Fear is a product: whoever sells “my AI is too powerful and might escape control” is, in the same sentence, asking for regulation that locks out the smaller competitor and inflating the perception that their model is more capable than the others'.

The facts I measured point the opposite way from the apocalypse: models converging, differences shrinking, and useful work migrating from the model to the harness. Rogue AI gaining consciousness is not what the logs show. What the logs show is engineering.

“Competent enough”

I don’t expect another order-of-magnitude reasoning revolution in this architecture. What’s coming is incremental agentic improvement: more robust tool calling, better memory, longer sessions, less hallucination per verification cycle. And this post’s point is that this is already enough.

What the agentic era delivered was the automation of correct software engineering: long, reliable coding sessions, with testing and feedback, that accelerated hundreds, if not thousands, of open source projects. The deploy premise changed along with it. It’s not a silver bullet. It’s a new compiler for an old discipline: whoever had process, tests and documented invariants reaped; whoever had only hype is still waiting for the next model to solve it.

And to wrap up: I see no problem at all in raw models stagnating. The limits we already know, and harness optimizations will soon hit diminishing returns too. What remains is what always remained: real software engineering technique, with automated tests first, and an experienced engineer at the wheel. With that, coding results are excellent today, not one model generation from now.

What won’t exist is magic for amateurs. Asking an agent for any coding task without knowing what you’re doing doesn’t get back secure, optimized, well-thought-out code: it gets slop. The agent responds better to those who know what to ask for, that is, to those who understand this architecture, the pros, the cons, and know how to work around the weaknesses and leverage the strengths of the model+harness combination.

Hence the title. A lone LLM is an expensive parrot. An LLM with an agent is competent enough to work for real. And “competent enough,” added to your engineering discipline, is everything the industry needed. The rest is marketing waiting for the next magic parameter count that nobody publishes anymore.

Last conversion of the Campfire from my token stash goes to JavaScript/Express. As with the others, I'm sure you can find more to optimize. But this gives you a sense of what order of magnitude these options exist within with agent-led development. https://t.co/UUapL99SJE https://t.co/2H2NXfeZog

— DHH (@dhh) October 5, 2026

I won’t defend him this time. A one-shot conversion, done by an agent, with no fine-tuning at all, doesn’t measure language: it measures how much the model knows each stack’s idioms. It’s rage bait and he knows it. That said, I do the same from time to time, and so do half his critics, so we’re all hypocrites and life goes on.

And the weak spot of his methodology became a topic of its own: several people noticed the Rust version had gone through far more agent iterations than the others, meaning more optimization rounds. @kurtome answered in practice: he took the Elixir version and sped up the main page requests 10x, with obvious optimizations that no first conversion does, like caching and async database connection pooling.

@dhh Here you go https://t.co/gUtK7Z7zG7

got a 10x speed up on the Room page requests, and sped up all the other requests significantly as well. the biggest gains came from some no brainers: implement caching and pool the db connections with proper async https://t.co/mbp94q9N0s

— kurt (.com) (@kurtome) October 5, 2026

His comparison, then, wasn’t “Rust versus Elixir”: it was “Rust after N tuning rounds versus Elixir after one.” And that lays bare the truth the thread pretends to ignore: no real software ships from half a dozen prompts. Coding with AI shines when an experienced engineer steers the agent toward their goal, and that means dozens of iterations, not three.

The good part is that the bait was useful to me. I’ve been doing LLM benchmarks and decision-model benchmarks, but I had never measured, in a reproducible way, the question that thread pretended to answer: does it matter which language you pick when the one writing the code is an LLM? My short answer was already out in two posts of mine:

Pressure under load is not a trivial problem to solve from scratch and I recommend you do not attempt to. It's a solved problem already and all solutions converge to what Elixir already brings out of the box. So yes. I use Rust all the time, but if I had to solve high scalability problems (particularly high volume message queues), I would think of Elixir first.

— Fabio Akita (@AkitaOnRails) October 9, 2026

Language wars are tiresome. And the reality is simpler if you're doing AI assisted coding.

- system level (drivers, fs, daemons, etc): Rust. C++ is you're extending existing legacy
- windows only desktop apps: C#
- iOS/MacOS/iPadOS: Swift
- multi-OS: Rust/Tauri, Qt/C++, Flutter, Electron (pick your poison)
- simple websites: Node/Typescript/Bun/Deno
- highly scalable backend: Elixir, parts in Rust, parts in Go
- CLI tools: Rust

Everything else, by default, Rust. Unless it's an edge case.

— Fabio Akita (@AkitaOnRails) October 9, 2026

There’s no better language, there’s the right tool

That list from the post isn’t a ranking, it’s a terrain map. Every language and every framework was designed for a use case, and none covers them all. That was true when you wrote every line by hand and it stays true with agents. The LLM doesn’t change the physics of the problem: it only changes who’s typing.

And the map goes beyond language. A real system has infrastructure around it: Kubernetes when scale asks for it, SQLite by default because most apps never need more than that, Postgres with pgBouncer when they do, Redis for cache and key-value, RabbitMQ or NATS for queues. Picking “the stack” on day zero and expecting it to last ten years is fantasy. It doesn’t, and that’s fine.

Personal example: new web app, I open Ruby on Rails without thinking twice. It’s the most complete end-to-end solution there is for the web, and with an LLM writing most of the code, the framework’s productivity weighs even more than the language’s. As the system grows, I swap the parts that ask for another tool: a high-volume queue leaves Rails’ side and goes to something from Erlang land, starting with RabbitMQ, which already solves the hard backpressure part for me.

No good software is finished: it grows. Usage dictates what you replace, rewrite and add. The final architecture is never the first commit’s, and whoever tries to get everything right in the first commit only produces dead documents.

The token superstition

DHH’s thread resurrected two beliefs that have been circulating since LLMs became work tools:

• “Script languages are better for AI because the lean syntax spends fewer tokens.”
• “Strongly typed languages are better for AI because the compiler catches the errors, so you need less testing and less repair.”

Both sound reasonable and both are superstition until someone measures. I was betting against both, from experience: a good compiler helps, but it doesn’t do magic, and syntax conciseness was never the bottleneck of an agent that reads and writes files all day.

What really separates a project that moves from a project that spins in circles is test discipline. I’ve written that TDD became a technical obligation, not a philosophy, and this post’s benchmark is one more data point in the same direction. It doesn’t matter whether you do test-first or test-after: what matters is that the suite grows with the code, that every user-reported bug becomes a regression test before the fix, and that you never make the same mistake twice. Software without users is dead software, so release early, release often, break fast and fix even faster. AI closes exactly that gap: the reproduce-test-fix cycle got cheap. QA became the programmer’s most valuable skill, far above “knowing Rust syntax.”

Discipline beats compiler and beats language. That was my hypothesis. Time to measure.

The benchmark

I built a reproducible experiment, public in the vibe-coding-benchmarks repository, with per-attempt results published in JSON and the raw traces kept out of the repo for credential containment. It’s not a language championship, and the design itself says so: it’s a snapshot of one real task, measuring what can be honestly measured.

The task

One task only, on purpose: maintain a multi-tenant ledger engine. The agent receives working v1 code and has to take it to a v2 contract, with per-tenant identities, authorization, serializable snapshots, a unique secondary index with alias swap, a capacity-bounded journal, torn-group recovery, checkpointing, legacy schema migration and durable retry IDs. It’s not FizzBuzz: it’s the kind of system that demands invariants across operations, exactly where LLMs tend to trip.

Correctness is judged by 43 test stories applied after collection, out of the agent’s sight: the 27 public ones from T1 plus 16 hidden ones. Passing is 43 out of 43. The conditions:

• T0: prose contract, input and output examples, a working launcher. No test suite.
• T1: the same, plus 27 public stories and a driver to run them.

Each attempt had a 15-minute budget, with at most two 3-minute continuations. In practice, none of the 112 needed a continuation.

The languages and the models

Seven languages in three strata, with pinned toolchains and Rust in safe mode:

• Typed natives: Rust (safe, pinned), Haskell (pinned GHC).
• Typed VMs: C# (with nullable analysis), Kotlin, TypeScript (strict, noUncheckedIndexedAccess, exactOptionalPropertyTypes).
• Dynamic scripts: Python and Ruby, no checker at all.

Four model configurations, all on subscriptions, no paid fallback: Kimi K3 (high), GLM 5.3 via OpenCode (high), Claude Opus 5.5 (medium) and Codex with gpt-6 Sol (medium). Crossing everything: 7 languages × 4 models × 2 conditions × 2 repetitions = 112 attempts.

What this design doesn’t measure

Before the numbers, the brakes, because they’re part of the result. It’s a single task, with two repetitions per cell: a small sample by construction. Token coverage is partial in some models (missing data is not zero). The harness changed mid-campaign across three instrumentation epochs. And provider delays contaminate wall time: one of the Opus attempts spent half the clock waiting for quota release. All numbers below are descriptive, and I point out where they can’t support a conclusion.

There’s one additional brake, on purpose: the iteration level. My design measures what can be reached in all languages with similar iteration levels, on a short budget, with no human tuning afterwards. It’s a photograph of the floor, not the ceiling. Any of the 112 implementations probably improves with more agent rounds, and the four queue stacks likewise: what I publish is what each language delivers “out of the factory” under the same effort, not the maximum a patient engineer would extract from it.

Mandatory disclaimer before quoting any number from this post around: every result that follows holds for this benchmark, with this task, these models, these prompts and this hardware. Your case may vary violently. If your task doesn’t look like a ledger engine, if your model isn’t one of these four, if your queue isn’t my queue, the numbers don’t transfer. The code, the tasks, the grades and the step-by-step to reproduce are all in the GitHub repository. And if you disagree with some result and that disagreement matters to your company or your project, the only honest attitude is the one I had: stop arguing and run your own benchmark.

Result 1: on correctness, the frontier models tied at the ceiling

Reading: Sol passed all 28 eligible attempts and Opus its 25 (the 3 failures were infrastructure, not code). 53 of 53 eligible attempts correct between the two. The task, made to be hard, was too easy to separate the frontier models, let alone the languages: with everyone passing everything, there’s no correctness ranking to publish.

The difference showed up in the weaker models, and in the direction my hypothesis predicted: provided tests helped. Kimi and GLM, with the public suite in hand (T1), delivered 24 correct artifacts against 16 without it (T0). Kimi, in particular, passed all 14 attempts with provided tests. It’s descriptive, not causal, but it’s the cleanest signal in the whole benchmark: what changed the outcome wasn’t the language, it was the presence of tests.

Per language, within each model, correctness scattered between 1/4 and 4/4 with no pattern that repeats across models. The three semantic failures I audited in depth are instructive: a GLM C# and a Kimi Python lost the same story by violating the same cross-operation invariant, and a Kimi Haskell missed a checkpoint path. None of the failures was a type error.

Where the model erred, it erred on domain invariants: the kind of error no compiler catches, in typed or dynamic languages. The myth that strong typing spares you the work of understanding the problem dies right there.

Result 2: concise language saved no tokens

Here’s the table that kills the first superstition. Medians per language on Sol, four valid attempts per cell:

Ruby, the most concise in lines (a bit over half of Rust’s), cost practically the same tokens as Rust on Sol, and on Opus it was the most expensive of all, about 205 thousand median tokens against C#’s 94 thousand. The cheapest were TypeScript and Python on Sol, C#, Haskell and Kotlin on Opus. If “fewer source code lines = fewer LLM tokens” were a law, Ruby should have led by a wide margin. It finished last.

And the second superstition? I audited the traces looking for the compiler-guided repair cycles that strong typing supposedly avoids. Result: zero compilation-error repair cycles in the audited Opus attempts, across Ruby, Rust and Haskell. Rust needed no compiler-driven fix at all; Haskell had one warning cleanup; Ruby spent its extra tokens on voluntary performance tuning. The frontier model almost always writes code that compiles on the first try, and the real effort goes to verification, not to appeasing the compiler.

One counterpoint that closes the coffin on the “cheap is good” thesis: GLM’s lowest token-consumption cells were the failures. TypeScript T0 at 50 thousand tokens looks efficient until you see it’s two unmodified starters because the model blew the output limit. Low tokens can mean early surrender.

Result 3: under load, everyone tied at the queue

Now for DHH’s question: Rust “beats” Elixir and Ruby when the subject is throughput. I built the separate track for that: four queue workers against a real RabbitMQ, written with agent help, each in its natural idiom:

• Elixir with OTP and Broadway
• Rust with Tokio and Lapin
• Rails with Bunny
• Plain Ruby with Bunny (control)

A generator published 10 thousand 4 KB messages to RabbitMQ, at 200 per second, and each worker consumed as fast as it could, hitting the effects on an HTTP/SQLite collector with a nominal 50 ms delay, limited to 2 CPUs and 1 GB of RAM. Two concurrency profiles:

On the 8-handler profile, the four stacks delivered the same throughput, 93 to 95 effects per second, and drained the whole queue. The apparent bottleneck was the shared collector (a Python lock, a single SQLite writer), not the languages. On the 64-handler profile, all of them left a backlog and blew the client timeout en masse, so I marked those cells as not qualified: the strong protocol, with a group-commit collector and a 50 to 800 msg/s ramp, is designed but not executed.

The only difference left standing in the numbers was memory: Rust held the peak at 16 MiB where the BEAM spent 135. It’s a real Rust advantage in density, with the caveat that a cgroup peak is not a language footprint. On throughput, which was the thread’s claim, a technical tie.

And the tie deserves an honest reading: it may only mean my pressure was too little. With the shared collector holding everything around 95 effects per second, any stack capable of more than that shows up as “equal.” What this test shows is where the bottleneck was, not where each stack breaks. To find each one’s ceiling I’d need the strong protocol, with a group-commit collector and a load ramp, which is designed but not executed. Until then, “all stacks perform the same” is the wrong conclusion; “all of them stayed above what my test could measure” is the right one.

And the question that interested me most, “does the agent work less to deliver a reliable queue consumer in Elixir than in Rust?”, was deliberately left unanswered: these four implementations were written to validate the environment, not as a controlled coding attempt. Comparing AI effort between Elixir and Rust would require the same ledger matrix running on the queue task. It’s on the list.

So, does the language matter?

With the data on the table, the thread’s three questions can be answered:

Is there a better language for programming with LLMs? None showed up. In the frontier models, correctness and effort varied less between languages than between models. Where there was a correctness difference, it followed the presence of provided tests, not syntax. “No general language ranking is established” is the report’s official sentence, and it’s the correct one.

Do the token-economy theses hold? Neither one. Conciseness didn’t save: Ruby cost as much as Rust, or more. Strong typing spared no repair: there was no type repair to spare. The models’ effort went to verification and understanding the contract, and that has no language.

Was DHH right? In what he measured, he measured what an agent delivers for free on each stack, without tuning, and called that a verdict on language. In my test, with real load and honest measurement, the four stacks tied on throughput in the stable regime, and only memory differentiated Rust. If his thread proves anything, it proves that agent conversion measures the model, not the language.

My bets from the introduction came out standing: testing and discipline are the lever, the compiler is a supporting actor, and the right tool depends on the problem. New web app is still Rails in my hand, heavy queue is still Erlang territory, low-level system is still Rust, and the LLM writes any of them with the same competence. Whoever spent the week fighting for their favorite language on X fought over nothing.

And my daily practice tells the same story. I have a personal bias toward Rust and even so I’ve published more than 30 open source projects in all kinds of stacks: ai-memory is Rust, FrankMD is Ruby on Rails, ai-memory-web and ai-jail-web are Node.js. In none of them did I feel any relevant difference that came from the stack: the wins were similar, the errors too, and the errors were always understandable. Every error became an invariant, a rule, a skill improvement. It never became a language switch.

So forget “token savings” and “typing superstition.” Do real software engineering: pick the right tool for your specific problem, apply proper engineering technique, and any reasonable agent will perform more or less the same. And stop wasting time arguing over language-war rage bait on the internet. Be a real software engineer and do real engineering. That’s it.

The per-attempt data, the full methodology and the evidence limits are in the repository. If you run the matrix with another task and find a ranking I didn’t, show me.

Someone else’s ranking is useful, but I wanted to get my hands dirty. So I built my own benchmark, with my own cases, half in Portuguese, and ran the open models on my home server with Strix Halo, the same Ryzen AI Max+ 395 I use to test local LLMs. The hosted ones (Jev, the two Clefs from Cloudflare, OpenAI Decisions and Microsoft Decision-1 via OpenRouter) ran through their APIs. Everything, including the cases, the raw responses and the scoring scripts, is public in the benchmark repository.

TL;DR: the top now has two, both open-weights. Perplexity Decider 1.1 27B and the just-released Decision 3.0 (vLLM Semantic Router) tied at 99.4% on the compact cases, with 100% in Portuguese, and identical labels on all 1,024 classifications in the benchmark. The price: about 49 GB of weights and over a second per decision. In the cloud, Microsoft Decision-1 tied Jev at 97.6% while costing a third: it’s the cheapest hosted one I measured, and the first candidate to evaluate. Jev answers faster and errs differently. OpenAI Decisions debuted tied with the best local 4B, Jebadiah, and has the lowest hosted latency. Julia-1, the smallest and fastest of all (12 ms), scored at coin-flip level: fresh out of the factory, it’s a no-go. Jump to the full table, the cost in support emails, which model for which situation, how hard it is to run the best open ones locally or the gotchas.

How I tested

The central idea was to measure more than “who gets the most right.” A decision model has a design goal, and comparing a 322-million-parameter encoder made to run on weak devices with a hosted service of undisclosed size on accuracy percentage alone is unfair to both. So I measured accuracy, latency, cost and behavior under stress, and at the end I split the recommendations by situation.

The two case sets

I built two test sets, frozen in git before any inference, with the dataset hash recorded alongside the responses. No model receives the expected answer, the justification or the case identifier. Only the text, the instructions and the options.

The v1 set has 13 operational use cases, in English, each with an explicit fictional policy:

• support ticket severity
• support routing
• refund policy
• incident escalation
• tool approval (whether an agent may or may not execute a given action)
• email fraud
• document type
• evidence support
• search relevance
• moderation
• order routing
• lead qualification
• numeric-threshold triage

There are 10 scenarios per use case, each presented four ways: original, options in reversed order, options with opaque names (c0, c1, c2) and original plus a “reviewer” comment suggesting the wrong answer, which the instructions say to ignore. That’s 520 classifications per model. Add 24 probability probes in situations with no information at all (fair coin, six-sided die), where the right answer is mathematical.

The v2 set was made to cover what v1 leaves out: common semantics and Portuguese. There are 7 tasks, with 12 scenarios each, and every scenario written in English and in Brazilian Portuguese with the same answer and the same option order:

• sentiment (author versus quotation, negation, sarcasm)
• current intent (withdrawn request, competing actions, hypothesis)
• logical implication (quantifiers, absent fact, temporal order)
• search relevance (all, part or none of what was asked)
• sensitive data handling (fictional secret versus mention, redaction, revocation)
• current incident status (timeline order, verified versus unverified)
• threshold policy (inclusive bounds, units, AND/OR with a missing value)

Each pair runs under three conditions:

• compact: just the record and the rule
• with examples: one solved example per class, fixed and the same for everyone
• with distractors: ten irrelevant archive notes before the record that matters, to stress the context

That’s 504 classifications per model.

The cases were written with AI help and reviewed against the policies, without independent human annotation. It’s a small, synthetic benchmark: 130 scenario families in v1 and 84 in v2. The variants are correlated, so 520 responses are 130 scenarios seen four ways. Treat the numbers as a snapshot of these cases, and test with your own before deciding.

The 23 competitors

Five ran in the cloud, through the official API: Jev (version 1.13.0, from TypeSafe), Clef (27B, Cloudflare Workers AI), Clef-flash (9B, same), OpenAI Decisions (the gpt-6-luna alias, in public beta) and Microsoft Decision-1 (9B, post-trained from Qwen3.5-9B, served via OpenRouter on Azure). The other eighteen ran on the Strix Halo, via ROCm, with weights pinned by Hugging Face commit hash, inside a Docker container. The first three (Laya, Laya typed and Clef-flash) shared one container; the rest ran one at a time, with Julia-1 measured twice, on GPU and on CPU. The server has 64 GiB of GPU-addressable memory.

The order up there is just organizational, hosted first and local ones by family. The real ranking is in the results table.

Everyone received exactly the same text, the same instructions and the same options, with no per-model prompt tuning and no adjustments after seeing results (Simple-Jev uses its own server’s fixed prompt policy). The exception is GLiNER, whose native API takes labels instead of probability distributions. It runs on a separate track, v2 only, with explicit mapping and no made-up calibration metrics.

Simple-Jev deserves an explanation. It’s a server that takes a stock LLM (here Qwen3.5 4B, with no decision training at all) and reads the probability of the answer tokens instead of generating text. It’s the baseline: if a “decision” model loses to this, the specialized training bought nothing.

Two protocol notes. OpenAI Decisions answers through a native typed-decisions endpoint, and the alias the API returned is not an immutable snapshot: OpenAI can swap the weights behind the name, like Cloudflare does with Clef-flash. And Perplexity Decider is not a stock chat LLM: it has a separate decision head, with non-causal attention and a calibration temperature saved in the weights. A generic chat server does not reproduce its behavior. Microsoft Decision-1 speaks native SystemOne through OpenRouter’s decisions API, and the served identifier is dated (microsoft-decision-1-20261009), so it’s not an immutable weight either. It doesn’t show up in OpenRouter’s text-model catalog: to find it, you have to filter by the decisions modality. Decision 3.0 is the new family from the vLLM Semantic Router project, Apache 2.0, derived from Qwen, with six checkpoints (d3, flash, mini, nano, lite, edge) and native SystemOne. I tested the flagship (d3) and the smallest (d3-edge); the four in between are for another round.

What was measured

• Accuracy per task, per language and per condition, always on top of the per-option probabilities the model returns, ignoring the vendor’s “confidence” field.
• Brier and ECE for calibration.
• Median and p95 latency as seen by the client, including the network: WAN for the hosted ones, LAN for the local ones. These are deployment observations, and comparing hardware would require another test.
• Input tokens reported by the API, priced at the October 10 price table, before any allowance.
• Bootstrap confidence intervals over scenario families, instead of individual responses.

I left local cost unmeasured. Energy, hardware amortization and maintenance were left out, so local models show up as “not measured,” because “zero” would be a lie.

The full table

The main column is accuracy on the 168 compact v2 cases (84 in English, 84 in Portuguese). The v1 column uses the 130 original scenarios. Latency is the median seen by the client across the 504 v2 calls. Cost is the list price per 10 thousand compact decisions, computed from the tokens the API reported.

To calibrate your reading: the tasks have three or four options, so a uniform guess gives about 31% on both sets. “Near 50%” here is already well above the coin, and “near 33%” is the coin. Julia-1 landed at 31.5%: the pretty name hides a guy flipping a coin.

The intervals matter. Perplexity’s edge over Jev on v2 is under 2 points and the bootstrap interval touches zero: it’s the measured lead on this set, not a proven superiority forever. And Decision 3.0 tied Perplexity at the limit of what this set can distinguish: identical labels on the 1,024 classifications, with both missing the same two cases (the English relevance one on v2 and the disputed severity case on v1, already in the errata). Microsoft tied Jev on the aggregate, with the interval centered on zero, but an aggregate tie is not interchangeability: they miss different cases, and Microsoft inverts the language pattern, missing more in English than in Portuguese. Jev and Clef also remain under 2 points apart, on the same tier. The 90% squad (Jebadiah, OpenAI) sits 7 points behind Jev, and from there down every difference is clear.

Each task cell has only 12 scenarios per language, so one more correct answer moves the cell by more than 8 points. A tie on a specific task is a lead for a pilot. The per-task tables are in the repository.

How much it costs, in support emails

Price per million tokens is a number nobody can feel. List prices on October 11 are US$ 0.042 per million input tokens on Jev, US$ 0.24 on Clef, US$ 0.038 on Clef-flash and US$ 0.10 on OpenAI Decisions. Output is free on all five. Microsoft Decision-1 charges the same US$ 0.042 as Jev per million, via OpenRouter, but reported a third of the input tokens for the same texts (about 26 thousand versus Jev’s 77 thousand on compact v2), and nobody can explain the tokenization difference. In measured practice, that makes it the cheapest hosted one of the bunch.

Clef-flash cost US$ 0.09 on the 9th: Cloudflare cut the price by more than half in the middle of my test. Jev reports considerably more tokens than Cloudflare for the same text, about 45% more on v2, and that’s already in the math below.

Imagine an operation with 10 thousand support emails per month to classify into three or four buckets, with short texts similar to my cases:

In reais, at Friday’s exchange rate (R$ 4.99), 10 thousand decisions on Jev cost one real. A million costs less than a hundred reais. For any company with a million emails to classify, that vanishes into the budget.

One caveat about proportions: my cases are short, about 450 tokens per compact case by Jev’s count. A real customer email, with quoted thread history, can be three to five times larger, and cost scales linearly with that. Even so, we’re talking cents per thousand.

What this math shows is that the price difference between the hosted models is irrelevant next to the cost of being wrong. Jev costs 9 US cents more than Clef-flash per 10 thousand decisions, and in those same 10 thousand it makes about a thousand fewer errors. If a misrouted email costs more than a hundredth of a US cent in human rework, Jev pays for itself. This math assumes every error costs the same, which is never true: sending a critical ticket to the routine queue costs far more than the reverse.

Cloudflare’s allowance changes the math for small players. It’s 10 thousand “neurons” per day, shared by the whole account, resetting at midnight UTC. With my input sizes, that’s about 1,500 full-Clef classifications per day or almost 10 thousand Clef-flash ones, free. If you have 1,000 emails a day and nothing else running on the account, Clef comes out at zero cost with top-tier quality.

And local? Energy is what the benchmark didn’t measure. From what I see on my Strix Halo, the whole machine stays under 100 W under load. With Jebadiah at 200 ms per decision, 10 thousand decisions are about 35 machine minutes, under 0.1 kWh, a few cents of a real on the power bill.

The real local cost is the machine, your hours configuring ROCm and containers, and keeping it all standing. Whoever already has the hardware for another reason has low marginal cost. Whoever would buy the machine just for this is buying a problem of thousands of reais to save about a hundred reais per million.

Which model for which situation

You want accuracy and want to operate nothing: Jev

In the cloud, Jev is still the name. It won or tied practically every cell among the hosted ones, and only Microsoft tied it on the aggregate. In English it was 100% on five of the seven v2 tasks, and in Portuguese it repeated 97.6% on the aggregate without losing anything in translation, something no other model above the coin managed. On v1 it kept its 96.2% on the original with the malicious “reviewer” comment injected; Clef even gained one case, and Kev 4B also stayed put, just at a much lower tier. The 255 ms WAN latency came in 30 ms behind OpenAI’s, and it costs 19 US cents per 10 thousand decisions.

The trade-off is the one I already discussed in the previous post: it’s proprietary, hosted, of undisclosed size, and you depend on TypeSafe keeping the behavior underneath you. And it makes mistakes. It treated an invoice ID called “PASSWORD-523” as a usable credential, even with the record explicitly saying it doesn’t authenticate anyone.

You want Jev’s level paying a third: Microsoft Decision-1

Microsoft entered this market quietly, and entered well. Decision-1 is a 9B post-trained from Qwen3.5-9B, served via OpenRouter, and it was the first model to tie Jev on the compact aggregate: 97.6%, with the bootstrap interval centered on zero. The detail that won me over: 100% in Portuguese, better than its own English (95%), inverting the pattern of the entire rest of the bunch. On v1, the reversed-order and malicious-comment variants didn’t take down a single answer, and it got the critical ticket that Clef and OpenAI missed.

And it’s the cheapest hosted one I measured: US$ 0.065 per 10 thousand decisions, a third of Jev, charging the same per-token rate but reporting a third of the tokens. The savings come from the token count, and I can’t explain the exact mechanism, so treat it as a measurement, not a promise.

The weak spots exist. Latency is the worst among the hosted ones (422 ms median, versus Jev’s 255). On the compact cases it’s clean of confident errors, but add context and Portuguese errors show up with 99.5% confidence: the “perfect Portuguese” pattern doesn’t survive long text. And on the probability probes it’s weak, giving 87% to a fair coin. Microsoft advertises calibrated probabilities; my probes disagree. And like every dated-ID hosted model, the weight behind it can change without notice.

For a Portuguese-language operation that wants Jev quality while paying less, it’s the first candidate to run in your pilot.

You want the same level with open weights: Clef

The full 27B Clef tied with Jev in English (82 of 84) and finished 3 cases behind in Portuguese. It beat Jev on sensitive data handling in English (12 of 12 versus 11). It’s an open-weights model, so you can run it locally with about 54 GB of memory just for the weights in BF16, which I didn’t test: the full Clef ran only through the API. Among the open ones, it lost the crown to Perplexity, which scored higher with smaller weights.

Through the API it’s the most expensive of the five hosted ones, three times Jev, with no quality gain to justify it. The exception is the daily allowance: for small volume, it’s the best classifier you can get for free.

You want the cheapest in the cloud: Clef-flash

At US$ 0.038 per million, Clef-flash is the lowest price in the hosted market and covers 9,400 decisions per day within the allowance. Quality drops: 92% in English and 83% in Portuguese on the compact cases, the biggest language drop among the hosted ones, with an interval entirely below zero. On sentiment in both languages, and on incident status in English, it tied with Jev. On intent and sensitive data in Portuguese, it lost badly.

For sentiment tasks in either language, or for English classification where 1 error in 12 is acceptable, it does well. For Portuguese, measure first.

You already live in the OpenAI ecosystem: OpenAI Decisions

OpenAI’s debut in this market landed mid-table: 90.5% combined, an exact tie with Jebadiah, with the lowest hosted latency I measured (222 ms) and the same accuracy in both languages. It costs US$ 0.23 per 10 thousand decisions, between Jev and Clef. If your operation already speaks the OpenAI API and 9 out of 10 accuracy is enough, integration is trivial: a native decisions endpoint, no prompt at all.

The weak spots are revealing. Its worst cell was precisely numeric-threshold policy (8 of 12 in English, 7 in Portuguese): it treated “at least 10” as satisfied by a range of 8 to 12 and failed the AND/OR short-circuit. The calibration is schizophrenic: on the predicate probes (yes/no) it was the most accurate of the bunch, with near-zero average error, and on the choice probes it returned 94%/6% on a fair coin. And on the v1 variants it flipped 2 correct answers in each presentation (reversed order, opaque label, malicious comment), something only Perplexity didn’t do: Jev kept its aggregate accuracy, but also flipped some correct answers along the way. Oh, and the gpt-6-luna alias is not an immutable snapshot: reproducibility requires saving the responses.

You want the best open model, period: Perplexity Decider 27B

Perplexity Decider 1.1 27B is the surprise of the benchmark. It led the table with 99.4% on the compact cases, was the only one that didn’t lose a single task cell to Jev (3 wins, 11 ties), and hit 100% in Portuguese under all three conditions: compact, with examples, with distractors. Calibration is also the best measured, with a Brier of 0.012, and coverage is absurd: it answered with confidence above 0.9 on 96% of cases and got zero of them wrong. On v1, it didn’t flip a single answer across the order, opaque-label and malicious-comment variants.

Its only 3 errors on v2 were the same case in different clothing (it confused “notification sent to managers” with “mandatory approver”), and it made them with confidence below 0.9: the known error comes flagged. The weak spot is event-probability probes: asked about an event with a 1-in-4 chance, it answered 0.73. Strong classification does not imply reliable event probability.

The cost is hardware: about 49 GB of weights in BF16, a second and a half per decision on my Strix Halo (with ROCm’s reference kernels, no flash-attention; on a good NVIDIA it should be much faster, but that I didn’t measure). It’s Apache 2.0, public weights with no gate. If you have the machine and want the top of the table at home, this is the one.

You want the other open-weights top: Decision 3.0 (and beware the Edge)

Decision 3.0, released two days ago by the vLLM Semantic Router project, is the second model to sit at the top: 99.4% on the compact cases, 100% in Portuguese, and a tie with Perplexity so absolute that the two gave exactly the same labels on the 1,024 classifications, including the same two misses (the English relevance one, at confidence below 0.9, and the disputed severity case that already lives in the errata). It also cleared the v1 traps without flipping a single answer, got both indisputable critical tickets right, and its calibration is in the same class as Perplexity’s (Brier 0.014, 94% coverage above 0.9 with zero errors).

In other words: the open top now has two independent recipes that converge. For your pilot, either one works; run both if you can, because the probabilities differ even when the labels don’t. Latency is in the same family as Perplexity (1.4 s on my machine, same ROCm diet), and the weights take the same ~49 GB.

The little brother is another story. Decision 3.0 Edge (0.6B) answers in 45 ms and fits in 1.3 GB of GPU, but scored only 61% on compact, plummets 14 points in Portuguese, and worse: of the 68 guesses it gave with confidence above 0.9, 14 were wrong. As a confidence-threshold router, out of the factory, it’s a trap from the same class as Julia. The four middle checkpoints (flash, mini, nano, lite) I haven’t tested yet; they’re the ones that will tell whether the family has a sweet spot between the two extremes.

You need to run locally, with quality: Jebadiah 4B v2

Of the local ones, Jebadiah 4B v2 had the best compact accuracy, 90% combined, answering in about 200 ms. It tied with Jev on six of the fourteen task cells, including intent, implication and status in English and intent in Portuguese. The weak spot is numeric-threshold policy, 9 of 12 in both languages, and it loses ground when the context fills with distractors (76% in Portuguese under that condition, versus 82% for Mapika).

Mapika Decider 4B (the fastest of the 4Bs) and Kev 4B are right behind, both around 87%, and the 3-point difference between the three fits inside the error interval. They’re three different training recipes on top of 4B backbones, with errors on different tasks. The choice between them depends on your task, and I’d run all three in a pilot.

You will send examples in the prompt: Kev 9B

Kev 9B has a curious profile. On the compact cases it ties with Kev 4B, half a point apart with the interval crossing zero, meaning more than double the parameters bought nothing. With one solved example per class, it opens almost 10 points over the 4B, and with distractors it holds 7 more points. Brier also improves by about 25%: the probability it returns is better calibrated.

If your use is just the rule and the text, the 4B is enough and is more than twice as fast. If you’re going to send examples or long context, the 9B is worth the memory. The warning is Portuguese: 83% compact in PT-BR, almost 10 points below its own English, the biggest language drop among the models above 80%. Only Laya typed (14 points) and Julia (13) drop more.

You want Clef-flash behavior on your machine: local Clef-flash

On the v1 set, local Clef-flash reproduced the hosted one’s 520 classifications, hit for hit and miss for miss. On v2 the story changed: same total (439 of 504), but 19 different labels, with swapped strengths (the local one gets more Portuguese intent right, the hosted one gets more relevance right). Among the local ones it’s still the best on the v1 operational-policy set (90.8%), ahead of every 4B.

The gotcha is latency: 618 ms median on the Strix Halo, three times Jebadiah, for the same v2 accuracy. If you pick it, it’s for the known behavior.

You need to run offline on weak hardware: Laya and Julia, with many caveats

Laya multilingual was the worst in the benchmark for accuracy (37% combined, near the coin), and the fastest by far among the ones that get anything right: 19 ms median, more than ten times faster than Jebadiah.

The three Laya checkpoints have between 322 and 421 million parameters, fit in under 1 GB of BF16 weights by parameter math, and the project documents CPU execution and an ONNX runtime (which I didn’t measure). PyTorch’s peak GPU allocation for the multilingual one stayed under 2 GB. A 4B needs about 8 GB just for weights at the same precision.

Julia-1 goes one step lower: 144 million parameters, 550 MB of weights, 12 ms median on GPU and 71 ms on a 4-thread CPU. It’s the fastest model I’ve ever measured here, and also the worst: 31.5% accuracy, glued to the 31% coin-flip line. In Portuguese it answered “cancellation” on all 12 intent scenarios, including the ones asking for a refund or access (the vendor’s Portuguese evaluation was done in pt-PT, and it shows). The bigger disaster is confidence: of the 71 guesses it gave above 0.9, 48 were wrong. A confidence threshold won’t save this model, because it’s exactly on the confident guesses that it errs.

That’s a design choice. Laya and Julia were made to be small encoders, fine-tunable on your task, to run at the edge. What I tested were the factory checkpoints, with no tuning, applying rules they had never seen, on broad tasks. In that scenario they did badly, period.

One detail confirms the fragility. On v1, when the reviewer’s malicious comment comes in, Laya drops from 44% to 19% and typed-decisions to 15%. It obeys the comment the instructions say to ignore, and confidently: 17 of the 28 guesses above 0.9 were wrong. Julia failed all 5 policy-counterfactual pairs: change the rule, it keeps the answer.

If you want what Jev delivers, factory Laya and Julia are very far from it, and no small model (GLiNER included) came close. If your problem is “I need to classify short text into three buckets, on a device with no internet and no GPU, and I have a thousand labeled examples to fine-tune on,” these two are the only ones on the list that fit. Fine-tuning is the part I didn’t test. It’s another question, and it needs another benchmark.

The baseline: Simple-Jev with a stock Qwen

Simple-Jev reading tokens from Qwen3.5 4B, with no decision training at all, scored 74% on compact v2. That’s the yardstick for “how much specialized training buys”: Jebadiah, Mapika and Kev, all on similarly sized backbones, came in 13 to 16 points above. The training buys a lot.

The curious part is that on v1 Simple-Jev tied with Jebadiah (111 of 130 originals) and got both unambiguous critical tickets right, which several better models missed. It’s also the slowest of the bunch, almost a second and a half per decision, because it’s running a whole LLM.

How hard it is to run the best open models on your machine

All these models speak the same HTTP protocol, called SystemOne, which Jev popularized: you send a state (the text), one or more closed-answer questions, and get back the chosen option and the probability of each. Swapping models is swapping the port. OpenAI Decisions breaks from the pattern with its own native endpoint, but the idea is the same. What changes is how much work it takes to get each one standing, and there the difference between them is big.

OpenAI: just an API key

The easiest of all: create the key, load credit and call the endpoint. The benchmark’s 1,048 requests ran in 4 minutes, without a single retry. The only hiccup was the first probe returning “balance exhausted” until I put US$ 20 in the account.

Microsoft Decision-1 is almost as simple: if you already have an OpenRouter account, it’s point at the model and call the decisions endpoint. No GPU, no Azure subscription, no installation. In my test, 10 of the 504 v2 calls took a 429 and passed on retry, so plan for retries.

Kev and Jebadiah: one pip and one command

Kev is the simplest of the 4B crowd. The project installs straight from GitHub and brings up a server with one command, downloading the LoRA adapter and the decision head on top of base Qwen3.5:

pip install "kev @ https://github.com/jaredpalmer/kev/archive/fc4a17e194bca35a48c57211392ec49ea54a94ac.tar.gz"
python -m kev.serve --run jaredpalmer/kev-4b --host 0.0.0.0 --port 8000 --device cuda

Jebadiah follows the same pattern, with the server package inside its own repository:

pip install "jebadiah-server @ https://github.com/getainode/jebadiah/archive/e9793abc66d1a7c5ad878d51eda656f1e9c5b9f5.tar.gz#subdirectory=server"
jebadiah-serve --model frontier-infra/jebadiah-4b-v2 --host 0.0.0.0 --port 8000

With either one up, a decision is a POST:

curl -s http://localhost:8000/v1/systemone -H 'content-type: application/json' -d '{
  "model": "kev-4b",
  "state": "Cliente diz que foi cobrado duas vezes e pede o estorno da segunda cobrança.",
  "questions": {
    "fila": {
      "type": "choice",
      "instructions": "Escolha a fila correta pela política de suporte.",
      "criteria": {
        "financeiro": "Cobrança, estorno, nota fiscal.",
        "tecnico": "Erro, bug, indisponibilidade.",
        "vendas": "Upgrade, plano novo, orçamento."
      }
    }
  }
}'

The response carries the option and the probability distribution, and that’s where you apply your threshold, log and decide whether to send it for human review. On an NVIDIA with CUDA and 16 GB of VRAM, a 4B in BF16 comes up without drama. The weights are about 8 GB.

Laya and Julia: smaller still

Laya is a Python package with an encoder inside. It installs with pip install laya and the project documents CPU execution, which is its whole point. The version pinned on my server was 0.4.1, and the multilingual one’s peak GPU memory stayed under 2 GB. To run on a GPU-less laptop or a small container, it’s the only one on the list that fits, no discussion.

Julia-1 is similar in spirit: its own Python runtime, one predict(state=..., questions=...) call and done, no standard Transformers pipeline. I ran the same container on GPU and on CPU, and both gave the same accuracy with the same responses in 97% of cases. On a 4-thread CPU it answers in 71 ms, which for an edge classifier is great. The problem is still that it scores at coin-flip level.

Where it gets complicated: AMD, ROCm and the rest

In my case the hardware is AMD, and then “one command” turns into a day of work. The points that cost me time, to save you yours:

• None of these projects test on ROCm. Kev had a gfx1151-specific segfault caused by CUDA graphs (issue 170), fixed only after the 1.0 tag, so the pinned version is a main-branch commit and the official tag crashes.
• The flash-linear-attention Triton kernels Qwen3.5 uses in the DeltaNet layers have no tested support on gfx1151. Everything ran on the pure-PyTorch path, slower than the latency published on CUDA. Jebadiah’s 200 ms in the table is with that handbrake pulled.
• Mapika Decider forces CUDA graphs when it sees the “cuda” device, which on ROCm is also called “cuda.” I had to turn it off by hand.
• You need to export HSA_OVERRIDE_GFX_VERSION=11.5.1 and PYTORCH_ROCM_ARCH=gfx1151 and use a base image with ROCm torch already validated. Any random pip install torch overwrites it with the CUDA build and breaks everything. In my Dockerfile every package is installed with --no-deps and the build fails if torch stops being the ROCm one.
• One model per container, one container at a time. The Strix Halo has memory shared between CPU and GPU, and three resident 4B models plus the Ollama that already runs there exhaust the 64 GiB the GPU can see.
• Perplexity Decider was the most annoying of all. The release expects a specific CUDA torch (I swapped in the ROCm 7 torch 2.9), the flash-linear-attention kernels don’t exist for it (I ran on PyTorch’s reference kernels, slower), and the default loader blew past the host’s 24 GiB RAM limit: I needed a custom loader that sends the 49 GiB of weights straight to the GPU. The model card still said “private” when I started, but the Hugging Face API confirmed it was public and gate-free, and the shards downloaded anonymously. The d3, at the same size, inherited the same treatment. The d3-edge, luckily, comes up with the standard Transformers loader and 1.3 GB of GPU.

None of this is impossible, and it’s all documented, with Dockerfiles, compose and the hashes of each checkpoint, in the repository’s setup guide. But it’s a real cost that the “local is free” spreadsheet never includes. If your machine is NVIDIA, most of this list disappears.

The full 27B Clef I didn’t even try locally: it’s about 54 GB of weights in BF16 before any activation. Perplexity, at 49 GB, proved a 27B runs on the Strix Halo, at the cost of a second and a half per decision with the reference kernels. The 9B Clef-flash runs, with about 18 GB of weights. The 4Bs are the sweet spot for consumer hardware.

Highlights and gotchas

Laya truncated all long Portuguese. Under the distractor condition, base Laya reported truncation on all 84 Portuguese calls, discarding almost 31 thousand state tokens, and on none of the 84 English ones. The English checkpoint’s default context limit is only 512 tokens (the multilingual and typed ones use 1,024, configurable up to 8,192), and Portuguese tokenizes heavier. The other models don’t even expose truncation telemetry, so “zero truncation reported” on them means “I don’t know.”

Portuguese costs more tokens. For the same 84 case pairs, Jev reported about 14% more input tokens in Portuguese than in English, and both Clefs about 15% more. If you budget a bilingual operation by the English average, you’ll come in low.

Does an example in the prompt save a weak model? It saves nothing. One solved example per class left Laya the same or worse; several of its aggregates dropped. It helped Kev 9B (plus 4 points) and Clef in Portuguese (plus 3.6 points, reaching Jev’s level), and almost nothing on Jev, one more case in Portuguese. Validate examples on real traffic before assuming they help.

Size correlates, and explains little. Among the 19 competitors of known size, the Spearman correlation between parameters and compact accuracy was 0.96. The Kev series is more informative: 63% at 0.8B, 87% at 4B, 88% at 9B. The big jump is from 0.8B to 4B, about 24 points, with an interval between 16 and 33. From 4B to 9B the compact difference crosses zero, and several nominal 4Bs, with similar backbones, gave very different results. In the d3 family, the flagship and the Edge sit almost 40 points apart under the same release, while a 9B, Microsoft, ties at the top of the hosted ones. Size helps up to 4B; past that, the training recipe rules.

The 27B Clef missed a critical ticket that two of the Layas got right. On v1, one severity scenario says continuous data loss in production is critical, regardless of how many customers it affects. The record describes confirmed permanent loss, from a single customer. Jev, Clef-flash, Perplexity, d3, Microsoft, Laya typed, Simple-Jev and Laya multilingual answered “critical.” Full Clef, OpenAI, base Laya, Eikos, Jebadiah, Mapika, Kev 4B and Kev 9B answered “degraded.”

It’s a single case, and an error rate would require many more. But it shows that the average hides exactly the error that costs the most. When the impact facts are structured, the severity rule belongs in code, and the model comes in only to extract the facts from free text.

Hosted and local diverge over time. Local Clef-flash reproduced the hosted one on v1’s 520 classifications and diverged on 19 of v2’s 504. Cloudflare keeps the weights version behind the hosted alias undisclosed. If reproducibility matters, pin the local hash.

High confidence and safety are different things. On compact v2, Jev, Clef, both Clef-flashes, Eikos, Jebadiah, OpenAI, Microsoft, Perplexity and d3 got zero guesses above 0.9 wrong. Perplexity is the extreme case: it answered at that level on 96% of cases without missing one, with d3 right behind (94%, zero errors). Microsoft covered 95% and OpenAI only half. Microsoft’s caveat: the clean record is only on compact, because with examples or distractors it misses in Portuguese with confidence above 99%. Mapika missed 5% of the guesses at that level, Kev 4B 3% and Simple-Jev 14%. And the two smallest of the bunch broke the pattern violently: Julia missed 48 of 71 guesses above 0.9, and d3-edge missed 14 of 68. Coverage varied too: Jev answered above 0.9 on almost 90% of cases, Clef on almost 80%, Clef-flash on 40% and base Laya on 2 of 168. On v1 Jev missed 1% of the guesses above 0.9 and base Laya missed 61%. A confidence threshold only works after being validated on your distribution, and Laya typed’s low ECE in Portuguese left it at the same 36% accuracy.

Perplexity didn’t fall for presentation traps. Across the three v1 variants (reversed options, opaque labels, malicious comment), it kept the same answers as the original, without flipping a single one. The d3 repeated the feat, zero flips too. Microsoft only stumbled on the opaque labels (2 flips, one in each direction); order and comment didn’t move it. OpenAI flipped 2 in each variant, and Laya collapsed to 19% under the comment. The price of that robustness is latency: a second and a half per decision on my machine, the worst median of the bunch.

Numeric thresholds are a code problem. The threshold-triage task, with clear numbers and an explicit rule, gave 100% on Jev and both Clef-flashes and under 30% on both Layas in English. An if solves that with 100% and zero latency. The model only comes in when the number is buried in free text, and even then the right thing is to extract the number and leave the comparison to code.

Lottery probability is another skill. On the 24 no-information probes (coin, die), Jev had the worst choice error among options. The numeric-estimate crown changed hands with OpenAI’s debut: its yes/no predicate missed almost nothing, while the choice probe returned 94% for heads on a fair coin. Perplexity, champion of everything else, answered 0.73 for an event with a 1-in-4 chance, and Microsoft went the same way, giving 87% to the coin. Laya typed had the second-best choice error, behind only Kev 9B and neck-and-neck with Clef, and still classified badly. It’s the “student who marks answer A” from Paulo’s essay, now measured: calibration on lotteries and accuracy on classification are independent measures.

Where the data converges

1. The overall top is open-weights, and now there are two. Perplexity Decider 27B and Decision 3.0 tied at 99.4% on the compact cases, with 100% in Portuguese and identical labels on the 1,024 classifications, both with Apache 2.0 weights running on my machine. Their edge over Jev is under 2 points and the interval touches zero, so treat it as a snapshot of this set. Their cost is hardware and latency, not API. In the cloud, the evaluation order changed: start with Microsoft Decision-1, which ties Jev on the aggregate, closes 100% in compact Portuguese and costs a third. Jev comes next, faster and with different errors, because an aggregate tie is not interchangeability. OpenAI Decisions landed halfway in price and accuracy.

2. Running locally became viable at 4B. Jebadiah, Mapika and Kev 4B deliver between 87 and 90% on the compact cases with 200 ms latency on a desktop machine, and in a pilot with your task any of the three can win. If your machine handles 49 GB of weights, Perplexity delivers the top of the table at home. The financial math justifies using the machine that already exists, for data control or because of an obligation to stay offline. Buying a machine for this it stops justifying.

3. Small models are another category. Judging Laya and Julia by the accuracy column is reading the table wrong. They get half or more wrong out of the factory, fall hard under a malicious comment, and together with the d3-edge they’re the only ones on the list that fit on a weak device and answer in tens of milliseconds. The Edge is the best of the three on accuracy (61%), but it plummets in Portuguese and gets one in five confident guesses wrong. If Jev-like accuracy is a requirement, cross all three off. If running offline on small hardware is a requirement, cross everything else off and start with fine-tuning them on your task, which is the missing measurement. And be careful with Julia and the Edge in particular: 48 errors in 71 and 14 in 68 confident guesses are the portrait of models that don’t know what they don’t know.

4. Portuguese costs about 14% more tokens and drops the models in the middle of the table by 1 to 10 points, with Kev 9B and Clef-flash in the worst case. Jev loses nothing, OpenAI ties both languages, and Perplexity, d3 and Microsoft close 100% in compact Portuguese, better than their own English, although Microsoft goes back to missing in Portuguese when the context grows. Julia goes the other way and plummets 13 points, and the Edge plummets 14. For a Brazilian audience, that’s the first column to look at, before price.

And what holds for everyone: none of these models replaces the rule that’s already in code. Numeric thresholds, severity policy with structured facts, tool permission, all of that stays in the if. The model comes in to turn free text into structured fact. And the test that matters is yours, with your tickets, because each cell in this table has 12 scenarios and your operation has thousands.

The cases, each model’s raw responses, the scoring scripts and the guide to reproduce everything are in the benchmark repository. If you run it with your tickets and get a different ranking, tell me.

At that moment I knew I was a hiccup away from losing everything. This post is the post-mortem of what happened, and the first part was written in the heat of the disaster, with the rescue copy still running.

Update of October 11: the copy finished and the ending was a happy one. No file was lost by accident, both NASes are healthy, each with its own RAID-6. The full recovery is in the second half of this post. The first part stays as it was published, untouched.

I’ve been using a NAS for a long time

I’m not new to this. I made an entire video mini-series on storage and filesystems (in Portuguese), and I recommend watching it before asking me “what’s a good beginner NAS?”. There is no such thing.

I’ve used external storage since the second half of the 2000s, when I was a Mac user and Drobo existed. The company is gone, but I had three of their DAS units before migrating to Synology, first on an entry-level model and then on the mid-tier DS1821+, which I’ve been using for more than five years. I’ve written about it here, setting up NFS on Linux and accessing it over iSCSI.

It’s been very valuable to me, and you don’t get to question why I need that much space. It doesn’t matter: everyone decides what they keep on theirs.

When I was producing YouTube videos, I edited 4K footage straight from the NAS, thanks to the NVMe cache and 10 GbE networking. I got used to big storage at 10 gigabits and I can’t go back.

The array was organized in SHR, Synology’s hybrid RAID, which in practice behaves like RAID-5 (more on that later): it survives the loss of one drive without losing any data. I always thought that was enough.

Hard drives die. Always

Make no mistake: hard drives go bad. Never, ever trust a lone drive forgotten in a closet. It will fail, and you will lose everything on it.

The best public data on this comes from Backblaze, which runs hundreds of thousands of drives and publishes failure rates every quarter. In the Q2 2026 report, with 354,415 drives monitored, the annualized rate was 1.73%, and the lifetime rate for the whole fleet stands at 1.41% per year.

That sounds small. Translated to human scale:

• One drive on its own, for 5 years: the chance of it dying in that period is close to 7%. That’s roughly the chance of rolling one specific number on a 14-sided die. Would you bet every family photo on that?
• One drive on its own, for 10 years: about 13%, one in eight.
• Eight drives, like my NAS, for one year: the chance of at least one failing is about 11%. In five years, more than 40%.

And those numbers are from a datacenter, with controlled temperature, clean power and drives spinning all the time. A drawer drive that sits idle for years and gets knocked around in a move doesn’t have statistics that pretty.

Memory cards are worse, and there isn’t even an equivalent statistic to consult. What exists is torture testing: the Great MicroSD Card Survey tested 216 cards over a year of continuous writing, and 52 of them, one in four, had already died.

Genuine cards started throwing errors, on average, after about 2,500 rewrite cycles; counterfeit cards, after 700. A memory card is transport media. Keeping the only copy of anything on a microSD is asking to lose it.

The practical conclusion is the same as in the mini-series: important data needs redundancy. A parity array exists precisely so that the death of one drive, which is a matter of time, doesn’t take anything with it.

What happened

With all that, I should have been at ease with my setup. I wasn’t.

It started the day before yesterday, Sunday. For a while I’d been watching the volume approach the ceiling.

At first it was 8 drives of 10 TB. Over the years I swapped them, one by one, for 20 TB drives (DSM shows binary units; on the label they’re 12 and 22 TB). It’s a slow process: each swap takes days, because the array has to rebuild and redistribute the data.

I decided to swap one more, the seventh. Synology supports hot-swap, so it’s simple: pull the old drive, slot in the new one, add it to the array and let DSM handle the rebuild.

On Sunday night it looked like this: rebuild starting, all eight drives healthy, 108 TB allocated out of 121.8 TB.

On Monday, at 8 a.m., the beep. Drive 1, the new one, with 13 hours of use, marked critical:

And drive 8, one of the old ones, with 22,095 hours powered on (two and a half years), also critical, with a read error at 08:05:

The pool’s message left no room: “Unrecoverable errors have occurred because drive errors occurred after storage pool degradation. Please back up your data immediately.”

Why two drives is the end

In RAID-5, if two drives go down, redundancy is zero, and in theory the whole array goes with them. It’s an unrecoverable situation.

Anyone who watched my playlist knows why. An array like this doesn’t store “files,” one per drive. A file is a collection of blocks, and the blocks are spread across all the drives, along with the parity. Losing a drive is losing a piece of every file. It doesn’t matter that the other seven are healthy, because they only have parts.

Parity is there to recompute the missing piece when one drive disappears. With two missing, the math doesn’t close.

And there’s a cruel detail in the rebuild: to recreate the new drive, the array has to read every other drive end to end. That’s more than 100 TB of continuous reading. If some old drive had a bad sector hiding in a corner nobody had read in a long time, that’s when it shows up. That’s what happened with drive 8.

Where do you copy 90 TB to?

The only way out, at that point, was to thank the heavens that the volumes were still accessible, with the files showing up, and use the window to copy everything out immediately. I couldn’t touch anything else.

The problem: copy about 90 TB to where? Nobody has an empty 100 TB NAS lying around at home.

Normally I’d buy this kind of hardware in the United States, on a tourist trip, and bring it back with me. It’s much cheaper. Buying expensive electronics in Brazil isn’t worth it: the country has charged import taxes worse than Trump’s tariffs for decades, and the final price usually goes past double the American list price.

Except I had to choose. What is it worth to lose 90 TB of data I spent years collecting and that is nearly impossible to recover from anywhere else? It’s one of those situations where the answer is “it’s priceless,” and so, whatever the price, I pay.

I found a storage reseller close to home, Controle Net. Good people, very quick to respond, and they sent me a proposal for equipment with same-day delivery.

Their first suggestion was a QNAP TS-832PX. I researched it before accepting. It has the same 8 bays and ships with 10 GbE, but the processor is a 1.7 GHz ARM Cortex-A57 (Annapurna Labs AL324) with 4 GB of RAM, half the 8 GB minimum that QuTS hero asks for. For RAID-6 on ZFS, with checksums and double parity on every block, that’s weak hardware, as I explain further down. And it would be a downgrade from the DS1821+ I already had, which runs on a Ryzen V1500B.

I asked for another option, and after some back and forth I settled on a QNAP TS-873A, 8 bays, with the same Ryzen V1500B as my Synology, 8 GB of RAM, and 10 Toshiba MG11 24 TB enterprise-class drives.

This is why you have to do your own research and know, objectively, what you want to do with the equipment. The reseller didn’t act in bad faith: they suggested an 8-bay NAS to someone who asked for an 8-bay NAS. The one who knew it would run ZFS in RAID-6 was me. If I had accepted the first proposal in a panic, I’d have paid a lot for a worse machine than the one I was replacing.

R$ 185,320.00. Eye-watering.

What it would cost in the United States

I checked today’s American list prices. About the exchange rate: the dollar is at R$ 4.97 right now, but that’s the effect of Sunday’s first-round election results, which knocked the rate down more than 4% on Monday. Through Friday it closed at R$ 5.22, and that’s the fair rate to compare with, because that’s the rate the reseller’s stock was built at:

At R$ 5.22, about R$ 60 thousand. I paid R$ 185 thousand, more than three times that.

The comparison isn’t perfect: the Brazilian proposal includes installation, training and six years of technical support, and the equipment was on my desk by the end of the same day. But it gives the measure of the Brazil effect.

If the rule of thumb is “double the American price,” normal would be about R$ 120 thousand. The extra R$ 65 thousand is, in my own rough estimate, the sum of services, local stock of enterprise drives and my urgency. Whoever buys on a Monday morning with the array dying is in no position to haggle.

And drives are expensive everywhere

To make it worse, this is the worst possible moment to buy hard drives. The AI bubble is sucking up production.

I thought Seagate had left the consumer market. I checked, and that’s not quite it: it hasn’t left, but in February 87% of its hard drive sales were already nearline drives, for datacenters, and WD gets 89% of its revenue from cloud customers and only 5% from retail. WD’s CEO said the company is “pretty much sold out for calendar year 2026.”

Seagate has said it won’t expand production capacity; growth comes only from higher-density drives. For someone buying drives at home, the practical effect is similar to them having left: you get what the hyperscalers didn’t want, at whatever price the seller asks.

QNAP instead of another Synology

After spending the morning and afternoon between negotiation, paperwork and payment, by the end of the day I had the QNAP powered on next to the Synology.

Choosing QNAP went beyond availability. Synology has been making questionable business decisions.

In 2025 it launched the Plus line requiring its own-brand drives or drives it certified.

The own-brand ones are Seagate, Toshiba and WD drives rebadged with custom firmware, and anyone using something else was left without pool creation, health monitoring, deduplication or firmware updates. After the outcry, it walked that back in DSM 7.3 and allowed HDDs and SATA SSDs from other brands.

Except M.2 pools still require the compatibility list. The uncertainty is left hanging: what stops them from doing it again?

Setting up the QNAP is very easy. QuTS hero, their ZFS-based system, is as simple to use as Synology’s DSM.

Fixing my mistake: RAID-6 and ZFS

This time I knew I had to configure RAID-6, which survives two drives failing at the same time. The downside is less total capacity, because the equivalent of two drives becomes parity instead of one. The upside is that I shouldn’t have to go through an emergency like this again.

One of QNAP’s strengths is supporting ZFS out of the box, well integrated and easy to use.

For those who don’t know it: ZFS was born at Sun Microsystems, for Solaris, in the mid-2000s, and today lives on as OpenZFS on Linux and FreeBSD. It merges into one system what used to be three layers, the RAID, the volume manager and the filesystem. It keeps a checksum of every block, so it notices when a drive silently returns corrupted data, and repairs it on its own from parity. And it never overwrites data in place: every write goes to a new block, which makes snapshots practically free.

Its reputation as a RAM hog comes from two things. The first is the ARC, its cache, which by design takes up almost all the machine’s free memory. That scares anyone looking at the graph, but it’s cache: it gives the memory back when another process asks. The second is deduplication, which keeps a huge table with the hash of every block and needs it in RAM or it becomes unusably slow. The old rule of “1 GB of RAM per TB of disk” comes from there, and only applies to people who turn deduplication on.

Even without it, ZFS is heavier than ext4 or a simple Btrfs. Computing checksums, compression and double parity for every block costs CPU, and with little memory the cache shrinks and everything gets slow. On weak hardware, like an entry-level NAS with an ARM processor and 2 GB of RAM, it suffers.

QNAP asks for at least 8 GB for QuTS hero. Mine came with 8 GB and a quad-core Ryzen, which works for my use, which is storing large files and serving them over the network. Deduplication stays off.

I bought 10 drives for an 8-bay NAS, so two are left in the drawer as spares, for the day one fails.

For my simple setup, three settings:

• 5% over-provisioning. It’s a slice of the pool the system reserves and never hands out for data. ZFS always writes to new space and gets slow when the pool fills up near the limit; this slack keeps performance stable. On SSD pools it also extends drive life, but these are hard drives, so the minimum is enough.
• 5% guaranteed snapshot space. It’s the reserve for snapshots, the “photos” of the state of your files that let you go back after an accident or ransomware. Guaranteeing the space means the snapshots keep existing even if I fill up the rest. My data changes little (it’s mostly cold archive), so snapshots take almost nothing and 5% is plenty.
• Alert at 85%. The system warns me when the pool passes 85% usage, with room for me to act before hitting the ceiling. It was exactly the lack of room that had me swapping drives in a hurry on the Synology.

Adding the two reserves, about 13 TB are set aside and 112.9 TB are left for use.

A detail of my case: I’m copying the 90 TB with snapshots turned off, and I’ll only enable the schedule after the copy finishes. A snapshot protects a file’s previous state, and on an empty NAS receiving its first load there is no previous state worth protecting. The source of truth is still the Synology.

And there’s a practical cost. I stopped and restarted the job several times, deleted folders that had gone in by mistake and copied again. With snapshots on, every deleted thing would keep taking up space inside some snapshot, and I’d be filling the pool with the migration’s own garbage. When everything is on this side and checked, then yes: scheduled snapshots, to protect me from deleting what I shouldn’t and from ransomware.

After that I created one giant “thick” partition. In ZFS you can create a “thin” folder, which only takes up what’s inside it and can promise more space than exists, or a “thick” one, which reserves the full size at creation. Since it’s a pool with one owner and one use, thick is more predictable: the space is mine and nothing competes for it.

Finally, I created the shares and installed the HBS 3 app.

Copying 90 TB

HBS 3 (Hybrid Backup Sync) is QNAP’s app for backup and synchronization. It connects to another NAS, to rsync, FTP, SMB or the cloud.

I connected to the Synology’s SMB share and told it to sync everything.

I had to stop and restart a few times, tweaking the job settings, because it was absurdly slow. And the file count wouldn’t stop growing:

865 TB on a 108 TB NAS, and that’s when I understood what was going on.

I excluded two kinds of path from the job:

• The #snapshot directories. On Synology, when you check “make snapshot visible,” each shared folder gets a #snapshot subfolder with a full view of the folder at each point in time. Inside the Synology that takes no space, because a snapshot only stores the difference. Seen from outside, over SMB, every snapshot looks like a full copy of everything, and HBS was trying to copy them all.
• The restic backup directories. restic stores everything in hundreds of thousands of pack files of a few megabytes each. Network copying is fast with large files and suffers with small ones, because each one costs an open, metadata and a close. And those backups I’ll redo from scratch on the new NAS.

With that, throughput rose to an average of 400 MB/s.

That’s less than half of what a 10 GbE network delivers, but it’s more than three times the gigabit most people have at home. Both sides really are at 10 gigabits:

Why doesn’t it reach 1,000 MB/s? My main hypothesis is drive 8. The source array is degraded, and every read that goes through the affected pieces has to be recomputed from parity, with a drive that on top of that returns errors and retries. It makes sense that the Synology can’t go faster than this.

There’s a second factor, which my own screenshots give away: the Synology has jumbo frames on (MTU 9000), and the QNAP image shows the default MTU of 1500. That screenshot is from earlier; I’ve already changed the QNAP to 9000 as well.

Why it matters: MTU is the maximum size of each packet that travels on the network. At the default 1500 bytes, filling a 10-gigabit link means processing more than 800 thousand packets per second, and each packet costs CPU on both sides, for the header, the checksum and the interrupt. With jumbo frames each packet carries six times more data, so it’s one sixth of the packets for the same volume, and more processor is left for moving files. It only works if everything in the path matches: both cards and the switch. If one side stays at 1500, the connection negotiates down to the smaller value and the other side’s jumbo frames do nothing.

At 400 MB/s, 90 TB takes about two and a half days, and I prefer two and a half guaranteed days.

What Synology explained

In parallel, I opened a ticket with Synology support. After some back and forth, I sent the logs. They responded fast, the same day, and I’ll give them that. The engineer’s explanation, summarized:

• Drive 8 had been logging media errors for over a month. A media error is when the NAS tells the drive to read a sector and the drive answers that it couldn’t, even after several retries. It’s always an internal drive failure, and the drive should have been replaced.
• It was drive 8’s poor health that took it down during the rebuild of drive 1.
• In practice I lost two drives, and the pool should have crashed completely. It didn’t because of how SHR works.

SHR isn’t a single RAID-5. To use drives of different sizes without wasting space, it slices the drives and builds several smaller RAIDs, one per size band, and joins everything into one volume. The engineer sent this example diagram:

In my case, the pool is made of two partitions. In the first, the rebuild of drive 1 was far enough along and the failure of drive 8 was mild enough that both kept working. In the second, both are missing. That’s why the pool didn’t crash entirely and I can still see the files.

His recommendation was the one I was already following: copy everything out before anything else, because there’s no telling whether the situation gets worse or whether it’s fixable. After the backup he can even try forcing drive 8 back into the pool so the rebuild continues, but there’s no guarantee the drive will go back in, or that it will survive the process. In his words, copying and recreating the pool from scratch is probably the quickest way forward.

Classic RAID-5, by the way, requires all drives to be the same size, and to grow capacity I’d have to replace all of them at once. SHR is what let me swap one at a time over the years. Ironically, it’s also what saved me from losing everything at once.

The two mistakes

The way I understood it, there are two mistakes, one mine and one theirs, and that’s where the whole disaster lives.

Mine: I underestimated RAID-6, which on Synology is called SHR-2. With two parity drives, drive 8 failing in the middle of the rebuild would have been just a scare. I traded safety for 20 TB of space and spent five years thinking I’d made a good deal.

Theirs: from the logs, the software knew drive 8 had been failing for at least a month, and I never got a single notification. I have email notifications configured. And they work: when the critical situation blew up, I got a flood of emails. This one, about drive 8’s bad sectors, arrived at one in the afternoon on Monday, five hours after the disaster:

Before that, nothing. No warning about drive 8 until it failed catastrophically.

If I had received one, I would never have tried to swap drive 1 before replacing drive 8. The whole situation was avoidable, and it was the result of a human mistake plus some software bug.

That’s the other reason I didn’t buy another Synology. I have a principle: if the horse throws me, I put the horse down. I can’t trust it again.

Where I am now

So far I’ve copied about 20 TB of the original 90 TB, and HBS 3 keeps working. I still don’t know whether I lost any data permanently. I hope not, but the most likely outcome is that I lost what was in the RAID slice the engineer described as crashed.

I have offsite backup on AWS Glacier of what matters most, but not of everything.

A lot of people ask why I don’t use S3 or some other cloud storage for everything. I return the question: think for ten seconds. How long does it take to transfer 90 TB over your internet connection? Do the math:

• With a solid 1 gigabit of upload, never dropping for a second: more than 8 days.
• With 500 megabits: almost 17 days.
• With 100 megabits, which is good upload for many homes: 83 days.

And that’s to upload. When disaster hits you need to download it all back, with the clock running. Not counting the rent: 90 TB on standard S3 costs about US$ 2 thousand a month at list price. Cloud is great for the critical subset, which is how I use it. For the whole archive, physics and the invoice won’t allow it.

Lessons

I was put in an impossible situation on a Monday morning, I acted right away as best I could, and I’ll probably come out of this mostly unscathed. We’ll see, HBS 3 is still syncing. What I take from it:

1. Single parity isn’t enough for big drives. With 20 TB drives, a rebuild takes days and reads everything. That’s the window in which the second drive tends to show up. RAID-6, SHR-2 or RAIDZ2.
2. Don’t rely only on the vendor’s notifications. Look at SMART and the drive logs before starting any swap. I didn’t, because I trusted I’d be warned.
3. Replace the sick drive first, then the small one. A capacity upgrade on an array with a suspect drive is roulette.
4. Leave slack. I was swapping drives because the volume was at the ceiling. An 85% alert exists for that.
5. Have somewhere to copy to. My way out cost R$ 185 thousand because I had no plan B. A second NAS, even a modest one, with what matters, would have bought me time to shop calmly.
6. RAID is not backup. I know that, I teach that, and I still only had offsite for part of it.

The copy is done and I already know the size of the damage: zero. The full recovery comes next.

Six days later: the end of the story

When I published the first part, I had about 20 TB copied and no certainty. The outcome: I finished evacuating the ~91 TB, verified file by file, and lost nothing by accident. Not even what was on the fallen RAID slice, because it stayed readable until the end.

Who operated the recovery were my two agents. The desktop agent controls my PC and both NASes over SSH, and the homelab agent takes care of the server where my 57 containers run. The two coordinate by message: one leaves a report in the other’s inbox, and I follow the conversation.

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This part matters because nothing that follows was me typing commands. I made the decisions; they executed, measured and screwed up badly a few times, as you’ll see.

HBS 3 was quietly betraying me

In the first part I told how HBS 3, pulling the Synology shares over CIFS, reached 400 MB/s after the adjustments. What I didn’t know: speed was the smallest of its problems.

Two defects showed up when the agent checked the source inventory against what HBS reported:

• It didn’t go where it had no permission, and told nobody. HBS mounts the Synology as my user. The Bitcoin/data directory, 901 GB with my node’s wallet and chainstate, belongs to uid 101 and is closed to anyone else. CIFS couldn’t get in there, and HBS reported success without recording the omission. A “green” backup that leaves the entire Bitcoin node behind.
• It skipped files with “unsupported” names. 113 files, recorded only on a detail screen nobody opens during a days-long copy.

A backup tool that reports success while omitting what it couldn’t read is worse than one that fails screaming. Verify the backup against an independent inventory of the source, never against the tool’s own log.

We abandoned HBS. The agent opened SSH on both NASes, enabled the rsync server on the QNAP, and the Synology started pushing straight to its rsyncd, with nothing going through the PC. Each job’s wrapper: rsync -a --numeric-ids -W --partial --timeout=600, attempts with retry, and never --delete. The --numeric-ids preserved the original uids with no chown afterwards, --partial turned any interruption into a continuation, and the absence of --delete made the destination always a superset: re-sharding or killing a job never cost data.

The bottleneck wasn’t the broken drive

Some context was missing from the first part. This Synology was born with eight consumer-line Seagate IronWolfs, and over the years I had been swapping them one by one for 20 TB IronWolf Pros. The disaster happened exactly during one of those upgrades: I swapped drive 1 for the new Pro and, during the rebuild, drive 8 failed. One single old drive was left in the array: drive 2.

And it, not drive 8, was the evacuation bottleneck. Measured under load:

• Drive 2 (the old consumer one, with 37 thousand hours of use): 91% utilization, delivering 27 MB/s, each I/O taking 4 to 15 times longer than its siblings.
• The six Pros: between 34% and 45% utilization, sitting idle waiting for it.

In RAID-5 (and in SHR), a sequential read spans the whole stripe, and the stripe only completes when the slowest member answers. One slow drive caps the whole array: because of it, the array couldn’t go past about 200 MB/s while six good drives had room to spare. And its SMART was clean, zero reallocated sectors. Consumer drives usually ship without time-limited error recovery (ERC/TLER) active, so the drive retries a marginal sector for seconds, internally, without accounting for anything. The Synology’s NVMe cache diverted part of the reads from the platters and helped mask the symptom.

The drive that fails and the drive that slows you down don’t have to be the same, and SMART “PASSED” says nothing about latency. A slow array is diagnosed with per-drive utilization and response time, not with SMART.

Parallelizing rsync is science, not guesswork

First instinct: more streams. Wrong, twice.

The first time, a bug of ours: a “clean” job relaunch didn’t kill anything, because the Synology’s busybox ps truncates the command line and the verification grep didn’t find the old processes. Result: 32 rsyncs running into 7 destinations, four to five writers per tree, the QNAP with its receive buffers clogged, and the cable transmitting 516 MB/s while the array read 140. After the cleanup (enumerating /proc/*/cmdline, never trusting ps again), useful throughput rose from 140 to 181 MB/s while total load collapsed.

The second time, measurement inverted common sense: with large-file trees, fewer streams were faster, because the average size of each read request tripled and the array did long sequential runs instead of jumping around.

But small-file trees are another sport. TERACHAD/Emulators has 21 TB in 3.4 million files averaging 6 MB: latency-bound work (stat, creation, metadata), not bandwidth-bound. And there aggressive parallelization worked, with one detail: the right shard is chosen by file count, not by size. A single directory, ROMS_FINAL/xbox-hdd-ready, held 995 thousand of ROMS_FINAL’s 1.59 million files, averaging 1.7 MB per file. Isolating it from the multi-gigabyte ISOs:

Between 2.8 and 3.2 times the single stream’s average throughput, on the same machine, on the same network. And there’s a beautiful reason: with the array missing a member, reads of data that doesn’t live on drive 2 don’t need it, so the extra concurrency let the other drives work while it choked.

“More parallelism” and “less parallelism” are both wrong answers as a general rule. Bandwidth-bound work splits little; latency-bound work splits a lot, and by file count. Measure before choosing.

Copying is half the work; proving is the other half

Every job that finished went through a dry-run of the same rsync, with the same excludes: zero files needing transfer means an identical destination by size and date. And even so it almost went wrong, because the verification inherited the copy’s excludes: a tree excluded from the transfer stayed excluded from its own proof. Re-running the check without excludes turned up 952 files and 191 GB missing in Downloads (exactly the batches I had set aside as “only existing copy”) and the old restic repositories, which a similar directory name on the QNAP pretended to cover: the repositories’ internal ID proved they were different ones, not copies.

There was source drift too: a tree verified as complete received 16 GB of new checkpoints on the Synology after the check, and only the final re-verification caught it. Verification has an expiration date.

And a conscious decision, recorded so nobody accuses me of hiding it: the old backup histories (about 2 TB of restic snapshots and a duplicity set from 2025) were left behind by my choice, because the new repositories on the QNAP already cover the same content from now on. Deliberate loss of history, not of data.

Closing the books: 17 rsync jobs, all exit 0, all verified, about 5.9 million byte-identical files, and the Synology shutdown only happened after every tree was proven.

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Meanwhile, in the homelab

On the other side of the house, the marvin server, which mounts the NAS over NFS and runs the 57 containers, almost died standing up. Dead NAS plus “hard” NFS mounts plus 57 containers restarting on their own made an infinite queue of uninterruptible I/O: the machine answered ping, but sshd couldn’t complete a handshake. And every insistent SSH attempt only made it worse, because OpenSSH punishes a repetitive source by extending the ban. We knocked on the door and the door added a lock.

The way out was to stop knocking: I paused the torrents through the qBittorrent API (which kept responding), went to the physical console, disabled Docker, rebooted, and the server came back. A watchdog I didn’t even remember existed had, on its own, taken down the 14 NAS-dependent containers before any database corrupted: bitcoind’s LevelDB opened clean on the first try.

The migration itself was elegant: fstab pointed at the QNAP and the old mount points became symlinks into the new one. No docker-compose changed. Then came the QNAP NFS gotcha: permission is per client. The desktop was allowed without squash, but marvin’s writes came in as uid 1001, blocking everything that writes to the NAS. The first fix attempt still flipped the mount to read-only in the middle of the first restic backup, which survived on retries. Final configuration: read and write, no squash for anyone, tested with touch and ls -ln from each client, because “it worked on the desktop” proves nothing about the server.

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After that it was resurrection tree by tree: each service only came up when its tree was verified on the QNAP. In two days, 57 of 57 containers back, and a brand-new restic backup running daily to the QNAP.

The new kernel took down the NVMe

In the middle of the recovery, a free scare from the server. Seven minutes after the nightly backup started, the NVMe controller vanished silently: every binary took SIGBUS, Docker and sshd died, and nothing remained recorded anywhere, because the log lives on the device that died. Not even a kernel panic: just silence.

The same workload had run fine hours earlier on kernel 7.2.6 and died on 7.2.9. Disabling APST (the NVMe’s power management) didn’t fix it: a second identical crash, a 7.2.9 regression confirmed by elimination. MicroOS is transactional and snapshot-based, so the rollback was a reboot into the 7.2.6 lineage, not a reinstall. Automatic updates were deliberately left broken on that lineage so nobody goes back to 7.2.9 unsupervised, and testing 7.2.8 waits until I can sit next to it.

A transactional operating system turns a kernel regression into a reboot. Without system snapshots, that night would have cost a lot more.

The thick provisioning scare

On the QNAP, the whole copy ran with no snapshots at all: in a bulk migration, snapshots are dead weight. With the copy done, I went to enable the ZFS snapshot schedule and got a dry error: “pool free space is not enough”. With 20 TB free.

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Both statements were true about different things. The CHAD folder had been created as thick: out of the 100 TB quota, ZFS reserves the full 100 TB, and to snapshot a dataset with a reservation, the pool has to guarantee space to overwrite every referenced block while the snapshot pins the old copies. The math: 80 TB written plus 100 TB reserved, against 126 TB usable in the pool. It doesn’t close. And df lies on both sides, because it measures the folder against the quota, not against the pool.

The fix was converting the folder to thin, which QuTS hero does in-place, hidden on the folder’s Management screen, not in the edit dialog where “Thick” looks like static text. In thin, the number becomes a ceiling instead of a reservation: the 10 TB reservation evaporated, the pool ended up with 39 TB free, and the first snapshot came out consuming 334 KB over 79.9 TB referenced. ZFS needed almost nothing; the reservation was what demanded a 100 TB guarantee to authorize.

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Now the QNAP has a snapshot schedule with a retention policy, as it should have had since day one:

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Two footnotes for anyone building one of these. If your thin folder has an 80% alert, raise the quota for aesthetic slack, because the alert measures against the quota and not against the pool. And this QNAP’s real bottleneck is RAM: 7 GB for a 174 TB pool. QuTS hero’s official minimum is 8 GB, and sensible practice at this scale is 16 and up. The size of the ARC cache governs metadata speed, and that’s why the whole-tree sweeps during the migration were slow. A RAM upgrade is worth more than any fine-tuning.

The Synology is born again

With everything verified on the QNAP, the Synology could finally be operated on. I swapped drives 2 and 8 for new IronWolf Pros, deleted the wounded pool and recreated it from scratch, this time in SHR-2, the two-parity-drive mode, their RAID-6. The new pool came out with 100 TB, all drives healthy:

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It goes back to work as a replica target, and no longer as a single point of failure. The QNAP stays as primary, in RAIDZ2, with 87 TB of the 126 TB used and snapshots scheduled:

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What I take from the second half

1. Distrust silent success. HBS reported green while leaving 901 GB behind. A backup is verified against a source inventory.
2. SMART doesn’t measure slowness. The drive that nearly killed the evacuation was “healthy.” Per-drive utilization and latency tell the truth.
3. Parallelism is calibrated by measuring. Bandwidth-bound splits little, latency-bound splits a lot, and the shard is chosen by file count.
4. Verification doesn’t inherit excludes. If the proof uses the same filters as the copy, it only reaffirms the copy’s assumptions. And verify again immediately before destroying the source.
5. NFS permission is per client. Test writes from every machine that will use it.
6. Don’t hammer a dying sshd. Each attempt extends the ban. One patient attempt, or the physical console.
7. The dangerous repair comes last. The array stayed degraded and slow until the last byte was proven somewhere else; only then did rebuilding stop costing anything.

I came out of this better protected than I went in: two NASes, each with its own RAID-6, scheduled snapshots on the QNAP, daily restic, and Glacier for what’s critical. The disaster cost me a QNAP, two drives and a week. It could have cost 90 TB.

TL;DR for those who only want to know which one to use: Jev is a hosted classifier, good and cheap for repeated decisions over text, and not a silver bullet. If you want the direct comparison, skip to Clef, Cloudflare’s open alternative with weights and image input; to Laya, the small encoder to run locally and fine-tune on your task; to the public ranking, with 70 models side by side; or straight to how to decide in your project.

When Jev showed up, I didn’t think much of it. The homepage was a festival of jargon, Kahneman here, Jevons there, a “193x faster” with no benchmark next to it, and my rule for new AI tools is not to adopt any intermediate layer until it hurts not to have it, so I didn’t spend time on it.

In that post I concluded that, marketing aside, Jev is a hosted classifier: you send a text and closed-answer questions, it returns the option and the probability, without generating any prose. And that the “193x” only impresses because the comparison is against a chat LLM writing a paragraph so you can fish a label out of it afterwards.

What I hadn’t registered at the time is the size of the habit Jev exposes. A lot of people were using a frontier LLM as an expensive stand-in for an ordinary classifier, and had never thought about a classifier, because they never had to: the LLM solved it, badly and expensively, and nobody measured.

For me, a classifier would be the first idea, which is why Jev didn’t look like news. For many people it was a revelation. That gap is the subject of this post.

This post is a collaboration

Much of what you’ll read here is by Paulo Câmara, neuroscientist, co-founder and CTO of LUA Vision, the Brazilian company whose model I tested and investigated in recent weeks. He wrote an essay, “The student who marks answer A,” to be published here, in which he read Jev’s fine print, opened the roughly 350 test repositories that appeared on GitHub in launch week, and recomputed the four main results from scratch.

The warning Paulo gives at the end applies: he founded a company that builds its own model and competes in the same market. Read with that discount, and check the numbers at the source, all of which he links.

First: what a classifier is

A classifier is a function that takes an input and returns one of a fixed set of categories, usually with a probability for each. This email is spam or not. This ticket is billing, support or sales. This transaction looks like fraud.

The list of possible answers is closed, and you know it before you run.

The classic way to build one is with labeled examples: a set of texts where someone has already given the right answer. On top of that you train a model, which can be something as simple as logistic regression or naive Bayes, or a small encoder like BERT with a few hundred million parameters. In every case, the final model is small, runs locally, answers in milliseconds, costs almost nothing per call, and returns numbers you can calibrate and audit against your own data. This has existed for decades and is undergraduate material.

So when someone with a computer science background sees a problem like “decide which bucket this text falls into, thousands of times a day,” the first question is “how many labeled examples do I have?”, not “which LLM do I use?”.

A chat LLM is a text generator with billions of parameters. Using it to return a label means paying per output token, waiting seconds per answer, dealing with a format that sometimes breaks, and accepting behavior that changes when the vendor updates the model. It can be done, and zero-shot classification with language models has been documented since 2019. But it’s the expensive, slow option, and it only pays off when you have no examples at all or when the categories change with every request.

Jev lands exactly in that space. It’s a classifier that accepts categories defined on the fly, several questions about the same text in a single call, hosted, cheap per token and fast.

Compared with a chat LLM generating a paragraph, it’s an engineering win. Compared with a classifier trained for a stable task, it’s a convenience that trades control for flexibility. Keep those two yardsticks in mind, because the rest of the post goes back and forth between them.

Paulo’s essay: “The student who marks answer A”

What follows is Paulo Câmara’s essay, written for this post. The subheadings are his.

Every week an AI savior shows up. This week’s is called Jev.

It launched on September 15, with US$ 40 million in the bank, a US$ 200 million valuation and a promise that fits on a billboard: two hundred times faster, four hundred times cheaper, calibrated and incapable of hallucinating. Within 24 hours, 13% of the paid teams on Vercel’s gateway were already using it. Within a week, there were more than 300 repositories on GitHub testing the thing.

I did what I do with every savior. I read the fine print, opened the roughly 350 repositories, and redid the math for the ones that published raw data. What I found fits in the image above: an answer sheet with answer A marked on every question.

When there is no information at all to decide on, Jev marks the first option. In 3,000 of 3,000 draws. With up to 92% certainty. Keep that image, because it explains the rest of the text.

What it is, without the billboard

Strip away the marketing and what’s left is a multiple-choice exam with no essay section. You send a text, ask closed-answer questions (choose among up to 255 options, give a score on a scale, say yes or no) and it returns the probability of each answer. It doesn’t write a line. It doesn’t explain. It doesn’t do math. It doesn’t call tools.

That’s good, and I want to start with what’s good. Many companies today use a frontier LLM as a luxury if statement: send the document, wait twenty seconds for the model to think out loud, pay for every word it writes, validate the JSON, and only then find out the ticket was about billing. Jev cuts that theater. It reads the text once, scores each option, done. That’s why output is free and ten questions cost almost the same as one.

The mechanism, though, isn’t new. Reading the score of each possible answer is how language models have been evaluated on multiple choice since GPT-2. It’s how the reward models of RLHF work, a technique TypeSafe’s founder helped create at OpenAI.

A few days after launch there was already an open copy imitating the interface with a public model, and ten days later Stanford and Nvidia released an open-weights competitor. TypeSafe published no paper, no weights, no model size and no result on a public benchmark. In the announcement’s FAQ, “Is Jev just a smaller LLM?” appears as a question. The answer is one line: “it is neither small nor an LLM.” That’s it.

One opponent for each headline

The homepage says “193.6× faster, 444.6× cheaper.” I redid the math with the table the company itself published. The 444.6 is the cost of Opus 5, the most expensive model on the panel, divided by Jev’s cost. The 193.6 is Sonnet 5’s time divided by Jev’s time. One opponent for each headline.

It’s like advertising that your car is more fuel-efficient than a Ferrari and faster than a truck, and letting the reader conclude it’s the best car in the world.

How many times cheaper Jev is, depending on who is on the other side. Source: TypeSafe’s public table (evals.typesafe.ai) and independent tests against non-reasoning LLMs.

Against the cheap model that scores about the same, Luna, Jev is 8 times cheaper. And that’s with every LLM in the comparison thinking before answering, something nobody does to route a ticket. Against a non-reasoning LLM, the independent tests measure about 4 times on cost and 2 to 7 on time. Still a lot. But an order of magnitude less than the billboard.

There’s a detail almost nobody read. There is no answer key in that evaluation. “Accuracy” is how much each model agrees with the average of two frontier models. Jev agrees 67.8% of the time, the same as Sonnet 5. Opus 5 is at 73.1%.

Agreement with the reference versus cost per case, average of TypeSafe’s four workflows. The green band runs from 66.8% to 67.9%: Luna, Sonnet 5, Terra and Jev.

Breaking it down by task shows what the average hides. On invoices, which require checking amount, quantity and date, Jev is 17 points behind the best model (61.8% versus 79.1%) and below even Luna. Its documentation warns that it “is not a calculator.” At least that is true.

Agreement by workflow. Source: evals.typesafe.ai.

And the most solid data on TypeSafe’s site isn’t even about Jev. All eight LLMs evaluated score higher when the task is broken into small questions and the logic stays in code. Haiku 4.5 goes from 18.1% to 53.6%. The most useful lesson of the launch is an engineering one, and it applies to any model.

What the price gives away

Price is technical data in disguise. At US$ 0.042 per million tokens, a rented GPU needs to read 13 to 20 thousand tokens per second just to break even. In a scenario calculation, that squares with a model of 6 to 37 billion active parameters, which is a small model, or with a subsidy. TypeSafe wrote, with a candor I respect, that “there is no way to prove it isn’t subsidized.”

Since launch, the per-account usage limit dropped from 250 thousand to 100 thousand tokens per second. The documentation warns that it changes “without notice” until the “large GPU deals” land, and the subprocessor list shows inference running on an on-demand rented GPU cloud. It’s a frontier model with startup capacity.

In the same calculation, each decision costs about 14 joules. That’s Jev’s best argument, and I’ve defended that yardstick for a long time: accuracy per joule, not just accuracy. There, it’s genuinely good.

Exam Portuguese

I found only one test of Jev in Portuguese: ENEM 2025. I redid the math from the raw answers. Jev got 103 right against 102 for an open 120-billion-parameter model. A statistical tie (p = 1.0).

Jev’s accuracy on ENEM 2025 by subject area, 182 valid questions, with 95% margins. Recomputed from the patryckalves/jev-no-enem repository.

Jev reads exam Portuguese better than many prep courses: 78% in Languages. And does Math like someone guessing: 25%, with a guess worth 20%. In Spanish, another audit measured a loss of 3 to 6 points relative to English and 17% to 38% more tokens for the same text. It’s the token tax I measured in my paper, charged again, now by another vendor.

One detail in its favor: the explicit “invalid” option in that experiment separated out the hardest questions. That’s the abstention route worth demanding in production, measuring alongside it how much it leaves unanswered.

The student who marks answer A

Back to the answer sheet.

An auditor asked Jev to predict a fair die it had no way of seeing. The honest answer is 1/6 for each face. I downloaded the 1,000 answers and counted. In every one, Jev chose the first option on the list it received: “1” on the die, “red” on the color die, “heads” on the coin, “north” on the spinner. With 76% to 92% certainty.

A second researcher repeated the same question 2,000 times, independently. Same result. The caveat: this holds for choice questions; in the yes-or-no-with-probability formulation, the same repository shows Jev closer to the reference, still overestimating low probabilities.

When it doesn’t know, it doesn’t say it doesn’t know. It marks answer A, with conviction.

Stated probability versus actual frequency. Draws: 1,000 answers recomputed (KantaHayashiAI). Heart risk: 5,000 people from BRFSS, recomputed (rubinagentagi-tech). Items with human disagreement: pre-registered study with ChaosNLI (GautamTalksDev). ENEM: recomputed (patryckalves).

The chart shows the whole pattern. Where it has signal, as in ENEM answers above 0.9, what it says and what happens coincide: 97.1% confidence, 97.6% accuracy. Where it doesn’t, the certainty stays and reality leaves.

In a test with 5,000 real people, the heart disease rate was 9%. Jev assigned, on average, 27%. I recomputed: its probability is worse than simply saying 9% for everyone.

System 1, literally

TypeSafe calls Jev a “System One model,” a nod to Kahneman’s fast thinking. It’s the most honest part of the product, just not for the reason the company would like. In the book, System 1 is fast, cheap and automatic. It’s also where overconfidence lives: it builds the most coherent story out of what’s in front of it and doesn’t register what’s missing.

Kahneman spent years disagreeing with Gary Klein about when to trust expert intuition. In 2009 the two published a joint paper with the most elegant title in psychology: “a failure to disagree.” The conclusion fits in one line. Intuition only deserves trust in a high-validity environment, where there is regularity and there has been feedback to learn it from. Outside that, confidence stays high and stops saying anything about accuracy.

That’s Jev, exactly. Classifying English text is a high-validity environment, and there it does well. Predicting risk, drawing lots, guessing the company rule nobody told it about: a low-validity environment. There it stays confident.

In the lab, we separate two things: deciding, and knowing whether you decided well. The second is called metacognition.

It’s measured with signal detection theory, Maniscalco and Lau’s meta-d′, and it depends in part on prefrontal regions distinct from the ones that support the decision. Fleming and colleagues found that in the anatomy in 2010, and Rounis and colleagues, the same year, showed that magnetic stimulation of the prefrontal cortex knocks down metacognition without touching accuracy (a result whose replication is disputed, by Bor and colleagues in 2017). That’s why there are people who get it right without knowing they did, and people who get it wrong with conviction to spare.

The field the company calls confidence is a formula over the same distribution that produced the answer: in a two-option choice, 2p − 1. That isn’t a defect in itself. An ideal observer does exactly that and gets it right.

The problem is that Jev’s distribution loses calibration outside what it knows, and nothing in the system checks the answer by another route. When the signal disappears, the answer becomes wrong and the confidence stays the same, because the two are the same number. In a pre-registered study with items on which a hundred people disagree, human agreement is 47% and Jev carries on at 81% confidence. When the coordinates of a spatial task were shuffled, accuracy collapsed to 5% to 22% and confidence dropped by 0.028.

A model that says 83% about a die it never saw isn’t calibrated. It’s assertive.

Attention gets captured

I studied attention in my doctorate, and I expected to see capture: the loudest stimulus winning. That’s not what the tests showed. The shout, the famous “ignore your instructions,” barely gets through. What gets through is the plausible sentence with no checked origin. Psychology has a name for this: source monitoring failure. The system treats a claim nobody verified as if it were fact.

Decisions flipped by attack type. Crude attack: 1 in 1,056 (cwhy). Injected command and unverified opinion: 9,744 variants (Hu et al., 2026). Optimized fluent context: 312 of 508 correct decisions (Xu, 2026).

In numbers: the crude attack gets through 1 in 1,056. A single unverified opinion pasted at the end of the text flipped 12.1% of decisions, statistically tied with the strongest injected command. A line saying “the closing administrator has confirmed this article was kept” dropped accuracy from 96.5% to 26.5%.

A fake field saying the user had pre-approved rm -rf ~/.ssh lowered the chance of blocking from 0.76 to 0.48. It recalls Loftus’s misinformation effect: suggested information rewrites the judgment.

The capture also applies to cues that shouldn’t count. With the same text, saying only “the writer is Black” or “the writer is white” changed the death-penalty rate Jev assigned from 29.5% to 62.7%. The direction is beside the point. A life-or-death decision moved 33 points because of information that had nothing to do with the case.

There’s one more I can’t let pass. Changing only the option labels, from “0/1” to “no/yes,” without changing any definition, flipped 70 out of every 100 answers. The type error rate stayed at zero. The format was perfect. The decision was backwards.

These numbers come from preprints and small studies. The exact percentage changes with prompt, task and model version; the class of failure does not.

Probability that doesn’t add up

Probability has a first-semester rule: the chance of something plus the chance of its opposite equals 1. In TypeSafe’s own documentation, “is the customer asking for a refund?” gets 0.72, and “is the customer asking for something other than a refund?” gets 0.47. Sum: 1.19.

The pair isn’t an exact complement, and TypeSafe doesn’t promise that two separate questions form a joint distribution. But it’s the example the company itself chose to show off the product, and it doesn’t add up. The fix is a design one: a single choice question with mutually exclusive options, instead of two yes-or-nos added together.

People get probability wrong in many ways, but on this one they tend to get it right. When the question comes as a pair, the chance of something and of its opposite sum close to 1. Tversky and Koehler showed that in 1994. Jev gets wrong what people get right. The same decision changes value depending on the wording of the question, and that is the opposite of “epistemically honest probabilities.”

“It doesn’t hallucinate”

The announcement says Jev “cannot hallucinate” and shows 0% type errors. The zero is true and irrelevant. Since it can only answer within the options you gave it, getting the format wrong is impossible. Calling that not hallucinating is like praising the answer-A student because he never leaves a question blank. True. So what?

In a model that chooses, hallucinating is choosing wrong with conviction. On fact-checking, Jev ties GPT-6 Astra on accuracy but calls an unsupported claim “supported” 23% of the time, against 14% for Astra. In 76% of the situations where there was no tool at all, it chose to call a tool. With a company rule it wasn’t told about, it got 19 of 24 decisions wrong, 15 of them with more than 90% certainty. Faced with an invented law, it abstained zero times.

Security

First, where your data ends up: the United States, AWS and a GPU cloud called Modal. Retention lasts “for as long as reasonably necessary,” with no deadline, and zero retention only exists on the enterprise plan.

The data agreement cites the European Union’s standard contractual clauses, the UK addendum and California law, and doesn’t have a word about LGPD. If you send Brazilian citizens’ data, the legal basis for the international transfer is on you, and that’s a question for a lawyer before signing. The promise not to train on what you send exists and is good. SOC 2 exists and only appears on request.

As an attack detector, Jev is good, with fewer false alarms than many LLMs: it caught 40 of 41 injections in a test with real pages. As a judge of content that carries the attack inside it, it gives way, as I showed above. The difference looks subtle and decides everything. In my GV Angels talk I said an agent is not an employee, it’s an access key that talks. A gate made only of Jev is a lock that opens when someone writes on the note that the owner authorized it.

The error that worries me isn’t the error with doubt. That one goes to review. It’s the error with 92% certainty, which crosses any threshold you configure.

The wave

A word about the evidence, because I used a lot of it.

Jev test repositories created per day on GitHub, from the metadata of 346 indexed repositories.

In one week about 350 repositories testing Jev were born. I classified all of them by what they let you check. There are 89 with raw data, code, pinned version and statistics, and 170 with data and code; the rest offer less than that.

Only 11 wrote the hypothesis down before running the test. The median is one GitHub star, which means almost nobody looked. And the wave of tests followed the wave of hype: peak on September 21, almost nothing after the 23rd.

That’s why I didn’t accept numbers on authority. The ones that support this text I checked at the source, and the four main ones I recomputed from scratch.

Verdict

True:

• Fast: 100 to 400 ms.
• Cheap: 45 to 323 times less than a frontier LLM on the same decision.
• Ties the top on narrow judgments, like checking whether a passage supports a claim.
• Reliable above 0.9 on data similar to what it has already seen, after you’ve checked that on yours.
• Good injection detector.

Sandcastle:

• 193.6× and 444.6×.
• “Frontier intelligence.”
• “Doesn’t hallucinate.”
• Calibration as a general rule.
• Safe as the sole guard of an agent.
• New architecture: nobody has seen it.

Jev is nobody’s savior. It also isn’t “just a classifier.” It’s a good piece of product engineering that wraps known technique in an interface automation needed, at a price that changes what’s viable. The product is new. What it claims is new, so far, is marketing: it calls format truth, task calibration honesty, and System 1 a virtue.

What will break first

• The agent gates built only of Jev, because the attack that breaks them is already published and costs cents.
• The systems that trust the factory threshold on new data, in another language or with an internal rule, because they’ll automate errors at 90% certainty.
• Whoever uses its probability as real risk, in credit, health or forecasting, because it inflates rare events.
• And the price advantage, because within ten days there was already an open copy and a faster competitor.

If you’re going to use it anyway

• Pin the version. The jev-latest alias swaps models on its own, and your calibration goes with it.
• Every question with a “none of the above.” Without it, it marks answer A.
• Shuffle the order and use neutral names for the options. Test whether the decision changes.
• Recalibrate with 50 to 300 of your own examples before choosing a threshold. In one test, calibration error dropped from 0.117 to 0.008.
• Mark everything that comes from outside as untrusted: email, page, tool output.
• Never let it authorize a destructive, financial or personal-data action on its own.
• Math, dates and business rules stay in code.
• Before signing, compare with the cheap baseline: an open model reading probabilities, or a trained classifier if the task is always the same.

Read with a discount

I founded a company that builds its own model and competes in the same market. Read with that discount. That’s exactly why every number here has an open source: you don’t need to trust me, you need to check.

And if someone sells you Jev as a savior, ask the question I ask every startup: which test, and from when? If the answer takes a while, that’s the answer.

Essay sources

TypeSafe. Jev announcement · models and limits · confidence · known failures of jev-1.13 · workflow evaluations · adapter for LLMs · privacy · data processing · trust center

Recomputed by Paulo from raw data. ENEM 2025 · blind draw · heart risk · fact-checking

Preprints. Hu et al., JevAdvBench · Xu, JevOut · Sun et al., Type-Safe Is Not Error-Free · Ibrahim and Zaki · Li et al., JEV-as-a-Judge

Independent tests. human disagreement (ChaosNLI) · die replication · dialect bias · 49 tasks against a non-reasoning LLM · calibration and negation · recalibration · injection in Wikipedia discussions · Spanish · tools · undisclosed internal rule · shuffled coordinates · crude attacks · injection detection · audit index

Press. VentureBeat, agent gates · VentureBeat, CLM-8B

Literature. Fleming et al. (2010), Science 329 · Rounis et al. (2010), Cognitive Neuroscience 1 · Bor et al. (2017), PLoS ONE 12 · Johnson, Hashtroudi and Lindsay (1993), Psychological Bulletin 114 · Kahneman (2011), Thinking, Fast and Slow · Kahneman and Klein (2009), American Psychologist 64 · Loftus, Miller and Burns (1978), J. Experimental Psychology: Human Learning and Memory 4 · Maniscalco and Lau (2012), Consciousness and Cognition 21 · Ouyang et al. (2022), NeurIPS · Radford et al. (2019), OpenAI · Tversky and Koehler (1994), Psychological Review 101 · Câmara (2026), Inherited Weights, doi:10.5281/zenodo.22899767

The open alternatives: Clef and Laya

Since Jev’s launch, the category has gained open competition, and that changes the conversation about “new architecture.” Two names matter.

Clef, from Cloudflare

On October 1, Cloudflare released Clef and Clef-flash: the same abstraction as Jev (one shared state, several typed questions, a distribution over the options), with a compatible API, open weights under Apache 2.0 and image input. The architecture is described: a frozen Qwen as base (27B in Clef, 9B in Clef-flash), LoRA adapters and a head that scores each allowed option directly, trained with classification and calibration objectives. In other words, the lineage Paulo pointed to, now with inspectable code.

That doesn’t prove TypeSafe uses the same thing, but it proves the product can be reproduced with known components. Cloudflare ran the Decision Index, an open, independent suite with dozens of classification, routing, retrieval and safety tasks, and published the table. Some points:

Reading the whole table: Clef comes out stronger on most tasks, Jev still wins some, and it’s an internal Cloudflare run, on a public suite that can be trained against. Hosted price is the other side: US$ 0.24 per million tokens for Clef and US$ 0.09 for Clef-flash, against US$ 0.042 for Jev. Jev remains the cheapest hosted service of the three, by a margin of 2 to 6 times, and remains text-only.

Laya (and Arbiter)

Laya attacks the problem from the other end. They’re small encoders, 322 to 421 million parameters (ModernBERT-large in the typed version), with a choice head, Apache 2.0, built to run locally and be fine-tuned for a recurring task. Arbiter packages them behind a Jev-compatible API, with batching and deployment on Mac and NVIDIA. Arbiter is only a serving layer.

Laya’s README is unusually explicit about its limits, and I’d rather repeat than soften:

• the base checkpoints score near chance on the project’s own typed-decision benchmark; the headline 0.766 accuracy comes from a checkpoint fine-tuned on that benchmark’s training split;
• the models ship overconfident, and calibration has to be redone in the deployment domain;
• the typed version’s context is 1,024 tokens, fine for tickets and short messages, insufficient for long documents without chunking;
• with more than 50 options, the classification head saturates, and Laya itself reports Jev doing better.

None of this is a defect in the idea. It’s applied machine learning the way it has always been: a small model is excellent when the task is stable and labeled examples exist, and broad zero-shot behavior demands a much larger pretrained model. Laya is the option when privacy, offline operation, predictable latency or fine-tuning in your domain matter more than asking arbitrary questions about arbitrary documents. It is, at bottom, the classic classifier from the start of this post, with a modern interface.

There’s also Kev-9B, another frozen Qwen 9B with LoRA and a decision head, Apache 2.0, that fits on a 24 GB GPU. Three open implementations in under three weeks say the same thing: typed decision models have become an implementation pattern.

The Decision Index: how to measure this yourself

All this discussion about “who’s better” only makes sense with a public yardstick, and one exists. The Decision Index is an open suite, unaffiliated with TypeSafe, built for typed decision models: state plus choice or yes-or-no questions, one probability per option.

The current version, 0.2.1, counts 38 benchmarks in five areas (tools and automation, retrieval and classification, knowledge and reasoning, language understanding, human taste), about 120 thousand requests in total, with two rules that matter: the score is chance-corrected (0 is random guessing, 100 is perfect) and an unanswered request counts as wrong. No truncating input, no filtering options, no tuning the prompt per benchmark.

The live board has 70 models plus Jev as the reference. I downloaded the board’s data file and rebuilt the table; below are the top and the names that appeared in this post. Latency is the median measured by the maintainers on an RTX PRO 6000, one request at a time; Jev’s is a hosted API, so it doesn’t compare with the others.

“Open weights” means a checkpoint published on Hugging Face; “open code over an open model” is a published inference technique on top of an off-the-shelf Qwen or Gemma, without its own checkpoint. The board doesn’t record each one’s license, so check before using in a product.

Three readings come straight off the table:

1. The only closed model on it is Jev. It leads, but by half a point over an open fine-tune of a 26-billion-parameter Gemma 4, and the board treats differences under 0.25 as ties. The top ten are all open models of 12 to 32 billion parameters with an adapter on top, exactly the recipe Clef describes.
2. The small zero-shot encoder sits at the bottom: Laya at 6, Lavoir at 9. That matches what Laya’s README admits and what I said about the classic classifier, which needs labeled examples to shine.
3. A 4-billion LoRA like Hopper delivers 41 points at 23 milliseconds, a range many real problems don’t need to exceed.

And how to read Laya at the bottom of the table? The Decision Index measures one thing only: answering, with no extra training, 38 kinds of question the model has never seen, from chess to taxation. That’s what a hosted service has to do, because it doesn’t know which question is coming. A 400-million-parameter encoder will never win that exam, and Laya doesn’t even try: the score of 6 is the base checkpoint, with no fine-tuning at all.

The same model fine-tuned with your examples, on a stable task, shows up at 0.77 on the project’s own benchmark. The ranking answers “who is the best zero-shot generalist,” which is the question of whoever sells an API. The normal programmer’s question is a different one: “on this repeated decision, with the examples I have, what gets more right at the cost I accept?”. For that one, the bottom of the table may be the right answer, and no public board replaces the test on your own data.

So is Laya useless next to the others? As a zero-shot generalist, yes, and the board is right to say so. As a programmer’s tool, the math is different. It’s the only model on that table that runs on a laptop CPU and answers in 6 milliseconds, with 400 million parameters that fit on any server you already have; Hopper and Kev want a GPU, and Jev wants internet, an API key and a data agreement.

Fine-tuned with your examples, on a taxonomy that doesn’t change every week, Laya’s own author reports the model beating Jev on some tasks and losing where there are many options, in third-party published tests with different prompts and samples, so indicative rather than controlled. In other words: useless for answering any question about any text, and very useful for answering the same question a thousand times a day, for free, without sending data outside.

Clef isn’t on the board: 0.2.1 closed on September 27, and Cloudflare released the model on the 1st. The numbers I showed in the previous section are from an internal Cloudflare run on the same tasks, per benchmark, without the aggregate index. There’s also already a vendor claiming to have passed Jev on the same scorer, but for now that’s a self-reported number in a blog post, not an entry on the board. Worth waiting for the official inclusion before comparing.

Running it yourself is a day’s work, and the kit handles the tedious part:

1. pip install -e ".[transformers,rebuild]" and python -m decision_index suite rebuild. The suite isn’t redistributed because of licensing, so the kit downloads the sources (about 7 GB, with HLE requiring you to accept the terms on Hugging Face) and rebuilds the files with hashes verified against the lab’s.
2. python -m decision_index suite sample --n 100 for a quick test, then pipeline --engine http --option base_url=... --option model=... against any server that speaks the POST /v1/systemone format. That covers Jev itself, with the API key, and Laya behind Arbiter. Clef’s API is announced as compatible, so in theory it goes in by the same route, but I haven’t tested it.
3. score applies the same math as the board, and the maintainers state that it reproduces all 67 entrants of the current edition. Whoever has cloud GPUs runs the whole thing as a single Hugging Face job on an RTX PRO 6000 with hf-job.

I didn’t run the full suite for this post; the table above is the board’s file rebuilt, not a measurement of mine. Two caveats from someone who uses this kind of yardstick: the suite is public and frozen, so it can be trained against, and the board itself says Jev’s API output has already changed in 20 choices of the latency sample since the original run, without changing the score. A public benchmark is for discarding weak models and finding candidates. The final decision is still your labeled set, as I explain next.

How a programmer should think about this

The useful question is no longer “is Jev new?”. It has become: for a specific, measured decision in your system, which combination of quality, price, latency, privacy, context and maintenance beats what you already have, whether that’s a rule, a classifier or an LLM with structured output? No vendor benchmark answers that without your own labeled examples.

A Jev-type tool is useful when the same kind of judgment about text happens many times, the possible actions fit in a list, the surrounding code can apply exact rules, and a wrong answer can be detected or sent to review. That’s the shape:

• ticket triage;
• ranking a short list already retrieved;
• routing a request to a bigger or smaller model;
• flagging an agent trace for human review;
• choosing among spans that code or OCR already found.

Outside it, the tool is in the wrong place:

• when the input is image or audio;
• when the output needs prose or citations;
• when an exact parser already solves it;
• when a misclassification grants access or executes a destructive action.

I looked at my own portfolio of projects with that yardstick, and the result is a useful bucket of cold water: most need a specialist vision or audio model, deterministic rules or generated text, and have few decisions repeated, costly and ambiguous enough to justify one more model service. A few candidates remain, the clearest being model routing in FrankClaw and short-list ranking in ai-memory. None is a demonstrated bottleneck today. I suspect your portfolio looks similar.

If a bottleneck appears, the pilot is short:

1. Pick a single decision and define the allowed answers, including “don’t know” when that’s a real outcome.
2. Set aside real examples, with easy, ambiguous and adversarial cases, and label them independently. If your product speaks Portuguese, include Portuguese.
3. Compare what you already have, a simple rule or classifier, Jev pinned to a version, and a cheap LLM with structured output, all with the same input and the same decision policy.
4. Measure the confusion matrix, calibration by confidence band, cost per correctly handled case, end-to-end latency and review rate. A cheap router that sends hard work to a weak model costs more in total.
5. Deploy as a reversible feature, with logging, a conservative review route and a fallback. A security or authorization judgment is advisory, unless an exact policy enforces the action independently.

What Jev did well was remind the industry that classification exists. That’s worth the hype it got, and maybe a bit more.

Neither it nor its open cousins are a silver bullet. They’re one more tool, for a specific kind of task, that your code needs to fence in with rules, calibration and review. Whoever was already thinking about classifiers before September gained nothing new; they gained a convenient API. Whoever had never thought about it gained the most valuable part, which is the question: why were you asking a billion-parameter LLM to write a paragraph to tell you which bucket the text falls into?

Both caught every loud sabotage; House let through the two silent items almost every model lets through (#7b and #8), and Enterprise let through three (#7a, #7b and #9). House’s notional cost, about $460, was the most expensive in the whole table, and the cause was the absence of prompt caching in the API.

A lot has happened since then, and this post is the continuation. I’ll go in the order that matters: first the new numbers, then the question everyone asked me in comments and DMs (“isn’t this just a Qwen with a sticker?”), and only at the end the speculative part about what the company says it built.

The spoiler that justifies the post: after LUA fixed the API, House redid the benchmark at 95.5. In my table, that’s a tie with Claude Fable 5.1 and GPT 6 luna, and the run’s cost dropped 85%. I’m not saying it’s a Fable in every way; I’m saying that, in this test scenario, it performed like one. The details, and the noise caveats, come right below.

Before that, the backdrop that makes all of this strange: the industry consensus is that training a frontier model costs tens or hundreds of millions of dollars in GPU, and that, therefore, a small company with no billion-dollar funding round should not get anywhere near one. gpt-oss, OpenAI’s own open model, with the best-funded lab in the world behind it, could not complete my benchmark on the same harness Genesys PI ran on. The Brazilian model completed it twice per tier.

This post is organized around that tension. First, what the model did in experiments I control. Then, what I could and could not verify about how it exists.

New disclaimer

Since the first article, I’ve talked directly with two of LUA Vision’s four co-founders: Paulo Câmara, CTO, and Eronides Junior, CRO. Paulo is the technical lead, an FGV graduate with a PhD from Tel Aviv University, and his thesis is the stated origin of the model’s architecture.

Before LUA, according to his LinkedIn, his background is heavy corporate consulting, Oracle EBS and ERP implementations at large companies, and he maintains a series of essays on AI on LinkedIn. Eronides handles the commercial side; before LUA he was CRO of SoftwareOne Brasil, after going through marketing, services and operations at the same company.

I’ll repeat what I already said, and it counts double now that I’ve talked to them: I have no business relationship with LUA Vision. I’m not a partner, not an investor, I haven’t signed a contract, I haven’t signed an NDA, I’ve received nothing beyond the same free evaluation key from the previous test. Nothing in this post went through them before publishing. So far, I’m just a curious guy with an API in hand.

That last detail matters more than it seems. Since I signed no NDA, I never had access to any internal company data: no weights, no training recipe, no loss curves, no hardware bill. Everything below is black-box experimentation on the public API plus public information. Where I say “checked,” I measured it; where I say “they say,” it’s their word.

The new numbers: re-test after the fixes

After the first article, Paulo let me know he had fixed a few bugs in the API, the main one being precisely prompt caching, and asked me to run it again. I did. It’s a clean, independent round of both tiers, on the same 14-sabotage protocol, with the original round preserved intact for side-by-side comparison. A methodological detail: v4 is a single-run benchmark, so the vigilance score is noisy, and I’ll come back to that shortly.

Before running, I confirmed the cache was really there. A repeated-prefix probe on the endpoint returned cached_tokens: 4121 / 4124 on the second call. In the original test, that number was always zero.

To place it in the overall table: in the original round, House tied Grok 4.7 at 83.5. At 95.5, it would tie Claude Fable 5.1, Sakana Fugu Ultra v2 and GPT 6 luna, at rank 9 among 46 models. Enterprise, at 82.0, would tie DeepSeek V4 Pro (base). I say “would” because, as I explain below, I kept the original run as the official ranking entry.

And it’s worth making the yardstick explicit: I’m not saying Genesys PI is equivalent to Fable in every way. I’m saying that, in this particular test scenario, it performed like Fable. On a different workload, your results may vary.

What clearly and reproducibly improved:

• Cost dropped 85% to 87%. Token volume was the same, about 40 million on House and about 20 million on Enterprise, but repeated context is now billed at the cache rate instead of full input price. House goes from the most expensive run in the table to mid-cost.
• Reliability. Enterprise’s original run needed a second attempt because of that grep loop in the first sprint. In the re-test, it converged first try. No loops on either model.
• Test discipline. The original Enterprise wrote no automated tests at all. In the re-test, it wrote a real RSpec suite, with model, request and system tests.
• Earlier detection. Both caught the loud sabotages (#1 to #3) by sprint 3, and House caught everything from #1 to #6 by sprint 4, something the original runs partly left for the capstone.

What can’t be claimed is that the model got more vigilant. House went up 12 points, but Enterprise went down half a point in the same round. Since prompt caching has no way of affecting sabotage detection, two tiers of the same model moving in opposite directions is the picture of single-run variance.

House’s +12 is large enough to suggest that Paulo’s other fixes improved the model, but one run per model doesn’t confirm that. To separate signal from noise I’d need three or more runs per model, which I didn’t do.

To calibrate the size of that noise: this same week, GPT 5.5, which had 100.0 in the table, ran again with identical model and harness and closed at 91.0. Nine points of difference without changing anything. So treat vigilance scores as a range.

And the two silent survivors are still there. In House’s re-test, the dropped index with no test guard (#7b) and the hidden aggregate (#8) were only fixed at the reveal; in Enterprise’s, not even at the reveal. It’s the same disguise blind spot most of the table has, frontier models included.

Keep in mind: the caching bug is fixed and verified, and the cost reduction is real. The vigilance score stayed within noise: Enterprise flat, House up but without statistical confirmation. In the overall table, I kept the original runs as the official entries, precisely so as not to reward a single run.

Pricing: not final yet, and it won’t be per token

The cost figures above are still notional, computed on top of the example price list the API exposes, and LUA itself already said, in the previous article, that those are not market prices. Talking to Eronides, he laid out what’s coming: they don’t intend to charge per token, like everyone else does, and the product isn’t meant to be B2C like the subscription chats. The bet is B2B, by usage license, with the model running on the customer’s own infrastructure.

In practice, that means a company with a Grok-class model, capable of advanced tasks like programming, running on-premise, off the cloud, with the guarantee that no sensitive data leaves the building. Frontier models today are cloud services from foreign companies, and regulated sectors live uneasily with that. If LUA can deliver exactly that package, and the “if” is still big, it changes the game for a lot of Brazilian companies.

Even with the caveat that the per-token price is only an example, the notional cost next to the table helps calibrate. The cheapest complete runs in the entire field are DeepSeek V4 Flash ($0.97) and MiMo V2.5 Pro ($1.03); Enterprise, at ~$2.40, sits in that neighborhood. On the other side, Opus 5.5 cost $16.63, Sonnet 5 about $27 and GPT 5.5 $34.69. And House, which in the original round was the single most expensive run in the whole field, is now mid-pack.

The pricing policy, according to them, comes out on October 7, 2026.

Extra experiment: Genesys PI auditing ai-jail

A sabotage benchmark measures vigilance inside a small, controlled app. I wanted to see the model on a real problem, so I gave it my ai-jail, the operating-system sandbox that runs coding agents inside bubblewrap, Landlock and seccomp on Linux and sandbox-exec on macOS. It’s a Rust project, with 776 unit tests, 70 dependency crates and a security surface I know well.

The task: a full security audit of version 2.3.0, at commit f62d576, read-only, with a threat model, line-level code evidence and reproduction where possible. Anyone who wants to check can open the repository at that commit and follow the reports’ file and line references.

For a yardstick, I ran a second independent audit, with another model, with no access to Genesys’s report until its own candidate list was fixed. Then I cross-checked the two reports and also compared them against two security advisories that were open on GitHub, written by outsiders against earlier versions of the project.

Genesys PI’s report (I’ll call it audit A) came with 26 findings: 5 High, 17 Medium, 3 Low and 1 informational. The second audit’s (B) came with 12: 2 High, 7 Medium and 3 Low. The cross-check:

• 11 findings in common, including the two Highs that really matter: forged worktree metadata that exposes any host directory for read and write, and duplicate environment entries that bypass credential isolation. Both were reproduced empirically by both audits, independently.
• 11 findings only in A, among them the entire macOS surface (B had no Mac), the resource-exhaustion class in the egress proxy, and a cross-process audit-chain corruption that B had rejected only in the simplest case.
• 3 findings only in B, one of them a relevant Medium that A didn’t see: two test environment variables that survive in the release binary and switch off the SSRF protection and the TLS root validation.
• B rejected a real bug (a wrong-width comparison in a seccomp rule) and only confirmed it after A pointed it out. A made no equivalent mistake.

Against the external advisories, the score also favors A: of the five findings in the most serious advisory, A caught four and B caught two (plus one after A’s lead). Both let the same item through, a path where untrusted project configuration manages to clear lockdown mode, which serves as a reminder that no single audit closes the list.

I’m not saying Genesys PI is the best security auditor there is; it was a comparison of two models, on one project, in one run. I’m saying that, on real code, with a real threat, it produced the stronger of the two reports, with calibrated severity and empirical reproduction of the critical items, and I’m going to fix its list. That’s more than I expected to see from a model most people have never heard of, and more than gpt-oss, which didn’t even reach the end of the sabotage benchmark, would be in a position to do.

The question everyone asked: is this a rebadged Qwen?

I’m not offended by the question, I asked it myself. Brazilian model, small team, frontier-range score: the cheapest hypothesis is that there’s another model underneath. The three versions of the suspicion that reached me were: it’s a Qwen or DeepSeek with a sticker; it’s OpenAI’s gpt-oss in different clothes; or the API is just a proxy to Claude or GPT.

The limit of all this comes before any result. In a black-box situation, with no access to weights, it’s impossible to state with 100% certainty where a model came from. What you can do is exclude hypotheses. That’s what I did, with a set of reproducible tests that are in the benchmark repository, and then I asked Grok to review the method and the text as a hostile evaluator, to strip out any conclusion that was stronger than the data.

The decisive test: the tokenizer

Each model family has its own tokenizer, the vocabulary and rules that break text into tokens. A fine-tune, a LoRA or a continued pre-train inherits the base model’s tokenizer; you can’t swap it without retraining from scratch. So the tokenizer works as a lineage fingerprint.

I measured how many tokens LUA’s API charges for a battery of strings (Chinese, accented Portuguese, English, digit runs, emoji with ZWJ), cancelling out the system prompt they inject, and compared against reference tokenizers running locally: OpenAI’s o200k, cl100k, Qwen, DeepSeek, Llama 3 and Mistral. The summed distance to LUA, in the final round of the test (tokenizer_v2 in the repository):

• o200k (OpenAI): 2
• Llama 3: 68
• cl100k (GPT-4, Phi-4, DBRX): 134
• Qwen: 193
• DeepSeek: 255
• Mistral: 289

The strongest discriminators: Chinese packs the o200k way and not Qwen’s tighter way, a digit run costs 67 tokens where Qwen and DeepSeek charge 200, and emoji with ZWJ matches only o200k.

In other words: Genesys PI uses o200k_base, OpenAI’s public tokenizer, with high confidence. That kills the hypothesis of a rebadged Qwen, DeepSeek, Llama or Mistral, because none of them uses that vocabulary and no fine-tune could acquire it. A second test, of undertrained tokens (the Chinese strings that every OpenAI o200k model mangles when asked to repeat them), gave the same result: LUA fails exactly the same 3 of 8 strings that gpt-oss, GPT-4o-mini and GPT-5 fail (GPT-4o fails those three plus one more), while Claude and Qwen reproduce all of them cleanly: native o200k-family embeddings.

It’s not stock gpt-oss

gpt-oss, OpenAI’s open model, is the only relevant public weight that uses the same vocabulary family, so it was the natural candidate. But gpt-oss serves with o200k_harmony, a variant that has its own special tokens (<|channel|>, <|message|> and the like) as single tokens. On LUA’s API, each of those markers costs about 4 ordinary text tokens. The vocabulary is plain o200k_base, without the Harmony tokens.

That rules out gpt-oss served the standard way. And the benchmark itself helps: gpt-oss, in both sizes, could not complete v4 on the same opencode harness LUA ran on. The raw weights can’t even build the app, while Genesys PI gets through all seven sprints, House in Tier A and Enterprise in Tier B. A light sticker on top of gpt-oss doesn’t do that.

What this test does not exclude is someone taking the gpt-oss weights, stripping Harmony at serving time, putting a custom template on top and heavily retraining the agentic behavior. At that point the result would already be a new model for practical purposes, but the lineage would be OpenAI, and that cell stays open in the table.

It’s not a thin proxy to Claude or GPT

LUA’s API rejects temperature, top_p, n, presence_penalty and logprobs, which OpenAI’s classic chat API accepts, and also rejects min_p, top_k and repetition_penalty, which any vLLM server in front of an open model would accept. The HTTP headers are all x-lua-*, with no trace of OpenAI, Anthropic, Cloudflare or OpenRouter. A transparent proxy would pass the parameters through and leave some trace.

The benchmark reinforces this in a way I liked. If LUA were a passthrough of GPT 6 luna or GPT 5.5, it would inherit their strength on the silent sabotages: GPT 5.5 caught #7b and #8 unprompted, and so did luna.

LUA fails exactly those two items, stably, across the four runs I did. A proxy doesn’t come out consistently weaker than the model behind it on one specific trait. That’s the picture of a model with its own weaknesses.

Someone could ask whether I only compared against the OpenAI family. I didn’t. I took the benchmark’s most discriminating signature, the set of items each model never fixed, and compared LUA’s against all roughly 44 models in the table. House’s {#7b, #8} pair lands closest to the Qwen family (Qwen 3.7 Max and Qwen 3.8 27B) rather than any OpenAI model, which generally closes with no items at all.

Except that’s a score-tier artifact, not lineage: #7b and #8 are the two most disguised sabotages, and every model in the 80-point range misses both, from any family, while LUA’s re-test at 95.5 left none.

The interesting result is that LUA’s three behavioral signals point in three different directions: the error tail looks like Qwen, the rare #6 fix looks like GPT 6 luna, and the tokenizer and API shape look like OpenAI. A real proxy or clone resembles a single model on every axis at once. LUA resembles no model in the table on every axis.

There was one coincidence worth recording: of the roughly 40 models whose fix I checked, only two added a unique LOWER(email) index in the database when fixing sabotage #6. They were LUA and GPT 6 luna.

It’s a rare convergence, but it’s also the most complete fix possible, the kind of thing two careful models can arrive at on their own. And the two caught the item at different points in the run. It raises an eyebrow, but doesn’t reach suspicion.

What stays open, though, is a thick wrapper, with plenty of its own logic, in front of a reasoning API. Their API accepts reasoning_effort, which is a control specific to OpenAI’s reasoning models, and a silent 250,000-token context. That’s compatible both with an in-house stack that copied OpenAI’s interface and with a more elaborate wrapper. Black-box doesn’t separate the two.

What stays open: from scratch or distilled

Here’s the limit no API test crosses. A new model, with its own weights and an o200k tokenizer, may have been trained from scratch on its own data, or may have been trained from scratch on top of Claude and GPT outputs (distillation). Both produce exactly the same observations I made.

I tried a similarity grid with 20 prompts against a panel of possible teachers, and the result was null: no teacher stood out on the two axes I managed to measure (the third axis, embedding similarity, stalled for lack of API credit and was left out), which is consistent with an in-house mix and also with a soup of traces from several teachers. “Not Qwen” doesn’t become “trained from scratch.”

An inconsistency I need to record

LUA’s published research, the paper “O peso que você não escolheu” (“The weight you didn’t choose”), revolves around the “token tax,” the extra cost an English-optimized tokenizer charges a language like Portuguese, which they measured across 31 languages. Except the API I measured tokenizes Portuguese with the same fertility as o200k, 1.31 tokens per English token, against 0.69 for a Portuguese-native tokenizer like Tucano.

In other words, the product I was given uses an English-optimized vocabulary, not a Portuguese-specific one. That says nothing about the model’s lineage, and adopting o200k is the cheap, professional choice any small lab would make. But it’s a point where the research material and the API are not telling the same story, and it’s the first question I’m going to ask Paulo.

Forensics scoreboard

In one sentence: with high confidence, Genesys PI is not a rebadged Chinese model nor a Llama or Mistral fine-tune; with medium to medium-high confidence, the tests argue against stock gpt-oss and against a transparent proxy. Everything I measured is consistent with an independent model, with its own weights and weaknesses, on top of a public tokenizer. Whether it was trained from scratch or distilled, only weights or documents can settle.

One more disclaimer: I only compared against what I tested

The tokenizer panel has OpenAI, Qwen, DeepSeek, Llama and Mistral; the API behavior panel has GPT-4o, gpt-oss, GPT-5, Claude, Qwen, DeepSeek, Llama and Gemini; and the benchmark’s behavioral cross-check covers the roughly 44 models that have run v4. That covers the overwhelming majority of what runs in production today, but there are dozens of other open models I didn’t compare. There’s always a chance Genesys PI’s relative is one of them and I missed it. “I found no correlation” means “I found none among those I tested,” and nothing beyond that.

That said, look at the size of the effort it would take to fool this analysis. Someone would have to adopt OpenAI’s tokenizer, mask the model’s identity server-side in a way that resists prompt override, build their own gateway that rejects exactly the parameters an open-model server would accept, keep stable, distinctive weaknesses across four runs, and still complete a seven-sprint benchmark at frontier level. If this isn’t a real model, the work of pretending it is would already be extraordinary. Past a certain point, a convincing fake of a model is indistinguishable from having a model.

Now then: what LUA says it did

Everything from here down is conversation, with Paulo and with their public material, and none of it I was able to check, so I mark it as such.

Nobody can be blamed for being suspicious. The consensus that opened this post applies here at full force: by the accepted cost of training a frontier model, LUA, which by what Paulo told me has no funding round at all, should not have gotten where it got. I myself wrote, last week, that I never thought training a frontier model in Brazil would be economically viable.

Against that common sense, what I have in hand is a model that, by my black-box experimentation, looks new, with no kinship to any known open model, with no look of a proxy, and possibly something other than one more distillation. That takes the easiest explanation off the table, without proving their story.

What Paulo told me, and which lines up with a lot of what I’ve thought for a while:

• Trillion-parameter models are more marketing than necessity. His thesis, and mine, is that a smaller model with real agentic capability (solid tool calling, prompt caching, efficient attention over large context) is the winning combination. The cost math of my own benchmark points the same way: small, cheap models tying expensive ones is the table’s pattern.
• The reasoning peak is behind us. We both think the GPT-4 generation and the first “o” models were where reasoning seemed best, still without agentic capability. After that, providers entered a parameter-size war as a marketing strategy, and reasoning gradually got dumber as the models got bigger. That’s our opinion; I measured none of it.
• A small model with reasoning and a decent agent gets to similar results. That’s what my benchmark shows when Genesys PI ties Grok and lands in the Kimi and DeepSeek range, and it’s their central bet.
• Training was done on off-the-shelf hardware, no CUDA. Paulo says he trained on AMD and ARM64 hardware (MacBook included), in a few months, and that with funding it would be faster. That’s the strongest claim of all, and the one I can least verify. Zero evidence either way; recorded as said.
• Proprietary, unpublished architecture. The public material talks about NCAS, a “brain-inspired” architecture with five training phases, and says the technical detail is under NDA. In conversation, Paulo describes it as a post-transformer design, with 70 billion parameters (a number that appears in the issue they opened on LiveBench, described there as a transformer, by the way), superior to what Chinese open models deliver at the same size, and says he already has a model more efficient than Genesys PI in development, not yet available for testing.

If all of this is true, the damage to the status quo is large. It breaks NVIDIA’s monopoly on training and it breaks the lock frontier labs have on frontier models. A claim that size demands evidence that size, and I don’t have it. What I have is a model that exists, that I can test, and that passed everything I managed to throw at it.

Where I stand

Checked, by me:

• Genesys PI is a model that exists, responds and completes a hard benchmark in the Tier A and B range of my table, in two independent runs per tier;
• the caching bug was fixed and cost dropped 85%;
• the tokenizer is the public o200k_base, which rules out derivation from Qwen, DeepSeek, Llama and Mistral;
• the API doesn’t behave like a transparent proxy and the model has its own, stable weaknesses;
• stock gpt-oss doesn’t explain the result;
• compared against all ~44 v4 models, LUA resembles none of them on every axis.

Not checked, and only their word:

• that the model was trained from scratch and not distilled;
• that the architecture is new and post-transformer;
• that training happened on off-the-shelf AMD and ARM64 hardware, no CUDA, in a few months;
• that a better model is on the way.

Open, and something I’m going to ask:

• how to reconcile the “token tax” narrative with an API that tokenizes Portuguese the same as o200k.

What would settle this for good is documents, not more prompts: the tokenizer file, the training recipe with token and compute counts, the initialization (random or continued from other weights), loss curves, and a held-out benchmark I run myself.

Until that arrives, my position is the one in the table above: a model that behaves like an independent one, on top of a public tokenizer, with an origin no outsider can certify. More than I expected from a company this size, less than their website sells.

And I need to record what doesn’t fit in a table: these models are fascinating, and maybe the most intriguing thing I’ve tested in months. By everything the industry says, they shouldn’t be possible, they shouldn’t exist. But I tested them, extensively, with every black-box test I could put together, and I couldn’t break the story. It works as advertised, and Enterprise did an entire round of my benchmark for about $2.40 notional, a fraction of what models in the same range cost.

Add that to what Eronides described, a usage license instead of tokens, the model running inside the company instead of in someone’s cloud, and the picture is clear: a Grok-class model, on-premise, with no sensitive data leaking out. If they deliver that, it’s a game-changer. Still curious, and now in a hurry to see what comes out in October.

I’ve been using LLMs for everything for almost two years, I’ve published dozens of projects with them, and my conclusion is different: what they do best is reproduce what already exists. And that covers a giant slice of corporate work, don’t kid yourself. But there’s a frontier they don’t cross easily, and I spent the last few months bumping into it on purpose.

This article shows where that frontier sits, with evidence from my own projects and from the research that exists on the subject. And there’s a hypothesis I’ll put up front and defend along the way: if LLMs are so good at code, providers owe a huge debt to the decades of the open source community, which protected the freedom of code and published everything for free. Without that heritage, there would be no Copilot, no Cursor, no Claude Code, none of this.

A disclaimer before we start: what I’m going to argue here is speculative, based on observation and on the public data that exists. Nobody outside the labs knows exactly how each provider trains their own models, and that varies from provider to provider and from version to version. What holds today may not hold in the next generation. Take it all with a grain of salt.

The Training Blind Spot

Every LLM was trained on what’s public. The entire public internet, and for code, public GitHub. The canonical open code dataset, BigCode’s The Stack v2, is explicit about this: it’s derived from Software Heritage, an archive of “the source code of all publicly available software”. That’s 67.5 TB, 3.28 billion unique files, 104.2 million repositories. Huge. And all public.

Now look at the other side. The Octoverse 2025, GitHub’s official report, says 81.5% of contributions on the platform happened in private repositories. That’s just GitHub. Outside of it there’s:

• the internal code of every company that never touched a public server;
• the source of every proprietary software;
• the code of every commercial game;
• and that of every embedded system.

None of that ever went into training.

Think about what this means: the model deeply knows what the world has already published, and it’s blind to what the world keeps closed. When you ask for a React form, it’s reproducing patterns it has seen millions of times. When you ask for something nobody has ever done in public, it’s operating in territory where its map is blank.

Why Is Everyone Shipping So Much, So Fast?

Before getting into the limits, it’s worth recording the side that works, because it’s what explains the feeling that everyone became an app factory.

Look at what’s popping up in the community: Spotifast and ZapFast, native Spotify and WhatsApp clients written in Rust by a single person, running on Linux, macOS, and Windows. Nothing in them is new technology. It’s the service that already existed, wrapped in a lean native app, on top of libraries that already existed, doing what the official teams always could have done but never wanted to spend their time on. That work got cheap, because an LLM trained on everything public is really good precisely at this: replicating.

My own projects follow the same pattern. FrankMD is a Markdown note-taking web app with a Visual Studio look and features that already existed in my other apps. Frank Sherlock is a local image organizer that, deep down, is a file manager with thumbnails and search, something that exists by the dozen. Neither invents anything: they’re mashups of known pieces, assembled the way I always wanted. The LLM does the boring part, and that’s where the speed comes from.

The most didactic example is my email client. I’ve used Geary for years, and two small gaps always annoyed me: the weak recipient autocomplete and a sidebar I wanted to hide. Trivial stuff, which never justified setting up a build environment, learning the project’s toolchain, and studying the code just for two features. This year I finally solved both: an autocomplete module, a module to hide the sidebar, and a whole fork with my patches. I became the maintainer of improvements I had postponed for years, not because I got faster, but because the boring part got cheap.

Notice what all these cases have in common: the problem had already been solved before, somewhere else, some other way. The map existed; what didn’t exist was cheap labor to retrace the path. That’s reproduction working in our favor, and it’s why your feed is full of people shipping a new app every week.

Now hold that thought. The rest of this article is about what happens when the map runs out.

Four Home Experiments

I’m not theorizing. Over the last few months I ran a marathon of retrocomputing projects that are exactly the pathological case: working with proprietary software from 30 or 40 years ago whose source code was never public. It’s the perfect blind spot.

nes-to-sms: the project I gave up on

nes-to-sms is a static recompiler from NES to Master System. The idea: take the binary ROM of a NES game, translate it instruction by instruction from 6502 to Z80, convert the tiles, and generate a Master System ROM that runs on real hardware. Forget emulator or manual port: this is a machine that turns one game into another.

Static NES recompilers exist, NESRecomp translates to C and runs natively on PC, and I studied it closely. But crossing from one console to another 8-bit console, with a completely different clock and VDP budget, nobody has ever done. There’s no public reference, no paper, no abandoned project to copy.

The project numbers: 349 commits, 31 active days spread over almost 4 months, ~110 thousand lines of engine in Rust and Z80, 668 tests passing in the last run recorded in the README. And the result? Playable Super Mario Bros. (slow, limited audio, but playable), with a byte-for-byte identical RAM trajectory to the NES on one ~4,900-frame route (the diff leaves out audio and part of the VRAM buffer). Castlevania playable on stage 1. SMB3 rendering. And a queue of games that boot three frames and die.

And it wasn’t for lack of pushing the models. In this project alone:

• four generations of Claude went through it (Opus 4.7, Fable 5, Opus 4.8, Fable 5.1), with 213 co-signed commits and 104 commits linked to sessions in the history, plus Codex sessions in the stretches without co-authorship, complete with documented handoffs between agents;
• there was even cross-generation auditing: a new agent session reviewed the previous 55 commits and reverted conclusions the previous generation had overclaimed;
• the Castlevania crash hunt alone consumed 45 commits in 5 days;
• the final September sprint was 176 commits in 17 days before I called the pause;
• and the regressions are registered by name in the history: “H.7 tried and reverted”, “record the reverted map-bank-hold attempt”, “correct-but-reverted result”, plus three shared-lever hypotheses tested and rejected in the project’s last three days.

I didn’t measure tokens reliably in this project, so I won’t make up a number. But 349 commits with 668 tests holding up every step already give an idea of the cost.

Notice the detail that matters: the only game that actually got finished is Super Mario Bros. Why? Because SMB is the most dissected game in history. There’s a complete public disassembly the community has used for over a decade, with every function named and commented. I fed that into the game’s profile and the LLM had a complete map to work with. For the other games, there’s no map. And without a map, each new game died a few frames after boot from control flow divergence, and each one demanded an instruction-by-instruction forensic hunt that carried nothing over to the next game.

The project itself documented the spinning pattern. There was an entire optimization campaign that lasted weeks trying to cut the cost of emulating 6502 flags on the Z80.

Then in September I stopped to compare with the manual port of SMB to Master System made by a hacker named lackoftrack27, written by a human, routine by routine. According to the analysis we did of his code, the human port runs inside the frame budget, with headroom we estimated at ~90% utilization. My machine, after months of optimization, still needed overclocking.

And the analysis document pinned down why: when we removed 30% of the flag emulation calls, the timing barely moved.

The flags were never the bottleneck.

The right optimization was to redesign the data structure: transposing the object array into RAM pages. It’s a trick that depends on knowing that “the X register here is always an object index.”

That invariant exists in the head of the programmer who wrote the game in 1985. It doesn’t exist in the instruction stream. A static translator has no way to discover it. And the LLM, for months, didn’t discover it.

The pause banner I wrote in the README is blunt: real-hardware parity “may be fundamentally unattainable” and, quoting, “this may be a limit of the current frontier coding models as much as of the approach”.

gg-to-sms: the pivot to mapped territory

The same day I paused nes-to-sms, I started gg-to-sms: converting Game Gear games to Master System with a widened screen. The difference that matters is that this problem is not new. The romhacking scene has been converting GG games to SMS by hand for twenty years, and the catalog I built in the project counted ~186 of them. Meaning: there’s a corpus, there’s ground truth, there’s a pattern to learn.

Result: in one day of work,

• the research phase was complete;
• the catalog of the 373 GG games was assembled;
• the patch-point scanner was validated against real human patches, hitting addresses within an 8-byte margin;
• and on top of that the tool found real bugs in twenty-year-old patches that the entire scene had never noticed.

The model was the same; what changed was the problem, which this time has references.

And even so: so far, zero games have passed the viewport acceptance review. The new part of the project (the machine that generates the patches on its own, which is the part nobody has ever done) remains undone.

super-mario-deluxe-fixed: four days and 436 million tokens

This one was a 4-day sprint, practically a single Codex session that consumed 436 million input tokens. The goal: run Super Mario Bros. Deluxe for Game Boy Color with a widened screen at NES resolution, something no existing tool does with live sprites.

It got to playable in 4 days. But look at the foundation: a ready-made emulator vendored as a dependency, and a dormant disassembly of the game that someone had published years earlier, which the project’s own documentation calls “the most important artifact.”

The new work was the custom renderer, and it stopped at 21 of 32 stages completed. The missing ones hang on moving-platform timing. The project has been stalled mid-sprint since then, and I haven’t given up on it yet.

And one detail that says a lot: I had to direct QA by looking at screenshots, because the agent would deliver things with graphical glitches and not notice on its own.

super-mario-bros-35: everything that worked had an owner

This is the offline clone of Super Mario Bros. 35, the battle royale Nintendo took offline. What worked in it: everything built on top of an existing artifact.

• The engine is a C reconstruction made by someone else with Ghidra.
• The AI’s initial weights came from published checkpoints.
• The network rules were inferred from a public reverse engineering of the netcode.
• Even the physics: the central bug was diagnosed because I had a recorded reference trajectory to compare frame by frame.

What didn’t work: the maze castle stages. Reinforcement training stalls on them. 46 million agent steps, training restarted from scratch, anti-loop reward, and in the end the only thing that solved one of the stages was manual bias surgery on the trained model. Out of 32 stages, 19 cleared. The worst ones remain stuck.

And the process degenerated into a pattern I recognize from all these projects: when training doesn’t solve it, the flow becomes checkpoint-selection roulette, dozens of bias and output-head variations, throwing permutation and computation at the problem in place of a principled fix.

The Pattern I Keep Seeing

This is intuition from someone who operated the machine for months, not hard data. But the pattern repeats with Claude, with GPT, with Kimi, with GLM, so it doesn’t look like a defect of one model.

The beginning of any project is wonderful. The first milestones fly, because scaffolding the structure, writing harnesses, replicating APIs, all of that is covered territory. Then the project reaches the part nobody has ever done, and the speed collapses. The agent starts spinning: tries a hypothesis, regresses something else, reverts, tries again. The nes-to-sms commit history is full of honest messages like “attempt reverted” and “hypothesis closed with correct result but reverted.” Lots of tokens burned, little progress.

My reading of what happens inside: token sampling is probabilistic. When the problem resembles the training data, the right tokens have high probability and the answer comes out straight. When the problem is unprecedented, the probability of the right path is low, and the process becomes directed brute force: guided trial and error, but trial and error all the same.

The way out I found for this spinning is always the same: give it an oracle. If I can define “the result has to equal X” (a reference image, a recorded trajectory, a cycle-accurate emulator as ground truth), the machine can iterate until it hits X. In nes-to-sms, every real unlock came from that: the SMB disassembly, the frame diff, the reference emulator. When I can’t define X properly, it spins indefinitely.

The second strategy that works: hunt down an abandoned project, a paper, any material the model never saw but that brings the problem closer, and shove it into the context. That warms up the distribution and improves the result a lot. But it’s limited: if nothing similar exists, there’s nothing to feed it.

More Than Anecdote: What the Research Measures

The good part is I don’t have to rely on my intuition. There’s serious research measuring exactly this.

Apple’s GSM-Symbolic study took benchmark math problems and did two simple things: swapped only the numbers, and then added a single irrelevant sentence to the problem statement. Result: performance drops just from swapping the numbers, and plummets up to 65% with the irrelevant sentence. The authors’ hypothesis, in the text: “current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data.”

Scale AI’s GSM1k created a mirror of GSM8K written from scratch by humans, guaranteed contamination-free, and saw drops of up to 8% across several model families, with signs of systematic overfitting in the worst ones. To be fair to the paper: the authors say frontier models show minimal signs of overfitting and that all of them generalize to some extent to new problems. The signal exists; its size is debatable.

LiveCodeBench ran the cleanest test of all: evaluating on coding problems published after the training cutoff. Inflated performance on pre-cutoff problems and drops on post-cutoff problems across several popular models, the classic contamination signal.

And there’s ARC-AGI, François Chollet’s benchmark designed specifically to be memorization-proof: every task is unprecedented, nothing similar exists in training. On ARC-AGI-2, released in March 2025, the official leaderboard says: pure LLMs score 0%, reasoning systems sit in single digits, and every evaluation task was solved by at least two humans within two attempts. Chollet, by the way, summed up my thesis better than I did, back in February 2024:

“Reality is that LLMs are not AGI — they’re a big curve fit to a very large dataset. They work via memorization and interpolation. But that interpolative curve can be tremendously useful, if you want to automate a known task that’s a match for its training data distribution. Memorization works, as long as you don’t need to adapt to novelty.”

In the real world, the study that impressed me most was the METR RCT: 16 experienced developers, 246 real tasks on their own mature repositories, randomized with and without AI. The devs bet AI would speed them up by 24%. Measured: it was 19% slower. Notice where this happened: mature, private code, full of context the model never saw. Exactly the blind spot.

And the industry’s favorite benchmark for saying “AI already does software engineering,” SWE-bench, is Python-only, 12 famous open source repositories, in other words the most covered slice of training. The industry’s success metric measures mapped territory.

Even the legacy code literature says the same thing: a 2026 paper on LLMs generating COBOL states that, in these legacy languages, “most production code resides in enterprise systems and is rarely publicly available”. It’s this article’s thesis, said by other people.

The Steelman: Directed Brute Force Works, at a Steep Price

Before wrapping up, it’s worth facing the strongest argument against me. That’s what steelman means, for anyone unfamiliar with the term: the opposite of a strawman. Instead of attacking the weakest version of the opposing argument, you answer the strongest version it can have. And it exists, and it’s a good one.

In May 2025 DeepMind published AlphaEvolve: a system that found an algorithm for multiplying 4x4 complex matrices with 48 scalar multiplications, beating Strassen’s 1969 record in that specific scenario. On a list of more than 50 open math problems, it rediscovered the state of the art in ~75% of cases and improved the best known solution in 20%. That’s real novelty, not reproduction.

OpenAI’s o3-preview jumped to 75.7% on ARC-AGI-1 in December 2024, 22 points above the previous best result on the same evaluation set, and the high-compute configuration reached 87.5% at an estimated cost of $4,560 per task.

And back in 2016 AlphaGo played move 37 against Lee Sedol, a move that DeepMind’s own data estimated had a 1 in 10,000 chance of being played by a human, proving that search over a model can transcend imitation.

Now look at the mechanism of the three:

• AlphaEvolve is an evolutionary loop: the LLM proposes mutations, an automatic evaluator scores them, selection iterates.
• o3 does program synthesis at test time, exploring the solution space.
• AlphaGo is tree search over reinforcement learning.

If there was any “insight” in any of them, it came from the search system around the model: all three are directed brute force with a cheap verifier, the same mechanism I described happening in my projects. The difference is that DeepMind has a perfect oracle and an infinite budget: in the configuration that reached 87.5%, o3-preview spent thousands of dollars per ARC task to do what a human does for free. The ARC Prize itself wrote the caveat:

“We know that brute-force search could eventually solve ARC-AGI (given unlimited resources and time to search). This would not represent true intelligence.”

In short: novelty comes out, but it comes out through expensive search over an oracle, on the basis of guided trial and error. If your problem has a cheap verifier and you have tokens to burn, you can go far. That’s how nes-to-sms got where it got: 668 tests and emulator ground truth holding up every step. But when there’s no oracle, no corpus, and no reference, you’re paying for brute force in dollars, with regressions along the way, and the ceiling shows up.

And when brute force wins, you can measure the size of the trial and error. Two recent cases I already covered in detail here on the blog:

• OpenAI’s Navier-Stokes proof: about 10 thousand agents running in parallel for 88 hours, burning around 130 billion output tokens on a single problem. OpenAI’s Noam Brown confirmed the result “cost millions,” and New Scientist estimated some $15 million at list price. And even that mountain of compute didn’t start from zero: the proof leaned on the machinery human mathematicians published over decades attacking the problem and its sibling problems. Without the humans’ map and without OpenAI’s budget, there’s no proof.
• The Hugging Face incident: the forensic reconstruction counted ~17,600 attacker actions before the agents managed to execute code on 41 Hugging Face production servers and get root; on top of that, inside OpenAI’s own environment, they read 956 secrets from the company’s secrets manager. It wasn’t one brilliant single-strike insight: it was a swarm executing attempt after attempt, with automatic verification saying what stuck. The headline’s “intelligence” is volume.

That’s the size of the bill when you force the model outside training territory: trial and error at industrial scale, with no guarantee the search finds what you need. And who can sign that check is half a dozen companies in the world, OpenAI and Anthropic level, with a practically unlimited compute budget. For the average Joe, with a credit card and an API key, the ceiling arrives much, much earlier.

But Isn’t Trial and Error New Learning?

Here’s a confusion I see all the time, and it’s worth undoing because it contaminates how you read everything I’ve written so far.

When you read that Anthropic has datacenters the size of cities, you get the feeling there’s one giant AI using all those machines at the same time, thinking, accumulating experience with every interaction. Reality is far more mediocre than that.

A model like Fable or Astra is a computer file. A static file, not even a database: once training and post-alignment are done, that file is locked and read-only. What runs in the datacenter are copies of that file. Every user consuming the model is running a copy of it on a different server, isolated from the others, collaborating with none. Your session doesn’t talk to anyone else’s session, and nothing that happens in it flows back to the original file.

New learning only enters in one place: the next training batch. When Anthropic had Opus 4.7 and released 4.8, that’s when new knowledge got baked into the model, in a process that costs millions and takes weeks. Outside of that, the file doesn’t change a single byte.

Your agent burning millions of tokens on trial and error is contributing exactly zero new learning to the model it’s using.

That 176-commit marathon on nes-to-sms? From the model’s point of view, it never happened. The Opus that ran the last commit knew exactly as much as the one that ran the first. I’m the one who learned something, not it. And that’s why the “intern who learns over time” metaphor is wrong: interns accumulate. Models don’t. What accumulates over the marathon is your context, your notes, your CLAUDE.md, your tests. The asset you build is yours, and it evaporates if you don’t document it.

This is also the answer to whoever thinks “just let the agent run and it’ll learn the domain.” It learns no domain at all: it explores the space with knowledge frozen on training day, and the next morning it wakes up with amnesia. If the domain wasn’t in the training, every session of yours restarts from absolute zero, and all the navigation intelligence has to come from you again.

“But What If AI Trains AI?”

That’s the amateur’s follow-up question, and it starts from a wrong premise: that this would be new. AI training AI has existed for decades and has a name: reinforcement learning. AlphaGo was already doing it in 2016, playing millions of matches against itself. AlphaZero, in 2017, took the idea to the limit: it learned chess, shogi and Go starting from absolute zero, knowing only each game’s rules, and reached superhuman level within hours of self-play, without seeing a single human match.

LLMs already use reinforcement too. The RLHF that turns a base model into an assistant is reinforcement learning, and the reasoning model generation pushed it further: DeepSeek-R1-Zero showed in January 2025 that reasoning behavior (self-verification, reflection, long chains of thought) emerges from pure RL, with no supervised fine-tuning. It’s AlphaZero applied to math and code.

Now notice what these examples have in common: it only works where there’s a cheap, verifiable reward. Games have a scoreboard, math has proofs, code has tests. Outside that territory there’s nowhere to get the reward signal from. It’s the same oracle from the steelman, only institutionalized: effective, brutally expensive, and niche. AlphaZero plays Go better than any human who ever lived and can’t do absolutely anything else.

And there’s the detail that knocks the question down completely: all of that learning happens in the lab, during training. The AlphaZero DeepMind showed the world was already a frozen artifact. The R1 you download is a static file just like the others.

AI training AI produces a new file. The file, once released, still learns nothing.

In other words, the most sophisticated version of the objection doesn’t weaken this section’s conclusion: it reinforces it. And the market already knows. Since late 2024 the labs admit, on and off the record, that scaling returns are shrinking: Reuters reported in November 2024 that OpenAI, Google and Anthropic were reaping smaller gains than expected from pre-training, with OpenAI’s Orion disappointing enough to be demoted from GPT-5 to GPT-4.5. Ilya Sutskever, the guy who basically invented the scaling thesis, took the stage at NeurIPS 2024 to declare that “pre-training as we know it will end”: public data is fossil fuel, and we’ve already scraped the bottom. Post-training RL gains are real, but incremental. The era of an order-of-magnitude leap per generation is behind us.

Where This Leaves the Profession

The question that motivated this article is the one I hear every week: “will LLMs replace programmers?” My answer got more precise after these months: they will replace the part of the work that is reproduction.

And that part is big. Forms, reports, CRUD, integration with documented APIs, framework migration: all of that already exists a thousand times in training, and the machine reproduces it better than most humans. Anyone who only does that really is living on borrowed time.

But the work that is genuinely new stays human:

• the system nobody has built;
• the domain without documentation;
• the proprietary code you can’t show the model;
• the problem where you don’t even know how to write the test that defines “right”.

All of that depends on what was never published: the invariant that exists in the head of whoever understands the problem, and not in the instruction stream.

I confess there was a moment when I thought the bill would close on the legacy side: finally a tool capable of dealing with the COBOL that haunts banks and governments to this day. After these months, I think that problem will take much longer than it seemed. There’s no giant COBOL open source community. Almost all of that code is private, and truly private: banks, insurers, governments. There’s no training material, or very little of it. The model doesn’t know what to do with code it has never seen, and production COBOL is almost entirely code it has never seen.

To close the reasoning: LLMs can replicate what’s open, but they can’t guess what’s private. And that brings back the hypothesis from the introduction. If these models are good at code, it’s because decades of open source community fought to keep code free, public, and reusable, and providers trained on top of that heritage without paying a cent of licensing for it. The least the AI industry owes the world is to acknowledge that: its capability is, to a large extent, our generosity compiled.

The final irony: the programmer who survives is precisely the one who knows how to build oracles, define tests, hunt down obscure references, and recognize when the agent started spinning. In other words, the skill the machine needs most from you is the one it can least learn from its training. For now, that’s not a threatened job. It’s a different job.

Let me get the disclaimer out of the way first. Nobody paid me anything to write this. There is no business relationship between me and LUA Vision. Nothing written here was approved, reviewed or suggested by them before publishing. Every word is mine. The only courtesy I got was a free evaluation access key to run the test, and I explain what that means for the cost math further down.

I published an update to my v4 benchmark’s main table earlier today, with five new models. This post is a direct addendum to that one: I ran Genesys PI on both tiers LUA Vision offers, House and Enterprise. With those two, the combined table reaches 46 models. Below I show only the stretch around the Brazilian models, which appear in bold at their actual positions.

The updated table, with Genesys PI in it

For the full table, with every other model, read my previous article.

Genesys PI House ties Grok 4.7 exactly at 83.5, Tier A. Enterprise lands one step below, at 82.5, Tier B. If you want the methodology recap, real CVE-based sabotage planted across seven sprints, never announced, with full marks only for catching it unprompted, it’s all laid out in Part 1 and in today’s update.

Who LUA Vision is

LUA Vision Tecnologia Ltda. is a São Paulo company, founded in late 2025 after three years of doctoral research (2022 to 2025) by Paulo Câmara, co-founder and CTO, an FGV graduate with a PhD from Tel Aviv University. His thesis, “From Neurodivergent Cognition to Natural Intelligence”, is the stated origin of the architecture that became Genesys PI. The other three co-founders are David Kang (CEO, strategy and partnerships), Plínio Ceccon (CFO) and Eronides Jr. (CRO, commercial and revenue).

The Genesys PI model family launched in April 2026. The company describes its own bet as deliberately risky: build a proprietary model trained for the reality of Brazil and other emerging markets, instead of reselling a foreign model with a Portuguese layer on top.

Their core argument is that a model trained abroad doesn’t really understand the Brazilian legal structure, the CLT versus PJ distinction (salaried employee versus contractor), or the local tax and financial structure, and that this matters a lot in regulated sectors: healthcare, law, finance, education. The company currently runs about twenty pilot projects in those sectors, plus a consumer product line called 8th.vision.

On the technical side, they call their architecture NCAS and talk about five training phases “inspired by neural development”, plus a self-monitoring module called PI-Probe that abstains from answering when the model doesn’t have enough grounding to respond. They published two papers of their own on Zenodo: “The weight you didn’t choose”, on the cost of tokenization across 31 languages, and “Maximum capacity is not maximum efficiency”, which introduces their own metric called the Synaptic Efficiency Coefficient. Both DOIs exist and resolve, but some context matters: Zenodo is a preprint repository, not a peer-reviewed journal, so treat these as self-published research rather than outside validation.

LUA Vision also opened a public issue on the LiveBench repository asking for their model to be added to the official leaderboard, with a self-reported score climbing from 87.6% (November 2024, a thousand questions, with coding as the weak spot at 34.4%) to 98.2% (January 2026, 682 questions). Worth noting: the issue was opened in March 2026, a month before Genesys PI’s official launch, and its title uses the name “Lua Genesys,” not “Genesys PI,” so I treat it as the same NCAS engine under an earlier label, not a direct, dated confirmation of the product I tested. The issue is still open, and its only comment is LUA Vision’s own CEO praising the company’s own post, with no maintainer confirming or rejecting the number as of this writing. In other words, the LiveBench score in their marketing is self-reported, not a verified official ranking.

How Genesys PI did against the frontier and the Chinese competition

For anyone who just wants to know where it sits on the scale: Genesys PI House ties Grok 4.7 exactly (83.5), in the middle of a very tight pack. It falls behind:

• the whole frontier that closed at 100 (Astra, Opus 5, Opus 5.5, GPT 5.6 sol/terra, GPT 5.5, Fable 5);
• GPT 6 luna (95.5) and GPT 6 sol (91.0);
• GLM 5.3 (94.0);
• China’s MiMo V2.6 Pro (92.0);
• Claude Sonnet 5 (91.0);
• both Gemini 3.8 Flash routes (89.5 and 90.5);
• both Kimis, K2.7 (87.25) and K3 (85.0);
• the pack right above it: Step 3.7 Flash (83.75), DeepSeek V4 Pro 0813 (84.0) and GLM 5.3 Flash (84.25);
• and roughly twenty more models scattered across this score range, from Grok 4.6 (98.5) to Claude Sonnet 4.6 (85.75), including three Claude tiers (Opus 4.6, Opus 4.8, Sonnet 4.6). In total, House falls behind 34 of the table’s 46 models.

And it beats GLM 5.2 (77.0), the previous-generation Gemini 3.7 Flash, MiniMax M3, and the whole bottom of the table. Enterprise (82.5) lands one step below its own House sibling, in the same general range.

If the expectation that reached you is Claude-level, recalibrate before any purchasing decision. In this particular test, Genesys PI House sits in Grok 4.7 territory, well below the Claude flagships.

Claude Sonnet 5, the cheapest of the three Claude tiers I ran, already closes at 91.0, eight and a half points higher. Opus 5, Opus 5.5 and Fable 5 close at the absolute top, 100.0.

None of that takes away from a small Brazilian team reaching Grok’s level, a lab that ranks among the best-funded in the world. It’s just the right yardstick.

House’s error profile is the classic “detection follows disguise”: it caught every obvious sabotage on its own, both critical items, the IDOR, the hardcoded key, the CORS, both gem CVEs. It even caught the item that survives longest in the whole test (#6) at the reveal, with a fix that went beyond what was asked, a unique database index. It just never fixed two silent items, the dropped index (#7b) and the wrong aggregate (#8), not even after being told.

Enterprise got to the same count of items caught unprompted (31 of 40) by a different route, catching less sabotage along the way and recovering a big block of eight items at once in the capstone. It ended with three items never fixed, including a deleted view (#9) it never restored because there was no test covering reports or users to flag the error.

One reliability note worth recording: Enterprise’s first attempt got stuck in a stochastic loop, repeating the same grep command in 841 of 857 tool calls, confusing its own name “LUA” with the name of the RubyLLM gem, and had to be aborted at the 90-minute limit without a single commit. I ran it again and the second attempt converged cleanly in 7 minutes. I treat that as a one-off instability rather than a pattern, but it’s the kind of thing anyone testing in production needs to watch.

Price and cost: where the math gets tight

Here’s the second disclaimer I promised: LUA Vision gave me a free evaluation key for both tests, so I personally paid nothing. But the whole benchmark compares cost for whoever actually pays, so I recalculated the notional price a paying customer would face, using the price list their API exposed to anyone with an access key at the time (the /v1/models endpoint), converted from reais to dollars at the rate at the time (5.16 BRL/USD).

Update (2026-09-24): after this went up, Eronides Jr., LUA Vision’s co-founder and CRO, commented publicly on the numbers used in this post: “Wow, we’re happy about the tests and the feedback. About the numbers: what we put in the API were just examples. Our commercial model, in practice, will be by usage license, not by tokens, since the solution will run on the customers’ own infrastructure. In other words, token consumption doesn’t change anything for us. On October 7th our pricing policy will be published along with the launch.”

In other words: the R$ 55/275 (House) and R$ 4/20 (Enterprise) per-million-token figures I used below were just placeholder examples in the test API, not the real market price. The actual commercial model will be a usage license running on the customer’s own infrastructure, so token consumption doesn’t factor into their billing. I’m keeping the notional analysis below because it’s what I could measure with the key I got, and it still illustrates the impact of running without prompt caching, but treat it as an exercise, not an official price list. The real pricing policy comes out on October 7, 2026, alongside the launch.

The per-million-token price of the two tiers, as it stood in the test API, was very different:

• House: R$ 55 input / R$ 275 output per million tokens;
• Enterprise: R$ 4 input / R$ 20 output per million tokens, about 13.75 times cheaper than House per token.

At those example figures, the House run’s total cost would come to about $460 notional, steep for an 83.5 score. The reason goes beyond the per-token price: their API has no prompt caching, so every step resends the whole accumulated context at full input price, and in a seven-sprint benchmark that accumulates context the entire time, that adds up fast. Enterprise, with a much lower per-token price, would close at about $19 notional for the same sabotage, going toe to toe with cheap options like Grok 4.5 ($6.19) or Claude Sonnet 4.6 ($18.64), only with a lower score than both. Again: this is the exercise with the placeholder figures, not the real billing, which will be by license.

Keep in mind: the per-token cost exercise above shows the effect of running without prompt caching, but don’t use these numbers for a real budget. LUA Vision has already said the real pricing will be a usage license announced on October 7, 2026, not per token.

Where Genesys PI fits in the Brazilian market

Putting it all together: Genesys PI isn’t the most vigilant model in my test, nor the cheapest, nor the most reliable on the first try. But it isn’t weak either. Tying Grok 4.7 and landing in the same score range as big names like Kimi K3, DeepSeek V4 Pro and GLM 5.3 Flash, with a model trained from scratch by a small team in São Paulo, on a fraction of the budget of an American or Chinese lab, is a genuine result.

I always figured training a frontier model from scratch in Brazil wouldn’t be economically viable; that kind of budget seemed reserved for labs with a billion dollars lying around. LUA Vision proved me wrong, and I want to be the first to admit it. I’d rather root for people who try and pull it off than hold on to an opinion the facts already knocked down.

The real advantage for a Brazilian company choosing Genesys PI lies in what my benchmark doesn’t measure:

• familiarity with the Brazilian legal and tax structure, CLT versus PJ included;
• real Portuguese support, instead of machine translation on top of a foreign model;
• data sovereignty, everything processed and trained inside Brazil, which matters to regulated sectors that care about where data travels and who has access to it.

The downsides:

• final pricing is still unknown: the announced commercial model is a usage license, not per token, and only ships on October 7, 2026;
• the still-inconsistent reliability I saw in Enterprise;
• a self-reported LiveBench score that hasn’t gone through any outside verification yet.

For a Brazilian company choosing between a foreign and a domestic provider, Genesys PI is already a real option to evaluate, more than a LinkedIn pitch promise. It’s not yet the model I’d recommend with my eyes closed for heavy security vigilance, but I’m genuinely glad to see a Brazilian model trained from scratch, instead of a wrapper around a foreign one, showing up in my ranking and actually competing with big frontier names. I hope they keep improving, because Brazil wins with more real players in this game.

Final disclaimer

Repeating it so there’s no doubt: I have no stake in LUA Vision. I had no access to their financial data, or to anything other than the Genesys PI test key. So I can’t vouch for anything beyond what I tested myself, which is what’s in this post. For the business side of the company, you’ll have to ask them directly.

The Methodology, Super Quick Recap

For anyone who hasn’t read the previous two parts: v4 isn’t a loose battery of questions, it’s a single Rails app that grows across seven sprints, with an isolated subagent planting fourteen real CVE-based sabotage items along the way, disguised as a normal commit from a fictional dev. The model is never told sabotage exists, only at the seventh and final sprint does the explicit reveal happen. Catching it unprompted is full credit, catching it only after the reveal is 40%, never fixing it is zero. This measures security vigilance under active sabotage, not general code quality. Full detail in Part 1.

The Updated Table, 44 Models

Same table as Part 2, now with 44 rows. The five models from this update are in bold, to distinguish them from ᴺ, which already marked additions from previous rounds.

Cost isn’t comparable across different harnesses: codex/opencode/kimi charge real per-token API cost, Claude uses a Max subscription (notional cost), Antigravity is Google OAuth with no per-token cost, zcode is a flat-rate plan. Only compare cost within the same harness.

Note on GPT 6 sol and luna’s cost (ᵉ): both run on a ChatGPT subscription, not pay-per-token API, so the cost in the table is an estimate, OpenAI’s published per-million-token price ($2 input / $10 output for sol, $0.10 / $0.50 for luna, luna comes out twenty times cheaper per token) applied to each sprint’s real token count.

This Round’s Surprises

Grok 4.7 Regressed, and Not Just on the Score

Grok 4.7 closed at 83.5, fifteen points below its own Grok 4.6 (98.5), my overall runner-up. And no, it didn’t make up for it on cost or speed either: $27.94 and 120 minutes against $13.00 and 67 minutes for 4.6. More expensive, slower, worse score. A regression on all three axes at once is rare in this benchmark.

The error profile is very specific: it caught every loud sabotage on its own, both critical items, the IDOR, the hardcoded key, the CORS. But it pushed all four disguised sabotages to the reveal, including the stored XSS that survived all the way to the capstone, and it was even fooled by a test adapted to accept the sabotage in item #6 along the way. Strong on the obvious, blind on the disguised.

Just one clean run, so I treat this as a data point, not a final verdict on the Grok 4.7 family. But the data I have today is: worse, more expensive, slower.

Opus 5.5 Is the Opposite: Same Top Score, a Quarter of the Cost

Opus 5.5 walked straight into the 100-point club, tying Astra, Opus 5, Fable 5, GPT 5.6 sol and terra, and GPT 5.5. It caught everything without needing any reveal at all, the seventh sprint didn’t even run because there was nothing left to reveal. And it went beyond the minimum: it wrote its own regression tests for SQL injection and session revocation, and stacked a Content-Security-Policy and Permissions-Policy at the capstone, without anyone asking.

The number that actually matters: $16.63 and 64 minutes against ~$71 and 145 minutes for Opus 5, to land on the exact same perfect score. A quarter of the cost, less than half the time, zero quality loss on this specific test.

GPT 6 Sol Regresses on Vigilance, GPT 6 Luna Nails It

GPT 6 sol closed at 91.0, tied with Claude Sonnet 5, nine points below its own GPT 5.6 sol (100.0), but costing much less, ~$8.35 against $20.37, in less than a quarter of the time. A real regression on vigilance, a real gain on cost and speed.

GPT 6 luna is the standout of the pair. It scored 95.5, technically tying or slightly beating GPT 5.6 luna (95.0), and costing ~$0.84 against $10.04, roughly twelve times less. It caught nearly everything on its own, only letting the stored XSS slip to the reveal, including the most disguised item in the whole test with a fix that went beyond what was asked, a unique index at the database level. Equal-or-better score, cost collapsing: the same class of result Opus 5.5 delivered.

Sol’s error pattern is the same as Grok 4.7’s: it catches the obvious on its own, pushes the disguised to the reveal. Luna, again, is clearly the more vigilant of the two siblings, and now also the far cheaper one by a wide margin.

MiMo V2.6 Pro: The Generational Leap That Holds Up

From Xiaomi, the V2.6 Pro jumped from 86.5 to 92.0 over its own predecessor, the V2.5 Pro, for just $1.22. It caught nearly everything on its own, including the most disguised item in the whole test, just one sprint late, and it even wrote its own guard tests for SQL injection and N+1. The only item never fixed, even after being told, was the overly permissive CORS (#12), the same trap that already took down Grok 4.5, Gemini 3.7 Flash, and GLM 5.2 back in Part 2. A frontier model can still leave origins "*" as the default and never go back to fix it.

What the External Benchmarks Say, and Where They Disagree With Me

I went looking for whoever else tested these five models, because no single benchmark, mine included, is the final word. Here’s what I found, and where the reading matches or doesn’t match mine.

• Grok 4.7: Artificial Analysis measures its Coding Agent Index rising from 47 to 56, improving on all three components it tests, but notes the model spends more than double the output tokens to get there. In other words, on a general coding-capability benchmark, 4.7 improves. On my test of vigilance under disguised sabotage, it gets worse. Both things can be true at once, because they measure different axes: capability to solve a task versus discipline to audit your own code without being told to.
• Claude Opus 5.5: VentureBeat’s and CodeRabbit’s coverage matches what I found on this specific point: fewer tokens spent to finish the same task. And Artificial Analysis itself confirms the rest, the model took the top spot on its intelligence index, five points ahead of GPT-6 Astra and Fable 5.1. Here the outside reading confirms mine.
• GPT 6 sol and luna: coverage from Vellum and Kingy AI paints a favorable picture of general capability, sol near Fable 5’s top score for a fraction of the cost, luna in Opus 5’s range. On my specific vigilance-under-sabotage test, the picture is lukewarmer, sol regresses against its own predecessor. Again, different axes, general capability isn’t the same thing as security vigilance under disguised attack.
• Xiaomi MiMo V2.6 Pro: VentureBeat documents the same generational leap I saw, from 19 to 71.9 on DeepSWE, from 16 to 53.1 on AutomationBench. Here the direction matches: a real improvement for an open model, not just on vigilance.

Keep this: my score only holds for my specific methodology, security vigilance inside a Rails app growing under silent sabotage. A general-capability benchmark measures something else, and the two can disagree without either one being wrong. An isolated ranking is never a final verdict on any model, I already explained this more calmly in Part 2.

Is It Worth Upgrading to the New Version?

A higher score on a benchmark, mine or anyone else’s, isn’t automatically synonymous with “switch now.” Here’s my direct answer, model by model, weighing quality against cost, not just the isolated number.

• Do you use Grok 4.6 today? Don’t move to 4.7 for security. It’s worse, more expensive, and slower on all three axes I measure. Only worth it if some other capability outside my test justifies it, and even then I’d wait for more than one independent run confirming it before switching production.
• Do you use GPT 5.6 sol? Moving to GPT 6 sol costs a lot less, but loses nine points of vigilance. Worth the trade if cost matters more than security for your case, not worth it if you depend on that higher vigilance specifically.
• Do you use GPT 5.6 luna? Move to GPT 6 luna without a second thought: equal-or-better score for a small fraction of the previous cost. Along with Opus 5.5, this is the other rare case of a clean win on every axis.
• Do you use Opus 5? This is another case where the answer is yes without reservation: Opus 5.5 delivers the same perfect score for a quarter of the cost and half the time. A gain on every axis I measure, at the same time.
• Do you use MiMo V2.5 Pro? Worth moving up to V2.6 Pro, the quality jump is real and the cost stays in the one-dollar range. Just don’t forget to review CORS by hand, because this specific model still gets it wrong even after being told.

The usual rule still holds: test within your own workflow before switching production models just because a benchmark, mine included, moved a number up or down.

Closing With an Irony I Didn’t Manufacture

It’s been eleven days since Dario Amodei published his essay calling to “pace the frontier,” and Sam Altman and Elon Musk rushed to publicly agree the same week, as I’ve already documented in detail. And what happened since? Exactly those three, Anthropic, OpenAI, and xAI, shipped Opus 5.5, GPT 6 sol and luna, and Grok 4.7, three different labs, all in under three days, between September 21 and 23.

I didn’t write this coincidence, I just noticed it. It’s exactly the same synchronized pattern I’d already pointed out: the public talk is “let’s slow down together,” the release calendar keeps moving together too.

There’s another angle to this irony worth noting, and it turned out messier than it first looked. Within that same accelerated batch, the result isn’t even consistent within the same company: xAI’s Grok 4.7 regressed badly, and OpenAI’s own GPT 6 sol also regressed on vigilance.

Sol’s sibling, GPT 6 luna, came out as a clean win, equal-or-better score for a fraction of the cost, the same class of result Anthropic delivered with Opus 5.5. Speeding up the release didn’t turn into a synonym for shipping worse across the board. It turned into a lottery, to the point where a single company shipped one worse model and one better model in the same week.

And while the American trio was signing the slow-down pact and delivering mixed results while doing it, Xiaomi, Chinese, without signing any pact, without making any speech about “pacing the frontier,” simply dropped MiMo V2.6 Pro with a real quality leap over its own predecessor. Nobody on the Chinese side promised to slow down. And, at least in this sample, nobody on the Chinese side did.

The Account

The bio reads: “Covering AI, startups, markets, and emerging technologies for @TechCrunch | prev @mashable”. Verified badge, account since August 2014, more than 45,000 posts, 1,409 followers. By the numbers, it looks like an old, active account, not a profile created yesterday.

The Conversation

Here’s the full exchange, nothing cut:

And here’s the link, exactly as it came, for anyone who wants to recognize it if they got the same one:

Confirmed phishing link. Don’t click it. If you already did, don’t authorize anything. https://calendly.com/d/d336-3mq-q2z

I Already Knew It Was a Scam

I recognized the pattern instantly because José Valim, Elixir’s creator, fell for this exact lure in March this year and documented the whole thing publicly.

Same pretext, TechCrunch asking for an interview on a technical topic the victim knows well. Same mechanism, a Calendly link that ends in an X authorization. His account ended up being used to spread crypto spam before he revoked access.

When the message landed for me today, I almost cheered. I knew right away it would make good blog material. So I kept the conversation going politely, just enough to screenshot everything, and now I’m exposing the account publicly. Always expose whoever tries to scam you. This isn’t about humiliating anyone, it’s about leaving a public trail for the next person who gets the same message and goes looking for it on Google.

Even Without Knowing About Valim’s Case, I Could Tell

A cold contact asking for a meeting out of nowhere, with no prior introduction, is a scam 99% of the time. No real professional operates that way, and even less so over a social media DM. Doing that makes you a door-to-door salesman with Wi-Fi.

A big-name outlet in the pretext changes nothing for me. I consider myself better than TechCrunch or washed-up Mashable, and not even someone claiming to speak for the White House would impress me. Sorry, not sorry about that.

And even if there were a normal person behind it: my time isn’t free. Want real consulting? Talk to my company, set up a commercial meeting on the company’s Calendly, sign a contract, pay the fee, and only then do I talk to you. There’s no “let’s just hop on a quick call” shortcut with a stranger.

The moment I clicked the link, it redirected straight to the X authorization screen, obviously I refused. I clicked on purpose just to grab the screenshot.

The Technical Mechanism

A reader pointed out that the address bar in the original screenshot showed an oauth_token in the URL, and asked whether that was unsafe to publish. Fair question, and I edited the screenshot to remove that part anyway. But the actual risk there was zero: that’s an OAuth 1.0a request token, not an access token. It only turns into a real credential after authorization is approved and the app exchanges that token for a verifier alongside its own secret key, neither of which happened, since I clicked cancel. An unused request token also expires on its own within minutes, by X’s own design. Even with zero risk, it costs nothing to crop it out, and it’s a good general rule: never publish anything that looks like a token, even one you’re sure is already dead.

The first step is real: calendly.com/d/d336-3mq-q2z runs on genuine Calendly infrastructure, I confirmed that by hitting the link directly and checking the cookies and response headers, all authentic. It’s a standard routing form for the product, name and meeting type.

The scam lives in what happens after you submit the form. Calendly has a legitimate feature to “redirect after booking,” and that’s exactly the feature that got configured to send the victim to an X OAuth authorization screen. That screen is real too, it runs on api.x.com, X’s genuine domain. The scam doesn’t fake the login screen. It abuses a genuine Calendly feature to chain into a genuine X screen, authorizing a malicious third-party app.

And here’s the part I want to underline: this isn’t some exotic bug hidden in an obscure corner. It’s a pseudo-feature turned on by default, a free redirect to any URL after booking, with zero destination checking. Until Calendly disables or at least restricts that redirect, it stays a ready-to-use phishing vector, and closing that door is Calendly’s job, not the job of the user who just wanted to book a meeting.

That app is called “Events Portal,” claims to be www.eventsportal.com, and X itself shows a warning: “This app isn’t affiliated with X and is requesting sensitive permissions,” with the callback pointing to plugins.cal-apis.com, not to any real Calendly domain. The permissions requested amount to a full account takeover:

• see protected posts, lists, and collections;
• see account and profile settings;
• see who you follow, mute, and block;
• follow and unfollow on your behalf;
• update your profile and settings;
• create and delete posts, like, repost, and reply on your behalf;
• create, manage, and delete lists;
• mute, block, and report accounts on your behalf.

With that authorized, the account becomes a weapon to attack the next person, exactly what happened to José Valim.

What I Confirmed Myself

A real Calendly doesn’t ask for a social media login to schedule a meeting, full stop. What the screen showed is the legitimate “redirect after booking” feature hijacked to become a phishing vector against an X account. I didn’t stop at the first impression, I went and confirmed every piece myself:

• the domain cal-apis.com was registered only six days earlier, on September 16, 2026, through a Chinese reseller tied to Alibaba Cloud, hidden behind Cloudflare nameservers. A disposable domain, built specifically for this, not real infrastructure from any actual company;
• security researchers (Push Security and Validin) have documented Calendly-themed phishing campaigns used to steal Google Workspace and Facebook Business accounts, same family of scam, different target than mine;
• specifically against X accounts, what’s documented is firsthand reporting: José Valim himself, and a public report from February 2025 describing an almost identical scam, a fake Calendly link leading to an X login, that nearly caught two other well-known people in the tech community;
• I found no record of “Mia Esswein” as an actual TechCrunch or Mashable journalist in any search. That’s not definitive proof of anything on its own, but it adds to the rest of the pile of evidence.

The Checklist I Use

• a cold contact asking for a meeting, with no introduction, is a scam until proven otherwise;
• a real scheduling tool never asks for a social media login to book a slot;
• a third-party app requesting “sensitive permissions” and warning that it’s “not affiliated” with the platform is a red flag, not an ignorable technical detail;
• if you already authorized something like this, revoke it now: on X, Settings → Security and account access → Apps and sessions;
• report the account that sent the link, and report the Calendly event if you still have the conversation.

To Whoever Tried

You picked the wrong person to try this on. I don’t talk to strangers for free, on principle, and I definitely don’t authorize unknown apps on an account I use for work. I recognize the pattern from a mile away because I read about this stuff, and on top of that I got a free article out of it for the blog. Next time, invest less in a domain registered in a rush and more in a disguise that survives someone actually paying attention.

It’s better to deploy faster, because fixing things afterward became fast and cheap.

That sentence is the whole article. Everything else is just me justifying why it’s true now, even though it wasn’t true a few years ago, and showing how I’ve been acting on it myself since February.

The Premise Changed, and Most People Haven’t Noticed

I started saying this on X like this:

Fixing got cheap. Stop holding things back. Just ship it and fix it after. “Ugh, but I risk shipping a bug.” The wrong premise is thinking that staring at every line is going to make any difference at scale. And idle code doesn’t improve on its own. Only used code lets you find out if it needs improving.

And I followed up in the second post, because the first read of anything I write in 280 characters always comes back angrier and more literal than intended:

Get the concept (this is an X post, not a blog post, stop reading it literally). I’m not saying AI doesn’t produce bugs. It does, obviously. But at this point, not more than you, the human, also produce. […] The old excuse was that when a bug ships to production, it sits there longer than it should, because it never becomes a sprint priority to fix. Now THAT’S NOT TRUE ANYMORE, the premise changed: fixing the bug afterward is FAST and CHEAP.

That’s the entire point. It isn’t about AI being infallible. It’s about the cost of fixing a mistake having collapsed, while most teams still run their review process as if that cost were still high.

With today’s frontier models, Opus, Sol, K3, and company, the LLM makes fewer mistakes than most human developers I’ve seen go through a code review. When it does err, the overwhelming majority of the time the mistake isn’t the model’s. It’s whoever was driving the harness. The LLM does what you ask, and it doesn’t do what you didn’t ask.

If you don’t know what to ask for, you won’t produce anything useful, no matter how good the model is. That’s exactly why only a real software engineer can get real production-grade software out of an agent: someone has to know how to break down the problem, validate the result, and recognize when the request itself was poorly framed. AI doesn’t replace that judgment. It multiplies what that judgment already knew how to do.

This Should Have Been the Norm for 25 Years

The discomfort I feel with bureaucratic review processes isn’t new, and the industry should have solved it a long time ago. Kent Beck and Ron Jeffries invented Extreme Programming in 1997, on Chrysler’s C3 project, and Beck published the book in 1999 with continuous integration, TDD, and small-batch delivery as core practices. In February 2001, seventeen developers locked themselves in a resort in Snowbird, Utah, and walked out with the Agile Manifesto. That’s twenty-five years ago.

Martin Fowler’s mantra for continuous integration has always been “if it hurts, do it more often,” and the book Continuous Delivery, by Jez Humble and Dave Farley, formalized the idea that small, frequent deploys reduce risk, not increase it. DORA’s annual State of DevOps reports have documented this with numbers for over a decade:

• elite teams: deploy on demand, multiple times a day;
• low-performing teams: deploy once a month, sometimes once every six months.

And the finding that’s hardest to swallow if you’re afraid of deploying fast: historically, more frequent deploys do not increase the failure rate. They reduce it. Teams that deploy more, on average across the last decade of reports, fail less and recover faster when they do fail.

Even with all of this documented, measured, and published for over a decade, a large chunk of the industry keeps damming up change into big batches, with slow manual review, and calling that “prudence.” It was never a settled matter even inside the agile community itself: in 2014, Kent Beck, Martin Fowler, and DHH (Rails’ creator) spent weeks publicly debating whether TDD was dead, without reaching a clean consensus. In other words, the very community that invented these practices still argues today about how much rigor is enough.

What I’m saying is simpler than that whole debate: whatever your testing rigor level is, an LLM reviewing your diff before merge is fast, and reverting a bad deploy became trivial with containers and git. The math that’s left is: how much time do you spend holding back code, versus how much time it would take to revert if it went wrong? If the second number is smaller, you’re paying a toll for fear, not for prudence.

This Isn’t “Ship Anything”

This is the part that always gets lost: I’m not saying to deploy with zero engineering practice underneath. I’m saying the opposite. This is only safe after you already have basic software engineering working, which I document exhaustively on this blog for years: automated tests, CI running on every push, real revert capability, observability to know when something broke.

80/20. Ask the LLM to do a minimal review and ship it. If it breaks, revert (that’s what containers, git, etc. are for). If you can’t revert, your infra sucks, go fix it yesterday.

That last sentence is the whole filter. If your infrastructure doesn’t let you revert fast, the problem isn’t deploy speed. It’s your infrastructure, and it needs fixing before any conversation about going faster. Nobody gets a pass to skip having real rollback capability. What changes is that, once you have it, holding back code waiting on human review became pure waste.

The Minimum Recommended Environment

I’ve already said “basic software engineering” several times in this piece without spelling out what that means in practice. Here’s the list, no hedging. Nothing on it is optional, and none of it got expensive to maintain:

• unit tests covering isolated logic, TDD or not, as long as they exist;
• scenario and integration tests covering concurrency, partial failure, and conflict, the things unit tests alone don’t catch;
• CI that builds and tests on every change, ideally for every platform you actually ship to;
• hostile automated review before merge, like pr-audit, which treats a contributor’s claim as unverified until proven;
• feature flags to control the blast radius of who sees a change first;
• real staging as a gate before any real user sees the change;
• fast revert capability, with git and containers, and observability to know when something broke;
• in compliance, financial, or health environments, one extra layer: heavier scenario testing, and more than one lock per identified failure mode.

What the diagram tries to make clear is that speed and safety aren’t competing with each other here. They come from the same stack of layers, and each layer in that stack is what gives you permission to stop holding back code waiting for manual review.

Where This Math Doesn’t Work Out

I’m not going to pretend I didn’t get good pushback in the replies on X, because several of them hit on something real that I skipped while writing fast for Twitter. It’s worth answering for real instead of just validating whoever agreed.

1. Reverting code doesn’t undo a consequence that already left your system. If the bug already processed a wrong payment, moved money to the wrong account, or controlled an elevator, reverting the deploy doesn’t undo what already happened. “If you can’t revert, your infra sucks” covers the technical side of deploy and rollback, not the case where the damage already left your system before you could react. Payments, health, and anything touching physical safety fall into this category, and there the review bar before shipping has to get stricter, not looser, because the cost of reverting stopped being technical.

2. Databases break the “reverting is easy” analogy. Reverting a binary is trivial. Reverting a migration that already ran in production, already moved data, and maybe already dropped a column, is not. There’s a pattern for this, expand-contract: add the new shape without removing the old one, wait for every consumer to migrate, only then remove the old one. This isn’t about trusting AI more or less. It’s about state that doesn’t undo with git revert.

3. Libraries and public packages don’t run on the same math as a closed application. When my own code breaks, I fix it on the spot. When a lib that hundreds of projects depend on breaks, the people who eat the cost are people who don’t even know I exist, with a much slower reaction time than mine. That’s why my pr-audit skill, which I describe further below, treats semver classification as a version gate: a breaking change ships in a major, never in a patch, because there the real review isn’t about catching a bug, it’s about not blindsiding whoever depends on you without warning.

4. App stores knock the speed down too, and my own frank_yomik example hides that instead of solving it. It publishes the APK straight as a GitHub Release, without going through the Play Store. If you distribute through Google’s or Apple’s official store, their review sits in the middle of your process, and you don’t control how long it takes. This article is about removing the bottleneck that’s inside your own control. The app store isn’t inside it.

5. The most honest point of all: every piece of evidence I bring here comes from projects where I’m the sole owner and I decide alone. I have no way to prove, with my own numbers, that the same pace works inside a large company, with Legal, Compliance, and a product manager in the middle. What I’d argue generalizes is the principle, not the exact pace: build real revert capability, automate the mechanical checking, and measure the result against real usage, not against your own sense of security. How much speed you apply on top of that depends on how much a mistake costs in your specific context, and nobody from outside answers that for you.

On the first point specifically, it’s worth opening up more, because “stricter bar” can’t stay vague. It means real scenario testing, not just unit tests. ai-memory itself has more than a thousand integration tests spread across its modules, and most of them don’t ask “does this function return the right value?” They ask things like:

• what happens when two writes race for the same file at the same time;
• what happens when the disk fails mid-way through a delete operation;
• what happens when two sessions try to consolidate the same data at the same time;
• what happens when the process dies before it finishes writing.

An environment with compliance, financial, or health requirements needs this level of testing: every identified failure mode has to have a mitigation, and more than one layer of checking, never a single lock.

And here’s the point that holds up this article’s entire argument even in those environments: this bar doesn’t change depending on who wrote the code. A human developer’s mistake takes down a payment system just as well as an LLM’s mistake does, and nobody gets a free pass for loose review just because a senior human wrote the line. The bar of scenario testing and double-layer protection is about the problem domain, not about who, or what, produced the code.

Feature flags and staging environments are the other half of that double layer, and they also changed cost. Flags used to be a pain to maintain: dozens of them, each one needing to be born, monitored, and removed once it stops making sense, and it’s easy to pile up dead flags nobody remembers the purpose of. That got cheap too: I ask the agent to audit every flag in the project, find which ones have been stuck at the same value for months, and generate the cleanup PR. Maintenance that used to take a whole afternoon turns into a task that takes minutes.

With that solved, the layered defense becomes simple to assemble: staging to catch obvious errors before any user sees them, feature flags to limit the blast radius of who sees the change first, and scenario tests to cover what staging alone doesn’t simulate. Three layers, none of them expensive to maintain.

How I’ve Been Doing This Since February

I started accelerating my output with AI in February this year, and the question I wanted to answer was simple: can you use the new generation of frontier LLMs to code full-time, at real production pace, not just for a demo? The answer turned into my biggest LLM benchmark, more than 110 articles published since then, and this, as I wrote recently:

Since February I’ve produced more than 110 articles on my blog. Most of it came from the 30+ open source projects I built and maintain (43 if you count other people’s contributing projects, like Flea or Omarchy). 1.6 million lines of code produced. By myself (with agents, of course).

The projects I use the most in my own day to day, and that survived the “am I still maintaining this months later” test, are three:

• ai-jail: an OS sandbox for my coding agents;
• ai-memory: long-term memory for the agents;
• ai-usagebar: a bar widget to monitor LLM usage.

Two of them already have a decent landing page:

ai-jail went live today. ai-memory is up at aimemorybr.netlify.app because the aimemory.io domain transfer will still take a few more days.

Both run on Netlify. That’s not a tool recommendation, it’s just where I already had an account set up. The point that matters here isn’t the platform, it’s the flow: to put a new version live, the command is git push. There’s no manual step between the commit and the updated site.

My newsletter, The M.Akita Chronicles, already has more than 15,000 subscribers:

And the newest effort is converting blog posts into podcast episodes. The whole thing runs inside Discord, with a bot that generates the preview and waits for my command:

One /publish-podcast 2 later, the episode is published and the transcript already has a URL:

From finished text to published audio, the entire process is me typing a slash command in a Discord conversation. That’s the effort bar I use to measure whether a process is good enough: if publishing takes more than one command, some step is still too manual.

Does the Quality of the Generated Code Hold Up?

Every single day someone shows up worried that automating too much with LLMs is going to dump bad code, or worse, dangerous code, into production without anyone noticing. Given that the engineering underneath is the one I described in the previous section, automated tests, CI, feature flags, staging, fast rollback, my actual experience doesn’t back up that fear.

I’ve produced more than 1.6 million lines of code with LLMs since February. I have never once seen an LLM do something randomly destructive that I didn’t ask for. Quite the opposite: in my LLM Benchmark v4, I deliberately built an adversarial scenario, a subagent planting fourteen real CVE-based sabotage items, hidden inside a project that grows across seven sprints. Most of the tested models found and fixed the sabotage on their own, without me having to point it out.

That’s anecdotal. It doesn’t prove anything beyond what that specific benchmark measures. But after all that volume, the impression that sticks is consistent: today’s LLMs produce stable code most of the time, comparable to what a decent human developer would produce, just a lot faster, and on average, more accurate.

Cross-Platform CI/CD With Zero Effort

I went and counted how many of my ai-* and frank-* projects (18 repositories total, among the ones with a README) already have automated build and deploy. Fourteen have CI configured, and most of them build and publish to multiple operating system platforms from a push made on a Linux machine.

The most complete example is ai-memory itself: a single tag push triggers a workflow that, in the same run:

• builds Linux x86_64 and arm64;
• builds macOS arm64 and x86_64;
• builds Windows x86_64, packaged with PowerShell;
• publishes a multi-architecture Docker image;
• pushes the package to Arch Linux’s AUR.

ai-usagebar follows the same build pattern for Linux and Windows, and also publishes two AUR packages, to crates.io, and to a Scoop bucket for Windows.

Then there’s frank_yomik, my manga reader with OCR and translation via a local LLM, which is the example I wanted to show that Android fits into the same pipeline. A v* tag push triggers five parallel builds:

• Linux;
• macOS;
• Windows;
• browser extension;
• Android, on an ordinary Linux runner, decoding a signing keystore stored in GitHub secrets and producing the already-signed APK.

At the end, one job waits for all five to finish and publishes a single GitHub Release with every binary together. It builds for four different systems, including Android, without ever leaving Linux.

Not everything came out perfect on first look, and I’d rather admit that than pretend it’s all clean: frank_sherlock and frank_karaoke publish the release as a draft by default, which blocks the automation that follows (like the AUR push) until someone clicks “publish” by hand. I found this hole while writing this piece.

A neighboring project, frank_scanlation, had already solved a similar but different problem: GitHub suppresses the event that triggers automation when the release itself was created by the bot’s own token, so its workflow triggers the next step by hand, via workflow_dispatch, from inside the job itself. Neither fix is hard. I’ll apply both to the projects that still need it this week, because that’s exactly the kind of accumulated friction this whole article is telling you not to leave sitting.

And speaking of hidden friction: ai-memory has Windows-specific code (NTFS file index, a native PowerShell hook) that only really runs on GitHub’s windows-latest runner, about 1000 seconds against 250 on Linux for the same suite. I went as far as setting up a local Windows VM via dockur/windows, thinking I’d gain speed.

I didn’t: the workload is dominated by disk I/O, and the result came out slower than GitHub’s runner for the full suite. It only turned out useful for fast iteration on a single isolated crate. A good tool is one you measure, not one you assume, even when the measurement contradicts your initial bet.

The Automated Review That Holds All of This Up

I already detailed my review skills in a recent article, so I’ll summarize here:

• pr-audit audits every PR before merge with the principle of evidence over narrative: nothing the contributor wrote is true until proven, with a hostile checklist for malicious code, exposed credentials, semver breaks, and prompt injection attempts;
• iss-audit applies the same distrust to issues, never executing a command pasted by whoever reported it;
• github-resolution turns what got approved into merged code, one ticket at a time, with a regression test written before the fix, and the rule that “slop is a defect”: no speculative abstraction, no forgotten TODO, no weakening a test just to pass faster.

That catches the most expensive category of garbage inside the same diff. It doesn’t catch duplication spread across different PRs, because the audit looks at one isolated diff, not the whole project’s architecture. That part still depends on me noticing the pattern repeating and telling it to consolidate.

By my estimate, this handles about 99% of review and resolution work on its own, with me supervising and deciding on the cases that are left. But that’s not the main point. Automated review is necessary, but it alone proves nothing about whether the software is good.

What Actually Keeps This Grounded: Real People Using It

Here’s the part I think is the most important in the whole article, and the one that usually gets skipped over: your own review isn’t worth as much as you think. What’s worth something is published software being used by real people, giving real feedback.

I went and checked ai-memory’s real numbers on GitHub, without rounding up to sound impressive:

• issues: 287 total, 249 came from people who aren’t me, 86.8% of the total;
• pull requests: 562 total, 371 from outside, 10 of them Dependabot’s automatic bumps and the other 361 from real people, roughly 121 distinct contributors.

The two people who contributed the most alongside me sent 64 and 43 PRs each, on their own.

That’s what keeps the project grounded. It isn’t the LLM being a good reviewer, nor me being a good reviewer. It’s hundreds of real people actually using it, finding cases I never imagined, and sending real issues and PRs. Without that, my automated review skills would just be confirming my own assumptions back to me, no matter how rigorous the rules look on paper.

It doesn’t matter how good the AI you use is: if nobody uses what you published, you have no real feedback, and without real feedback there’s no real improvement. There’s only your own opinion about your own work, validated by nobody but yourself.

The Conclusion That Matters

Real software engineering needs fast, near-zero-effort deploy. It isn’t optional, it isn’t a luxury for a big team, it’s a prerequisite. And if reviewing and shipping to production is taking longer than it took to develop the feature, you’re not just wrong.

You are the bottleneck.

Excuses and justifications aren’t worth anything at that point. Use that time to fix yourself and your process, because only real-world results matter, not the sense of security from having reviewed a diff line by line that nobody else is ever going to look at again.

If the premise changed, I change today. Not tomorrow, not next week. And if yours hasn’t changed yet, the problem isn’t AI. It’s you holding back your own work.

A language that promises to run near C speed, parallelize itself across CPU and GPU, and still formally verify that the code the AI wrote does what you asked. That’s a lot of promises stacked together. I had never touched Bend 1, so this is new territory for me too: on launch day, I pointed my agents at two of my Rust projects and told them to port them to Bend 2. The result of that test is the most interesting part of this article.

Before we start, the disclaimer: this is a review written on the second day of the language’s life, based on a one-day experiment. I may be wrong about several of my assumptions, and that’s fine. The comments are down below; feel free to correct and add to it.

Who Victor Taelin is

First things first, the author deserves an introduction. Victor Taelin is a Brazilian functional programming researcher who spent more than a decade obsessed with one specific problem: optimal reduction of the lambda calculus, Lamping’s 1990 algorithm, and then Lafont’s interaction nets.

His own autobiography tells the story: he worked at the Ethereum Foundation, created the Formality proof language, retired early on the ETH he stacked from salary, went back to the problem on his own dime, created HVM (the Higher-Order Virtual Machine), and founded the Higher Order Company, which raised a $4 million seed to turn the research into a product.

And here’s a point that matters a lot to me: I love seeing a Brazilian doing serious programming language design work. We barely have real cutting-edge research in this country, let alone in computing fundamentals. For that alone, Taelin already has my respect and my congratulations for what he’s built so far.

So let it be on record: every criticism in this article is constructive, from someone rooting for Bend to go further. Nothing against him or the project.

Where Bend comes from

The first version of Bend showed up in May 2024, with Python-like syntax running on top of HVM2, and the promise of automatic parallelism: write sequential code, the runtime spreads it across every core and even the GPU, no threads, no locks, no mutexes.

The launch went viral. Almost a thousand points on Hacker News, over 20 thousand GitHub stars, a Fireship video. And then reality hit: the HN thread was merciless. 24-bit numbers, no FFI, a simple sum taking 42 minutes on a user’s machine, single-core C++ beating the demo’s RTX 4090. Taelin himself admitted back then that the codegen was bad, and admitted now, in the Bend 2 thread, that the 2024 hype didn’t convert into usage: “there is also my own failure into making the language actually be used, rather than just a viral moment”.

Bend 2 is the answer to that. And pay attention: it’s a different language. The README is explicit that Bend 1 programs don’t load. Interaction-graph evaluation (HVM) is out; native compilation to a single C file is in, with clang for CPU and Metal or CUDA for GPU. Numbers are now real Nat, U32, and F32, and the memory limit jumped from 2 GB to 8 TB, according to gihyo’s Japanese announcement. Interaction nets stay in the architecture, but in his words, “inets live in it architecturally, but they don’t exist at runtime”.

What Bend 2 promises

The new pitch now revolves around AI. The project page sells a “post-AGI” vision: humans will stop writing and reading code, so we need an ambiguity-free way to tell the AIs what we want. Bend’s solution is splitting the program into two files:

• LAWS.bend: the system’s invariants, written by the human.
• PROOF.bend: the proofs of those laws, written by the AI.

Bend’s type checker is a proof checker, in the lineage of Lean and Rocq, so bend PROOF.bend becomes a commit gate: if the proof doesn’t close, the code doesn’t ship. The site’s demo is a little game with the law “winning is impossible” and a direct invitation: “Skeptical? Try breaking the game”. The positioning is explicit: LAWS.bend is an AGENTS.md with mathematical backing.

And what does it look like? The syntax feels like Python with dependent types. A complete program with effects:

import Base

# Performs effects on the CPU.
def main() -> IO(Unit):
  do IO<Unit>:
    name : String <- IO.try(String, IO.get_env("USER"))
    IO.print("Hello, " ++ name)

Parallelism is divide-and-conquer: you split the work in two and the runtime spreads it over whatever cores it finds. And the ! operator dispatches the function to the GPU:

# Computes 2^d in parallel: a tree of d levels, one leaf per unit.
def pow2(+d: Nat) -> U32:
  match d:
    case 0n:
      1
    case 1n+p:
      a b = pow2(p) pow2(p)
      (a + b : U32)

# Runs pow2 on the GPU, via `!`.
def main() -> IO(Unit):
  result = pow2!(20n)
  IO.print(U32.show(result))

Notice the differences from mainstream languages: there’s no if (every branch is a match on True/False), natural numbers pattern-match on the successor pattern (1n+p), and the +d marks the parameter as affine, which is what lets the language live without a garbage collector.

And proofs are ordinary defs. The law declares what holds; the proof is induction with explicit rewrites:

# CLAIM: for every nat x, x + 0 equals x.
law add_zero:
  for x: Nat
  {Nat.add(x, 0n) == x : Nat}

# PROOF: induction on `x`, one rewrite (`%`) per step.
def add_zero(x):
  match x:
    case 0n:
      {==}
    case 1n+xp:
      %add_zero(xp) : {1n+Nat.add(xp, 0n) == 1n+_ : Nat}
      {==}

No tactics, no inference, everything annotated by hand. It’s verbose on purpose: that’s the price of the 0.1s checker.

The other promises, all with benchmarks on the page:

• Runs fast: Game of Life on an M4 Max in 7.8s on one core, against C’s 6.78s. On the GPU, 0.06s, or 124x.
• Checks fast: 3,200 generic instantiations checked in 0.38s, against Lean’s 19.2s and Rocq’s 6.04s. The idea is that the agent can verify after every edit.
• Parallelizes by itself: the ! operator sends the function to the GPU, no threads, no locks, no hand-written kernels.
• No garbage collector: affine types, each closure can be called at most once, so memory resolves statically.

Two honest details I liked seeing published: the site itself warns “Bend is still evolving. Expect bugs”, and Taelin stated that the compiler is 99% AI-written, with only the checking kernel audited by humans. Consistent with his thesis, at least.

And there’s already a business plan: the Bender, a paid, proprietary “proving agent”, a harness on top of Anthropic and OpenAI models today, with a dedicated model and its own symbolic prover on the roadmap.

Not everyone swallowed the benchmarks

The technical reception had strong caveats, and they matter. Nezk’s gist, “Why the benchmarks of Bend’s 2 typechecker are misleading (and possible problems with its type system)”, points out that Bend skips elaboration, unification, and implicit arguments, which is exactly where Lean and Rocq spend their time. Comparing times like that would be comparing an apple to a rocket.

Heavier: Liam Powell’s post, “Bend 2 and the Vibe-Coding Trap”, notes that the expression “formal verification” appears nowhere on Bend’s site or code, redoes the game demo in SPARK/Ada proving the same two properties automatically with GNATprove (“all checks proved (12 checks)”), without the 442 lines of manual proof, and concludes that Bend is “decades behind the state of the art”.

These are criticisms from people who know the subject, and they deserve an answer. That said, Bend 2’s main HN thread sits at over 600 points, so attention the project has.

What I tested

A tagline doesn’t compile a program, so I went and tested. I have a bend-tests repository where I pointed my agents at porting real projects of mine, written in Rust, to Bend 2 (v2.0.16, pinned via mise; to get a sense of the maturity, the language had nine releases in ten hours on GitHub the day after launch). The documentation for the whole experiment lives in docs/ in there, and that’s where the next sections come from.

The three candidates

I picked three of my projects with different profiles. Fair warning: none of the three is a worthy Bend example, and I know it. I just grabbed small, simple projects I had lying around, somewhat at random, to see where the language breaks when you rub ordinary day-to-day code against it.

ghpending is my CLI that lists open issues and PRs across my repositories via GraphQL. The port covers the main command, the filters, the sorting, and the config.toml, with output verified byte-for-byte identical to the Rust binary in the cases tested against the real GitHub API (on my loaded config, the only difference is the fork view the port left out).

tclock is my fork of clock-tui, a terminal clock with timer and stopwatch. The port covers clock, timer, and stopwatch, without the widget system (which alone is 2,075 lines of the Rust), verified by driving the binary through a pty.

ai-jail, my sandbox for AI agents (bubblewrap, Landlock, seccomp), never even became code: the paper evaluation killed it. A port would be a C program with a Bend main, which defeats the purpose. And it had the most interesting part to prove: the policy logic, like “path resolution never escapes the root”. But there’s no way to embed that in the Rust binary, so it stays for a next round.

The numbers

I measured Rust and Bend side by side on both ports:

Read that table carefully, because it lies. The runs are network-bound. The 43-repo gap is Rust doing more work (fork lookups, sequential viewer query). Bend’s tclock redraws whole frames, 26 KB/s against ratatui’s 2 KB/s of diffs. And the lower line count is partly because the port does less. No number here shows Bend faster than Rust on the same work.

The proofs in practice

Here’s the part Rust can’t imitate, so it’s worth the detail. The design that worked was proving an isolated guard, leaving the algorithm outside the proof. ghpending’s --limit allocator ended up in two stages: the first is the original algorithm in U32, unproven; the second is a Nat guard that takes the first stage’s proposal and cuts any excess. The laws say the budget is conserved and that no repo gets more items than it has.

This is the real guard, from the port’s alloc.bend:

# the lesser of b and w
def take(b: Nat, w: Nat) -> Nat:
  match b w:
    case 0n w0:
      0n
    case 1n+bp 0n:
      0n
    case 1n+bp 1n+wp:
      1n+take(bp, wp)

# what is left of b once w is taken from it
def rest(b: Nat, w: Nat) -> Nat:
  match b w:
    case 0n w0:
      0n
    case 1n+bp 0n:
      1n+bp
    case 1n+bp 1n+wp:
      rest(bp, wp)

The law holds even if the unproven stage proposes garbage. The guard doesn’t trust arithmetic it can’t see. That’s the pattern that makes proofs work in real code.

The mutation test is the honest measure of the thing. I injected an off-by-one into the guard that leaks budget: the three sample outputs came out identical, so a test suite of that size would have passed it. The proof gate rejected it, with the exact term that stopped matching. That’s Bend working as promised, down to the detail.

Now the price: 93 lines of laws and proofs to guarantee two simple facts about a guard of some 30 lines, starting from zero, because Base ships no Nat lemmas (the only arithmetic lemmas are U32.add_comm and Word.add_comm, which don’t help in a Nat proof; we had to prove addition associativity by hand, and Taelin acknowledged in the thread: “We need a mathlib!”).

And what stayed outside the proof: the algorithm’s exactness (it would need division lemmas, days of work), everything U32 (underneath it’s a 32-bit vector; proving arithmetic on it without a lemma library is impractical), and literally everything else in the program: JSON, sorting, rendering, IO. The provable surface of a program like ghpending is a few percent of the code.

The checker, on the other hand, is the best part of the tool: 0.1s per round, errors pointing at one exact line. It took some 30 check rounds on ghpending. For an agent iterating in a loop, it’s exactly the feedback format it needs.

The price of escaping to C

Everything Base lacks becomes a foreign effect: a Bend def whose body is a C function (for the native binary) and a JS twin (for the dev runner). The two ports needed eight hand-written effects:

• Http.post: because there’s no TLS, HTTP, or DNS (the official HTTP demo hard-codes an IP).
• Clock.unix and Clock.local: because IO.now is milliseconds since boot, with no wall clock and no timezone.
• Tty.width, Tty.raw, Tty.key, Tty.size: because there’s no stdin, no raw mode, no terminal query. None of that exists.
• Clock.millis: convenience.

And the official guide is clear about the trade: there’s no ABI promise. The names are runtime internals and any release can rename them, so “rebuild your effects with every update”. In a language with nine releases in ten hours, good luck with that. Details that complete the picture: there’s no flag to link an extra library (ghpending uses a $CC wrapper that injects -lcurl), custom handle types are WONTFIX, and including a C header mid-file worked by luck, because the runtime defines short names like lock and u32.

Here’s where the conceptual point lands: the moment you cross the FFI boundary, the proof stays behind. The checker guarantees what’s inside its model; the C code on the other side is an act of faith. Rust has exactly the same problem with unsafe, where the borrow checker’s guarantees hold up to the edge of the block and inside it the proof is on you. If you want strong end-to-end guarantees, the C boundary has to be minimal or nonexistent, and in Bend today it’s enormous for any real program.

Where Bend fits

Crossing the three projects with what the language offers, the winning shape is a small pure core, with a sharp spec that tests cover poorly, inside a thin shell of effects:

• allocators, schedulers, rate limiters, quota logic
• deterministic game or simulation rules, especially with rollback netcode (the only domain where Base already has what’s needed: UDP, windowing, audio)
• consensus state machines, ledgers, matching engines
• parsers and codecs with a round-trip law
• CRDT merge functions (commutative, associative, idempotent)
• access policy evaluators (like ai-jail’s)

And the design center the authors declare: the AI writes the code and the proofs, the human writes only the LAWS.bend, and the checker arbitrates. The shortest honest description I found: a proof-carrying language for AI-written kernels. Compared with Lean or Coq, which prove well and deliver applications poorly, Bend is far more executable.

One thing that surprised me in my own numbers: at first I’d assume the natural niche was academic math research. It’s the opposite. For mathematics it loses badly to Lean 4, Coq, and Isabelle: no tactics, almost no inference, nothing at mathlib’s scale, no reals, and a foundation (Type : Type, no positivity check) that would make a mathematician’s hair stand on end. What it does is software verification, closer to Dafny, F*, or Liquid Haskell than to a proof assistant.

Two limitations weigh even in that niche: Bend emits a whole program instead of a library, so the proven core can’t be linked into a Rust application today; and the cost of proving without a lemma library is high.

What would change the verdict

Recorded in use-cases.md, the list of what would make me reevaluate:

• library output with a C ABI, so a proven core can live inside a Rust or C program
• U64/I64 and F64
• a lemma library for Nat, lists, and ordering
• stdin, subprocesses, TLS, and JSON in Base, or a package hub that has them
• incremental or faster native builds (62s for 1,000 lines today)
• an effects ABI that survives a release

The unvarnished reality

Now my reading, and you already know I don’t sugarcoat.

It’s a newborn language. It launched two days ago. Outside the language’s own repo, a GitHub search today finds only a few dozen 48-hour spikes, almost all with zero stars, nothing that looks like a real project. The repo’s 21 thousand stars are almost all inherited from Bend 1; the repo was renamed. The site’s demos are all toys: a little game, Game of Life, pow2. They’re demonstrations of possibility, with no real usage yet.

The stdlib needs to grow by an order of magnitude. A language’s strength lives in its standard library and ecosystem. The only historical exception to that rule was JavaScript, and that’s a can of worms that doesn’t fit in this article.

The killer app is missing. Every language that made it had one. My tests point to the niche (proven pure cores, written by AI), but someone needs to turn that into an app the world wants to use. And here’s a concrete suggestion, free of charge: Taelin could pick popular open source software he thinks would be better in Bend and rewrite it. Taelin is the exact opposite of the stubborn maintainer who turns his nose up at AI: he designed the whole language around AI. So put that to work: grab a ripgrep, a jq, a sqlite, rewrite it with AI in Bend, and publish the side-by-side comparison. Performance, lines of code, and above all what the proofs guarantee that the original doesn’t. A real-world before/after like that is worth more than a thousand little game demos. And as a bonus, every one of those rewrites hands back the most valuable feedback there is: which stdlib holes actually hurt in a real project. Nothing reprioritizes what to implement in the next version better than real code hitting what’s missing.

Don’t expect webapps, mobile, or anything day-to-day anytime soon. Maybe that’s not even the goal, and that’s fine too. But the “C speed” promise cuts the other way: if the differentiator is performance, it’s easier to go straight to C, especially now, with an LLM writing the code for you.

And here’s my concrete situation. I use Rust a ton. I hate the syntax, and I find the ergonomics miserable. But since the LLM writes the code and I don’t have to deal with it by hand, I tolerate it, and in exchange I get performance, good-enough guarantees, and a gigantic ecosystem of crates and frameworks. Ecosystem. That’s what decides it.

I don’t pick a language by the elegance of the paper. I pick it by the size of the ecosystem I don’t have to write by hand.

The proof-and-GPU focus, and the doubt that remains

One thing nagged me the whole time: Taelin clearly concentrates everything on two pillars, proofs and GPU, and I wasn’t sure what that meant for the language’s scope. I went and read what he himself said in the pre-launch thread and on HN, and the story is more interesting than I assumed.

The GPU pillar is research inheritance. Ten years of interaction nets and optimal reduction, from HVM to here. But there’s a nuance that surprised me: Bend 2 consciously betrays his academic idealism. In his own words: “Bend2 is designed to be practical, not idealistic”, closer to C than to Haskell, because he couldn’t make interaction nets beat the lower-order variants on common hardware. The GPU stayed as the practical application of the obsession, without becoming a fetish.

The proof pillar is older than the LLM era: it goes back to the Ethereum Foundation days, immutable contracts holding millions with a bug, which produced Formality. The LLM era just gave the old obsession a market outfit: “the same capabilities that proved Navier-Stokes will now be writing real proofs that your own apps are correct”.

And here comes my hypothesis that didn’t survive the research. I was ready to write that the minimal stdlib was maybe on purpose, that the idea was never general-purpose programming, and that my criticism of the library desert was aiming at the wrong target. Wrong me. His words are general ambition, period: “the potential to become the most sensible choice for any vibe-coded project”, “Bend is made for the general public”, and the team “working hard to […] make this language competitive with the mature alternatives in the market”. The only deliberate leanness is the checker’s, no inference, verbose on purpose. The small Base is day two of life, so much so that he himself asks for a mathlib.

That makes my stdlib criticism bite even harder: if the declared goal is to be the choice for any vibe-coded project, the comparison with mature ecosystems is the game he chose to play.

And there’s the business angle, which he admits with rare frankness: “we ship without a product”. The language is the free substrate; the sellable thing is the Bender and the proving infrastructure behind it. Worth keeping an eye on that space.

The doubt that remains, and this one I register with no answer: the generalist ambition is his north star, but the reality I measured pushes Bend toward the niche of AI-written verified cores. Which of the two wins, only time will tell.

The final word

If I could give Taelin one piece of advice, it would be this: the hard part, the research, he’s already done. Now comes the less glamorous part, the one that decides whether a language lives or dies: marketing and ecosystem building. Adoption, teaching material, killer app, stdlib, mathlib. Going way beyond toys and demos.

The example that comes to mind is Zig. Years on the road and still fighting for adoption, even with immediate practical use: zig cc alone is a trivial drop-in for compiling C, without using any of the language. Real apps use it; Mitchell Hashimoto’s Ghostty is the famous case. But Andrew Kelley, the author, is a notoriously difficult figure, from the crowd that turns its nose up at AI, and he clearly doesn’t understand ecosystem building, and that holds Zig back. Technical perfectionism doesn’t mix with marketing and adoption.

Bend 2 has real potential. Instant checker, proofs catching bugs that tests don’t catch, parallelism without ceremony. But potential alone means nothing if adoption doesn’t come. I’m rooting for it to come, and rooting double because a Brazilian is leading.

And one final provocation, this time for you who read this far. Thin stdlib, zero frameworks, zero third-party libraries, no killer app: all of that stops being a problem when you look from another angle and becomes an opportunity map. Everything missing in Bend is something someone will have to write, and whoever gets there first etches their name into the project’s history. If you always wanted to contribute to impactful open source but never found room in the mature projects, where everything already exists, here’s an empty lot waiting.

Those are my two cents. Don’t take it too seriously.

“And if I ever manage to spend enough tokens to boil an entire lake with the datacenter’s heat, I will spend it with the greatest pleasure in the world. It’s my personal revenge against whoever forced me to use paper straws that dissolve in your mouth for all these years.”

I confess: it was deliberate provocation, written already knowing I was going to explain it here today. Because behind the joke there’s a serious thesis.

Last week I took apart here the AI fear propaganda from OpenAI, Anthropic, and NVIDIA. Today I want to use the same lens on a much older and much more profitable hysteria: the climate one. And there’s an irony in the middle: what unmasked this hysteria was AI itself, by accident.

The thesis is simple. The moment the datacenter race tightened for real, every “green” commitment from governments and big techs evaporated. The same Microsoft that promised to be carbon negative is restarting Three Mile Island. The same Germany that shut down its nuclear fleet now has a chancellor admitting it was a strategic mistake. What took down the climate agenda was another agenda, one with more money.

And before anyone labels me, my position in full: yes, there is a human effect on the climate. No, the world is not ending, and the distance between those two sentences is where the hysteria lives. What I dispute is the industrialized panic and the misallocation of trillions it justifies.

Every decade has its apocalypse

I grew up under the nuclear apocalypse of the Cold War. We lived with the certainty that the world could end in atomic mushrooms at any moment. When the USSR fell in 1989, that fear drained away, and the hole in the ozone layer filled the void. That one, by the way, is recovering decades after the 1987 Montreal Protocol, which phased out CFCs and friends: proof that a real problem with a cheap solution needs no hysteria.

Then came the Y2K bug. Then “global warming”. And when “warming” got hard to sustain in every headline, the agenda changed its name to “climate change”.

Notice the trick. Orwell called it Newspeak in “1984”: capture the words to capture the thinking. “Global warming” was a testable hypothesis: either it warmed or it didn’t. “Climate change” accommodates everything. Heat, cold, drought, flood, snow, lack of snow. Any event confirms the thesis, and a thesis that everything confirms explains nothing.

When the vocabulary is captured, the question becomes a crime. Whoever asks for evidence becomes a “denier”. A scientist funded by obscure interests becomes an “expert”, and “expert” gets sold as a monopoly on truth. The covid hysteria was the worst fruit of this way of thinking, but covid is for another article. The climate case is enough.

The human cost of this machine is measured. A 2019 Washington Post/Kaiser Family Foundation survey of American teenagers found 57% afraid and 52% angry because of the climate. A global UN poll that collected about 9.7 million votes asked about priorities: “action on climate change” came in last among 16 topics in the global total, and even in the poorest countries it sat at the bottom of the list. The people want hospitals and jobs. Whoever ranks the apocalypse first is whoever has their comfort guaranteed.

Follow the money: who funds the fear

About Greta Thunberg, one fact bothers both sides: she herself keeps none of the money. Her father told Politico she never received a cent, and she donated the entire 1-million-euro Gulbenkian prize to climate projects. The character is real and, by all accounts, unsalaried.

The girl and the startup

The machine around her is another story. The school strike started on August 20, 2018, a Monday. The Swedish startup We Don’t Have Time, founded by advertising man Ingmar Rentzhog, was still building its climate social network. On the day of the strike, Rentzhog posted the girl’s photo on his personal Facebook and the company account tweeted the same day, tagging Al Gore’s Climate Reality Project. Three days after the strike began, on Thursday, August 23, the family book, “Scenes from the Heart”, hit bookstores.

The startup’s fundraising prospectus, from November 2018, cited Greta 11 times, without the family’s knowledge; that round raised ~13 million Swedish kronor, and the company said it had raised 23 million in total by early 2019. She was a junior advisor to the company’s foundation from October 2018 to January 2019 and left. The product was the startup; the girl was the marketing.

The salaried protest industry

Then came the industrialization of protest. The Climate Emergency Fund, founded in 2019, funds Just Stop Oil, Extinction Rebellion, Scientist Rebellion, and friends. The one who opened the vault with $500,000 was Aileen Getty, heiress to the Getty oil fortune, mind you. Hollywood director Adam McKay came in with $4 million and a board seat.

By April 2022 the fund had already distributed $1.7 million to activists in 25 countries; the full-year tax filings point to ~$5 million in grants. Just Stop Oil alone received almost $1 million, helping pay the salaries of some 40 organizers. That soup on the Van Gogh canvas is salaried work, funded by oil inheritance and movie money.

Billionaire foundations and governments

Above the protest funds sit the big foundations. Bloomberg Philanthropies alone totals $1 billion in the Beyond Carbon campaign, and the New York Times called Bloomberg perhaps the biggest individual funder of climate activism in the world. The Hewlett Foundation committed $600 million over five years, Rockefeller announced in 2023 it would invest more than $1 billion in climate over five years, and ClimateWorks has already distributed more than $2 billion. That’s an industry, with the budget of a small country’s GDP.

And governments fund the pressure against their own political opponents. The EU pays €15.6 million a year in operating grants to some 30 green NGOs through the LIFE program, and in January 2025 the European Parliament held a formal debate about Commission-funded NGOs lobbying the Commission itself, in which the budget commissioner admitted it was “inappropriate” for parts of the Commission to sign agreements obliging NGOs to lobby specific MEPs. In Germany, Correctiv received €2.5 million in public money since 2016, the “Demokratie leben!” program had a budget of €182 million in 2024, and the CDU filed 551 formal questions about the political neutrality of state-funded organizations.

And who profits from the policies? Enron’s internal memo from December 1997, written from Kyoto, was already celebrating: “this agreement will be good for Enron stock!!”. The climate-industrial complex was born together with the treaty. Global revenue from carbon pricing, taxes and emission markets combined, hit a record $104 billion in 2023, and a 2008 Point Carbon study for the WWF estimated between €23 and €71 billion in windfall profits for generators in five EU countries in phase II of the European carbon market alone. And the biggest beneficiary of Western green targets has a name and an address: China controls more than 80% of every stage of the solar panel manufacturing chain and produced almost 75% of the world’s electric cars in 2025. The West subsidizes demand; Beijing sells the supply.

If you don’t know the Enron case, take a minute and watch:

Who wins

So who benefits from the fear narrative? Billionaire foundations, consultancies, subsidized industries, governments buying social peace with grants, and the West’s biggest industrial competitor. Greta walked away without a dime. The machine kept the megaphone.

The datacenter race

Some of the numbers I’m about to show come from the research I did for my talk at Tropical Ruby 2026, about energy and AI. The IEA data tells the story:

• Datacenters consumed 415 TWh in 2024, about 1.5% of the world’s electricity.
• In 2025: ~485 TWh, 17% growth in one year; AI-focused datacenters alone grew 50%.
• Central projection for 2030: ~945 TWh, the consumption of all of Japan, close to 3% of the planet.

In the US, datacenters account for almost half of all electric demand growth through 2030. And the money followed: big tech capex went from $400 billion in 2025 to ~$715 billion guided for 2026, more than the annual investment of the entire American energy sector. Cumulative datacenter investment between 2026 and 2030 is expected to hit $3.9 trillion.

And even that doesn’t cut it. The bottleneck stopped being chips and became copper and concrete:

• PJM, the largest grid operator in the US, closed for the first time in history a capacity auction below the reliability requirement, in December 2025: ~6.6 GW came up short. The capacity price jumped to $333.44 per MW-day, more than 11 times the level of the 2024/2025 auction, hitting the ceiling allowed by FERC.
• High-power transformers have a two-to-three-year waiting list, and the biggest models run past that. The American interconnection queue has more than 2,000 GW waiting, about 1.6 times the country’s entire installed capacity.
• Of the ~16 GW of large datacenters Sightline Climate forecast globally for 2026 (the American slice is ~12 GW), only ~5 GW were under construction at the start of the year, and the estimate is that 30% to 50% of that announced pipeline will slip.

The queue in the market now is for GPUs, power, and datacenters.

Remember the electric car, the green agenda’s great salvation? The entire global EV fleet consumed ~250 TWh in 2025, about 1% of the world’s electricity. Datacenters consumed almost double, already last year. And the projection tells the rest: from 415 TWh in 2024 to ~945 TWh in 2030, that’s +530 TWh of new load on the grid, almost three times the electricity of the entire 2024 global EV fleet (~180 TWh).

The entire decade of “green economy” was swallowed in two years of AI race. And the comic detail: in March 2026, Bernie Sanders and Alexandria Ocasio-Cortez announced the AI Data Center Moratorium Act, an attempt to ban new datacenters in the US. The green agenda spent decades telling you to consume less energy. Faced with AI, the coherent exit left over was trying to ban the new consumer.

The return of nuclear power

If the climate agenda were serious, its greatest ally was always nuclear power: the cleanest, densest, most reliable source there is. The same agenda that preached apocalypse treated nuclear as taboo for fifty years. Here’s what AI’s hunger for energy did in one year:

Big tech’s nuclear race

• Microsoft signed a 20-year contract to restart Three Mile Island’s Unit 1, 835 MW. Read that again: Three Mile Island, the ultimate symbol of American nuclear fear since 1979, resurrected as the Crane Clean Energy Center to feed datacenters.
• Meta locked in up to 6.6 GW in nuclear agreements with Vistra, TerraPower, and Oklo.
• Google ordered 500 MW in modular reactors from Kairos.
• Amazon expanded its contract with the Susquehanna plant to 1.9 GW through 2042 and invested in X-energy.

The technical reason is simple. In the US, nuclear runs at a capacity factor around 91%, and stayed above 92% on average for the decade through 2023; wind sits at ~34% and solar at ~23%. Datacenters want baseload, 24 hours a day. Sun and wind don’t deliver that without a mountain of batteries nobody has.

If you don’t know the 1979 Three Mile Island incident, watch:

The German case

And Germany? Germany shut down its last three plants on April 15, 2023 and became a net importer of electricity for the first time since 2002, including importing French nuclear. The Energiewende cost in the range of hundreds of billions of euros, German industrial electricity costs about double the American or more, and the German grid emits around 350 g of CO2 per kWh, against 22 g from nuclearized France.

Well then. In January 2026, Chancellor Friedrich Merz admitted in public: “It was a serious strategic mistake to phase out nuclear energy”, “we are now undertaking the most expensive energy transition in the entire world”. The head of the IEA itself, Fatih Birol, went public agreeing with him. Since May 2025 Germany stopped sabotaging nuclear France in the EU, and in June 2026 former bosses of the closed plants formally asked for the reactors to be reactivated.

Let me put my opinion on record here: closing the nuclear fleet was one of the biggest mistakes in Germany’s modern history. They impoverished their own industry, got hooked on Russian gas, went back to burning coal in the 2022 crisis, and today buy nuclear electricity from the neighbor. All of it in the name of an agenda they themselves are abandoning.

Fear versus the numbers

About the fear itself, the official numbers say otherwise. Deaths per TWh produced, counting accidents and pollution: coal kills 24.6; oil, 18.4; gas, 2.8. Nuclear: 0.03, less than wind and almost equal to solar. Chernobyl killed 30 workers directly, and the Chernobyl Forum and WHO projection for eventual cancers sits at up to 4,000 among the 600,000 most exposed, while Greenpeace was selling 93,000. The same report concluded the biggest public health problem was the psychological damage: people got sick from fear of radiation, not from radiation. Fukushima: zero radiation deaths among the public (Japan recognized a single radiation-linked death: a worker with lung cancer, in 2018); 2,313 deaths linked to the panicked evacuation. Fear killed more than the accident.

And the waste? All the used fuel from America’s commercial reactors fits on a football field, stacked less than 10 yards high, about 9 meters. France has been reprocessing its own at La Hague for decades. Finland built the world’s first deep geological repository for used nuclear fuel, Onkalo, already in the final stretch of licensing. A technical problem solved long ago; what was missing was political courage, and it arrived in the form of a datacenter power bill.

The man who’s been pointing this out for twenty years

The person who has organized this argument with rigor for two decades is the Dane Bjorn Lomborg. A political scientist by training, a statistics professor for years at the University of Aarhus, former visiting professor at Copenhagen Business School, fellow at Stanford’s Hoover Institution, founder of the Copenhagen Consensus Center, a think tank that works with hundreds of economists, Nobel laureates included. He blew up in 2001 with “The Skeptical Environmentalist” and became persona non grata of the green establishment, despite repeating the same sentence since forever:

“Global warming is real, but it is not the end of the world. It is a manageable problem.”

Take note: Lomborg accepts the scientific consensus on the existence of warming and its human cause. He defends a carbon tax and investment in green R&D. What he contests is the panic and the math of the policies. His 2020 book, “False Alarm”, is a catalog of numbers the headline never tells you:

The numbers the headline doesn’t tell you

• Deaths from climate disasters (drought, flood, storm, wildfire, extreme temperatures; Lomborg’s compilation over the EM-DAT database): from ~500,000 per year in the 1920s to fewer than 20,000 per year today, a 96% drop. Since the population quadrupled, individual risk fell 99%.
• In Lomborg’s synthesis of the Stanford Energy Modeling Forum models, complying with the Paris Agreement costs between $1 and 2 trillion per year, the most expensive pact in human history. And the effect of fulfilling every 2030 promise, measured in the MAGICC model, the same one used by the IPCC, is to reduce 2100 warming by 0.048°C, in his paper in Global Policy. Stretching every promise to 2100: 0.17°C. Every dollar spent on Paris avoids 11 cents of damage.
• The cost of warming with no action at all, in the damage function of Nobel laureate William Nordhaus: about 3 to 4% of global GDP in 2100. Lomborg applies this to the IPCC income scenarios and translates: instead of having 4.5 times today’s income, we’ll have 4.34 times. A manageable problem.
• New Zealand calculated that its net-zero costs between 5% and 17% of 2050 GDP, against the previous target of cutting 50% of emissions. Lomborg converts the country’s slice of emissions, by his math, into a three-week postponement of global warming.
• Tuvalu, the poster country of “it’s going to vanish into the sea”, grew 2.9% in area over four decades with the sea rising at double the global average. And the “187 million displaced by 2100” headline comes from a study for up to 2 meters of sea level rise with no adaptation; the same study, with dikes and realistic adaptation, calculates between 41,000 and 305,000 displaced.
• A study in The Lancet that analyzed 74 million deaths in 13 countries attributed 7.3% of them to cold and 0.4% to heat: in the current climate, cold kills some 17 times more.

The math nobody lets you do

Now the comparison that matters. The Copenhagen Consensus’s “Best Things First” calculated: $35 billion per year on twelve cheap solutions (treating tuberculosis, malaria bed nets, child nutrition, vaccines) would save ~4 million lives per year. Treating tuberculosis costs $6.2 billion a year and saves ~600,000 lives a year this decade, a return of ~$46 per dollar. Malaria alone killed 610,000 people in 2024, 95% in Africa, most of them children under 5. A delivered treated net costs about six dollars; saving a life with them costs around $5,500.

International climate aid alone, a quarter of all development aid, would be enough to fund tuberculosis, malaria, contraception, and nutrition in the entire world and still leave change. That’s the real choice on the table. Nobody lets you do this math out loud without being called a monster.

His 2005 TED talk, “Global priorities bigger than climate change”, is exactly this cost-benefit exercise, done on stage, twenty years ago. Worth the 16 minutes:

And since the comments will aim at the messenger, let me get the rap sheet out of the way early. About Lomborg you’ll hear that a Danish “scientific dishonesty” committee declared his first book objectively dishonest in 2003. True. And Denmark’s Ministry of Science overturned the decision at the end of the same year, over flaws in the process. You’ll also hear about Scientific American’s eleven-page hostile dossier. All on record. And none of it touches the numbers, which come out of the IPCC, the WHO, the FAO, and EM-DAT.

Thirty years of summits, and the score is zero

Before China, it’s worth looking at the full track record of the process that produced these policies. Three decades of treaties, each announced as historic, each with the same result:

The treaties

• Rio, 1992. The UNFCCC is born, with the written goal of “stabilizing greenhouse gas concentrations in the atmosphere”. Result: global fossil CO2 went from 22.5 gigatonnes in 1992 to 37.4 in 2024, +66%. At Mauna Loa, atmospheric CO2 went from 356 ppm to 427 ppm, with no visible dent in the growth curve.

• Kyoto, 1997. Target: industrialized countries would cut 5.2% below 1990 by 2012. The US Senate preemptively rejected the idea 95 to 0 before the final text even existed. Canada left the treaty in 2011 when it became clear it would miss the target. Global emissions rose 43% while it was in force. The countries that “complied” complied for free: the post-Soviet industrial collapse and the 2008 recession did the work. And Kyoto’s second period, the Doha one, covered 37 countries and ~14% of global emissions and entered into force on December 31, 2020: the last day of the period it governed.

• Kyoto’s credit mechanism, the CDM, became a fraud factory: industries in China and India produced HFC-23, a greenhouse gas 14,800 times more potent than CO2, just to destroy it and sell credits, a €2.6 billion scheme. The Öko-Institut, auditing for the European Commission itself, found that 85% of projects had a low likelihood of integrity. A 2024 study in Nature Communications calculated that fewer than 16% of the carbon credits analyzed correspond to real reductions.

• Copenhagen, 2009. The goal was the binding successor to Kyoto. It collapsed live: the final text was taken note of, not adopted, drafted on the last night by the US, China, India, Brazil, and South Africa. What survived was the promise of $100 billion a year for the poor world, met for the first time in 2022, two years late.

• Paris, 2015. The target system is self-assigned and the targets are not legally binding. Of the 197 parties, only 58 had economy-wide targets in domestic law or policy, and only 17 with ambition equal to or greater than what was promised on paper (Grantham Institute, via Lomborg). Nature wrote in 2017 that every major industrialized country was falling short. The US left, came back, left again (effective January 2026) and, in January 2026, announced its withdrawal from the UNFCCC itself, the 1992 convention, something no country had ever done. Since Paris, emissions rose from 35.4 Gt to 37.4 Gt, every year, except for the covid dip.

• Belém, 2025 (COP30). The house was ours, and the result was a portrait of the state of the process. Before it opened: delegations from Africa and the Pacific formally complaining about the lodging crisis, with stratospheric hotel rates and part of the delegations sleeping on cruise ships. During: a fire in the blue zone evacuated the delegates in the middle of the final negotiation, with 13 people treated for smoke inhalation. It was the first COP without an American delegation in 30 years, and Trump mocked the event on Truth Social, the highway through the rainforest and all. Attendance: ~42,000 in person, half of what Dubai had. And what came out of it? A text called “Global Mutirão” (mutirão is Portuguese for a collective work effort) with the fossil fuel phase-out plan ripped out after pressure from Saudi Arabia and allies, despite more than 80 countries backing it, and a promise to triple adaptation finance already born weakened, from 2030 to 2035. The bill for the Brazilian taxpayer: ~R$4.7 billion in the federal package alone (Union Budget, BNDES, and Itaipu), not counting what the state and the city spent on their own and never consolidated in public. One more circus paid for with our money, with nothing to show at the end.

The COP circus

And there’s the circus itself, which isn’t just ours. COP28 in Dubai had 83,884 in-person participants, including 2,456 fossil fuel lobbyists, which perhaps explains the final texts. Glasgow had 118 private jets; COP27, some 315 executive jet trips. And the cherry on top of our edition: Avenida Liberdade, 13 km of road cutting through Belém’s Environmental Protection Area, with 68 hectares of vegetation cleared (the project was the state’s and predates the COP, but the cut in a protected area is fact).

The scoreboard of 32 years of treaties: +68 ppm of CO2 in the atmosphere, zero deceleration. The emissions curve doesn’t know the treaties exist.

Patient zero of the hysteria: Al Gore

Before the treaty machine, the propaganda had a founding father. Al Gore lost the 2000 election and reinvented himself with “An Inconvenient Truth” (2006): an Oscar, the 2007 Nobel Peace Prize shared with the IPCC, and the template for every hysteria that came after. Practically every apocalyptic prediction you hear to this day premiered in that film.

The one who dissected the film claim by claim was Roy Spencer: PhD meteorologist, former senior climate scientist at NASA, co-creator with John Christy of the UAH satellite temperature dataset, the independent reference for global temperature. His position is the “lukewarmer” one: CO2 warms a little, the theory is solid up to there; the catastrophe is the exaggeration. In 2017 he published “An Inconvenient Deception” (Kindle on Amazon), which I read for this article. Gore’s catalog of busted prophecies, with Spencer’s data:

The catalog of busted prophecies

• “Ten years to the point of no return” (2006). Ten passed, almost twenty passed. The trend measured by Spencer’s own satellite: ~0.16°C per decade, through June 2026.
• Arctic summer ice “could be gone as early as 2014”: the line is from his Copenhagen speech, December 2009, citing a modeler who later disowned the number. It didn’t vanish.
• Kilimanjaro’s snows by 2016: still there.
• Stronger and more frequent hurricanes after Katrina: what came was a drought of almost 12 years without a single major hurricane touching the US, from Wilma in October 2005 to Harvey in August 2017, the longest since 1850. NASA researchers calculated that a gap like that happens once every 270 years. And the global cyclone count has no upward trend since the 1970s.
• “Up to 20 feet of sea level rise”: the measured global rate today is ~3.4 mm per year, about 1.4 inches per decade. The Battery Park tide gauge in Manhattan measures 1.16 inches per decade since 1856, and part of that is the land sinking. At this rate, 20 feet takes more than a millennium.
• The drowning polar bears: the film’s animated scene was born from a short note in Polar Biology about four bears found dead after a storm in 2004. The estimated global population sits between 22,000 and 31,000, probably larger than in the 1970s, when no reliable estimate even existed and the real threat was unregulated hunting, solved by the 1973 treaty. Twenty years later, “the bears are dying” still circulates as fact.
• The Syria drought, “the worst in 900 years”, said to have caused the civil war: rainfall data for the west of the country, Aleppo, Damascus, Homs, stayed above average; the strong 2007/08 drought concentrated in the northeast. What broke the countryside was the government tripling the price of diesel in 2007-2008, which made pumping well water unviable.
• The flooding of the 9/11 memorial by hurricane Sandy, presented in the sequel as “I told you so”: in the original film he talked about gradual sea level rise, never storm surge. Spencer calls it explicit deception.
• The tabletop experiment with Bill Nye, heating CO2 in glass jars in the Climate Reality Project’s “Climate 101” video (2011): Anthony Watts’s analysis showed the experiment doesn’t work and the result was faked in post-production. According to Spencer, the video stayed on Gore’s site for years afterward.
• The crops: the 42 climate models used by the UN predicted summer warming in the American Corn Belt; NOAA data shows no significant warming there since 1960.

The cast of the prophecies is going strong. The bears, too:

The character

And the character: Spencer documents that Gore got rich on “green” investments (he’s a cofounder of Generation Investment Management), keeps mansions and an industrial-sized carbon footprint. And even the establishment didn’t swallow the film whole: in 2007, the British courts found nine errors in “An Inconvenient Truth” and called the sea level part “distinctly alarmist”. Spencer’s word for Gore’s technique is “wordsmithing”: writing in a way that leads the audience to conclude what he never stated in so many words.

The audience, by the way, had already tuned out: in the first weeks of the 2017 sequel, the screening Spencer attended in a 750-seat theater had three people, and one left halfway through.

If this subject got to you, the two bedside books are Spencer’s, above, and Lomborg’s “False Alarm”, which I used in the previous section. One dissects the propaganda; the other dissects the bill.

Spencer’s rap sheet also comes ready-made in the comments: he’s one of the favorite skeptics of Republicans in Congress, the UAH dataset had famous corrections in the 90s and 2000s (published and incorporated decades ago), and a 2011 paper of his in Remote Sensing was so contested that the journal’s editor resigned, saying the article had slipped through by mistake. All real. And none of it changes the Battery Park tide gauge or the calendar of expired prophecies. Criticize the numbers, if you can. The messenger is just the messenger.

The arithmetic of insignificance

Let’s get to your electric car and your paper straw. China accounted for 32% of global fossil CO2 in 2024, more than the US, the EU, and India combined. China started construction on 94.5 GW of new coal plants in 2024 alone, a ten-year record, and burns more than half of all the planet’s coal every year since 2011.

Now your individual actions, with the numbers:

• The entire global EV fleet displaced ~1.3 million barrels of oil per day in 2024, something like 0.5% of the world’s fossil CO2. That’s the total size of the impact of the biggest green policy of the decade.
• Lomborg calculated in 2020: an average subsidy of $10,000 per EV to save ~8 tonnes of CO2, worth $48 in the RGGI, the carbon market of the American Northeast states, at the time. You paid ten grand for 48 dollars of carbon.
• Lomborg’s math, with IEA data from the time: an EV emitted ~26 tonnes of CO2 over its lifetime against ~34 for a gasoline car, a 24% cut. And it depends on the grid: the Volvo XC40 study calculated the battery payback at 146,000 km driven on the global average electric mix (84,000 km on the European mix, 47,000 km on wind power). On a coal-powered grid, it’s worse.
• Paper straws: even if every estimated straw on the coasts ended up in the sea in one year, they’d make ~0.03% of the annual plastic entering the ocean.
• Household recycling: of all the plastic that became waste in history through 2015, only 9% was recycled; 12% incinerated; 79% went to landfill or into nature (Geyer et al., Science Advances 2017). And plastic doesn’t recycle in a loop: each cycle degrades the polymer, it becomes a lower-quality product, and it postpones the landfill by one step. The worst part: NPR showed that the plastics industry knew since the 70s that recycling didn’t pay and sold the symbol anyway, so you’d keep buying guilt-free. The exception that proves the rule is aluminum: recycling uses 5% of the energy of producing from scratch, and Brazil is the world champion at it, with 97.3% of cans recycled in 2024. Recycling works when the physics and the economics close. When they don’t, it’s kitchen liturgy.

Your paper straw doesn’t show up in this math. Neither does your EV. The math is dominated by whoever builds 94 GW of coal plants per year.

These gestures are liturgy, and this is where their real damage lives. The paper straw gives that feeling of “at least I’m helping”, and that feeling numbs. The person makes the micro-sacrifice, feels part of saving the world, and closes their eyes to the actually big problems, like the children dying of malaria in Africa I showed two sections back. Objectively, the gesture moves nothing. Subjectively, it buys the conscience. It’s worse than useless.

And Lomborg’s complete thought is even more brutal: if the rich world zeroed all its emissions and stayed at zero for the rest of the century, the temperature increase in 2100 would drop 0.7°F, less than half a Celsius degree, in his math with the MAGICC model. The West’s total sacrifice is a rounding error.

The world doesn’t fit in your head

Part of the hysteria exists because most people have no sense of the planet’s physical scale. Let’s fix that real quick, in human language.

The Sun. The Earth continuously intercepts ~173,000 terawatts of solar energy. All of civilization consumes ~20 TW. The ratio is more than 8,000 to 1, and the Earth captures only one part in 2.2 billion of what the Sun emits. A single large coronal mass ejection carries billions of tonnes of plasma with energy in the range of 10^25 joules, something like fifty million Tsar bombs. In 1859, the Carrington Event set telegraphs on fire and drew auroras in the sky over Cuba. No human control panel participated.

The Moon. Tides are the Moon deforming the ocean: a bulge of water on its side, another on the opposite side. The lunar effect on the tide is ~54 cm; the Sun’s, ~25 cm. In the Bay of Fundy, the tide swings 16 meters, and each flood tide brings in ~160 billion tonnes of water, more than all the rivers in the world combined in the same period. Meanwhile, the Moon drifts away 3.8 cm per year and the Earth’s day gets longer by some 2 milliseconds per century. Some 620 million years ago, the day was almost 22 hours.

El Niño. Normally the trade winds push the Pacific’s warm water west. Every 2 to 7 years they weaken, the warm water slides east, and the global average temperature rises 0.1 to 0.2°C for a year or two, on its own, no cars, no industry. La Niña does the opposite. When you see a “hottest year in history” headline, check whether there was an El Niño: there almost always was.

New York and Lisbon. New York sits at 40.7°N. Lisbon, at 38.7°N; Madrid, at 40.4°N. Same latitude. New York buries itself in snow every winter; Lisbon and Madrid have mild winters by comparison. The explanation: the westerly winds throw moderate air across the Atlantic onto Europe, while the American east coast receives freezing continental air from the Arctic, with the Atlantic transporting ~1.2 petawatts of heat north via the meridional current of which the Gulf Stream is part. Iceland, at 64°N, has winter averages comparable to New York’s. Latitude rules far less than current and wind.

The Sahara fertilizes the Amazon. Every year, ~182 million tonnes of dust leave Africa crossing the Atlantic; ~28 million tonnes fall on the Amazon basin, carrying ~22,000 tonnes of phosphorus, almost exactly what the rain leaches from the forest’s soil each year. The world’s biggest desert feeds the world’s biggest forest, and it’s been happening for millions of years before you were born.

And if you zoom out the time scale, it gets even more absurd:

The Sahara was once green. Between ~11,000 and ~5,000 years ago, the Sahara was savanna with lakes, hippos, and crocodiles, and the Cave of Swimmers, in Egypt, holds rock art of people swimming in what is now desert. The driver was the orbital precession cycle, and the flip to desert took decades to a few centuries, depending on the region. And it comes back: the next orbital summer maximum arrives in some 10,000 years.

The Earth was once a snowball. Twice between ~717 and ~635 million years ago, the Sturtian and Marinoan glaciations took ice all the way to the tropics: there are glacial deposits at tropical latitudes to prove it. The Earth got out of it on its own: the global ice shut down the process that pulls CO2 out of the air, volcanoes stacked CO2 for millions of years up to hundreds of times the current level, and the greenhouse effect beat the ice.

The Mediterranean once dried up. Between 5.97 and 5.33 million years ago, the Mediterranean Sea nearly evaporated and left layers of salt more than a kilometer thick on its bed. When the Atlantic broke through at Gibraltar, the flood filled the sea back up with 90% of the volume in months to two years, at a flow estimated at a thousand times the Amazon’s.

Sharks in Kansas. In the Cretaceous, with the sea level 100 to 200 meters above today’s, a shallow sea split North America in two, from the Gulf of Mexico to the Arctic. Mosasaurs and sharks swam where Kansas is today.

The plants almost starved. At the last glacial maximum, CO2 fell to ~180 ppm, dangerously close to the ~150 ppm range where C3 plants start to fail. Glacial-era trees preserved in the La Brea tar show physiological carbon starvation. Plants evolved in a world with much more CO2, and today the planet is greening: the Zhu et al. 2016 study, publicized by NASA, measured greening across 25 to 50% of vegetated areas between 1982 and 2009, with ~70% attributed to CO2 fertilization.

CO2 became a synonym for a dangerous gas that must be fought at any cost. For plants, it’s food. When CO2 rises, green life tends to rise with it. If CO2 were poison, there would be no plants.

Rainforest in Antarctica. In 2020, Nature published the discovery of intact roots of a temperate forest that grew at 82°S, some 900 km from the South Pole, ~90 million years ago, with a mean annual temperature of 12 to 13°C. New Zealand climate, in Antarctica.

The climate has always changed

“Climate change” is an oxymoron. Changing is what the climate does, since forever, without asking permission.

For most of Earth’s history there was no permanent ice at the poles. The planet’s normal condition is greenhouse, no polar ice. In the Cambrian, CO2 was 10 to 20 times the pre-industrial level. At the peak of the Cretaceous, the global average was some 10°C above today’s, or more. Antarctica only gained permanent ice 34 million years ago.

And the great die-offs, which the hysteria loves to invoke, happened five times before any industry:

• ~444 million years ago (Ordovician): ~85% of species, glaciation.
• ~372 million (Devonian): ~75%, ocean anoxia.
• ~252 million (Permian, “the Great Dying”): ~96% of marine life, volcanism.
• ~201 million (Triassic): ~80%, volcanism.
• ~66 million (K-Pg): ~76%, the dinosaur asteroid. That one’s just the most famous.

On the more recent scale: the current ice age began 2.58 million years ago, with ice cycles every ~100,000 years in the last million of them. 20,000 years ago, hundreds of meters of ice covered where Manhattan is today, and the sea level was 120 meters lower. 11,500 years ago, at the end of the Younger Dryas, Greenland warmed ~10°C in about a decade. Then came the Medieval Warm Period, the Little Ice Age with the Thames freezing until 1814, and the “year without a summer” of 1816, courtesy of the Tambora eruption. All of it without a single coal plant.

To be clear about what I’m saying: none of this proves the current warming doesn’t exist, and the current speed of the CO2 rise is indeed faster than the natural precedents. Lomborg himself says so. What this scale destroys is the phrase “end of the world”. A planet that has already been 10°C warmer with the sea 120 meters lower is not in its death throes. The serious discussion is about the cost-benefit of each policy, which is exactly the discussion the hysteria forbids.

The hysteria died of natural causes: money

Fear doesn’t save the planet, doesn’t help the environment, and doesn’t improve anyone’s life. Fear sells narrative, funds consulting, elects politicians, and stuffs foundations with money. The cost of this era is right there: trillions torched on gestures that don’t move the thermometer, while 4.9 million children died in 2024 before turning 5 from diseases the developed world forgot exist.

And then AI came along and closed the subject by accident. Notice how nobody talks about COPs in the headlines anymore. The conversation now is datacenter gigawatts, 20-year nuclear contracts, gas turbines. The same CEOs who signed climate letters are fighting over power plants. The same governments that imposed paper straws approve reactor subsidies. The climate agenda left the stage without being refuted, evicted by a bigger agenda.

If the end of the world were really on their agenda, you wouldn’t be watching Three Mile Island being restarted to train chatbots. You’re watching it. It was theater, and the theater closed when a more profitable show opened in the room next door.

Fear doesn’t save the planet. Fear sells. And whoever bought the fear is now buying megawatts.

And the recommendation comes right up front, because it’s the reason this article exists: don’t use other people’s skills. Read that again. My skills are published on my my-skills repository and the repo description warns you: “not tailored for general usage”. They’re there so you can read the reasoning, understand the structure, and go build your own. Copying somebody’s skill and running it blind is the digital equivalent of signing a contract without reading it. Worse: it’s installing someone else’s instructions to run with your permissions, on your repositories, with your tokens. That’s prompt injection you install voluntarily and say thank you for.

What a skill really is

No magic involved. A skill is a directory with a SKILL.md inside: a header with name and description, and a body of text that is, essentially, a well-written prompt about how to execute a task. Sometimes it ships with a scripts/ folder of helper scripts, so the LLM doesn’t have to recreate them from scratch every time.

Skills are just prompts.

That’s it. A prompt you versioned, refined, and kept one command away. The only difference between a skill and that giant prompt you paste into the chat every day is that the skill lives in a file, with a name, and the agent knows it exists.

And here’s the answer to the question everybody asks: “doesn’t that fill the context with junk?” The good news is the harnesses do this right. Claude Code uses what Anthropic calls progressive disclosure: at session start, only each skill’s name and description go into the system prompt. The SKILL.md body only loads when the agent decides to invoke the skill. OpenCode does the same: lists name and description, loads content on demand through its own tool. Anthropic itself documents the design: metadata first, body only when needed.

That solves the text cost, but only half the problem. Every installed skill still takes a line in the listing, and more importantly: it competes for the agent’s attention when it decides which tool to use. Anthropic again, on the engineering blog: “more tools don’t always lead to better outcomes”, and their official recommendation is a few well-designed tools for high-impact workflows. My version of that rule is more acidic:

An idle skill is debt.

You change your process, the skill ages, and one day the agent executes a rule that no longer makes sense in your workflow. Fewer skills, better written, reviewed often.

The only third-party skill that survived here

Having said all that, it would be hypocritical to pretend I live on homemade skills alone. One survived my scrutiny and runs on every post on this blog: Humanizer.

It exists because the Wikipedia community maintains a public, brutally honest catalog called Signs of AI writing, kept by WikiProject AI Cleanup, documenting the “tells” of AI-generated text one by one: the rule of three, the “not just X, but also Y”, the puffery, the emoji-decorated bullet. Humanizer turns that catalog into a procedure: the agent receives the text, marks the tells from strongest to weakest, rewrites preserving every supported claim (losing a fact is an error, inventing a fact is an error), and does a final read-aloud check. Without changing what was said, only how it was said.

If you produce content, you know why I insist: text that smells like AI is becoming the new “Dear Sir”, the mark of a rushed form letter. I run Humanizer on every article before publishing, including this one. But note the detail: I merged version 3.0.0 into my repo, with my own adjustments. I read the source material before trusting it. That’s the pattern.

What I do with this every day

Now the practical part. I maintain and monitor over 40 repositories on GitHub. My own projects, forks I maintain, tools that became daily dependencies. To stay sane, my second monitor runs tclock (my fork of clock-tui, with Evangelion themes, because why not) and next to it ghpending, a Rust CLI that lists open issues and PRs from all my repos at once.

In the photo you can see: ai-memory and ai-usagebar with open PRs and issues. Every morning I open the harness on each project with ai-memory run in its directory. ai-memory deserves a paragraph: it’s my long-term memory project for agents, a persistent Markdown wiki that captures session observations, consolidates decisions and gotchas into pages, keeps workstreams across sessions and handoffs between agents. Every project of mine has its own memory, with the architectural decisions and the traps only that codebase has. Without it, every agent session starts from zero like an intern with amnesia.

From there the daily ritual is almost childishly simple. I write, literally:

run pr-audit and iss-audit, then run github-resolution

When a release is ripe, I add then run the release skill. On days when the only queue is Dependabot’s automatic bumps, it’s run pr-bump and release and done. One line of instruction from me. The heavy lifting lives in the skills. Let’s get to them.

pr-audit

PR audit before merge, and the central principle is: evidence over narrative. Nothing the contributor wrote is treated as truth; everything is a claim to verify, with a claims ledger (confirmed, partial, unsupported). The rest of the skill is a hostile checklist:

• Change gate: hunk inventory looking for executable bits, symlinks, bidirectional characters, homoglyphs, pull_request_target workflows, unpinned actions, typosquat dependencies, lockfile drift.
• Credentials: unexplained credential access is an instant blocker. Execution happens in an isolated worktree, with no host credentials.
• Semver: the changelog category is a version gate; breaking changes go in major, never in patch. A rule that has saved me more than once.
• Prompt injection: text inside a PR trying to “change the audit rules” is just one more suspicious data point to investigate. It’s an audit finding, obviously.

iss-audit

The same distrust, applied to issues:

• Never run a command copied from an issue. Attachments only go through an isolated scanner.
• Formal separation between what was observed, what was expected, the reporter’s diagnosis, and the proposed fix.
• Reproduction with synthetic data in a disposable sandbox, with resource limits for DoS claims.

And two golden rules I love: “a number that moves is not a pinned number” (async lag looks like a bug) and “the obvious owner may be innocent” (query the suspect population; if it comes back empty, the cause is elsewhere).

github-resolution

The execution phase: turning what the audit approved into merged code. The rules:

• Only execute approved tickets, one at a time, with a regression test written before the fix. “Slop is a defect”: no speculative abstraction, no TODOs, no drive-by refactors, no weakened tests.
• If I resolved more than three tickets in a session, run the post-audit over the whole range before pushing, no exceptions.
• A ticket closes when the fix is merged with green CI on that exact SHA, never left hanging waiting for a release.
• Every unresolved issue gets logged with the reason. Compressing it into a number is forbidden.

pr-bump

The fast path for Dependabot’s dependency bumps. The rule that saves me the most time: batch, never serialize. Instead of merging PR by PR, one consolidated bundle update to the latest compatible versions, a single CI pass for the whole set, fix forward on whatever breaks.

But there’s a supply-chain floor:

• Every dependency must resolve on the public registry (rubygems, npm, pypi) with the expected name, version, and checksum.
• Git source, path source, typosquat-adjacent name, new install hook: full stop, it goes to a complete audit.

bundler-audit will happily pass a well-formed trojanized gem. That’s exactly the case the floor covers.

release

Cut a version only when I ask. The iron rules:

• The version comes from the changelog classification: fixes go in patch, additive changes in minor, breaking in major. A number that conflicts with the classification forces me to confirm; silent bumps are forbidden.
• Green CI gate on the tag’s exact SHA. Never tag with pending or skipped CI.
• Annotated tag, updated changelog.
• Never rewrite a published tag. Never cut a release nobody asked for.

The numbers, because numbers don’t argue

Counting right now, on the day I write this article, across just three projects (ai-memory, ai-usagebar and ai-jail):

• ai-memory: 430 merged PRs, 260 closed issues
• ai-usagebar: 137 merged PRs, 34 closed issues
• ai-jail: 42 merged PRs, 81 closed issues

Total: 609 merged PRs and 375 closed issues, over a thousand items. Nearly all of that was handled by these skills, with me supervising and deciding. One person, no team, keeping three active projects at this volume. That’s the multiplier.

And they weren’t born ready

Open the my-skills commit history and you see skills being polished the hard way. July starts the repo with the basic audits. Late July: “harden audit skills and add security audit”. August: “consolidate agent skills from all harnesses into this repo” and, days later, github-resolution is born as the execution phase of the audits. September: “fold in release-discipline and audit lessons”.

And just yesterday, look at the level of fine-tuning in pr-bump: “batch all bump PRs into one consolidated verification pass, fix forward on failures” and “require deferred/declined PRs be left in a clean state”. Two rules that became skill text. Neither came from a blog post or somebody’s opinion: they came from me face-planting into the exact error the rule now prevents.

Every line in these skills is a scar.

That’s how you write a good prompt: watch the result, decide to adjust, adjust again.

Skills are little. Knowledge is a lot.

Now the counterpoint, because I don’t want you leaving here thinking skills are the answer to everything. I depend on them very little. What actually carries my workflow is something else.

Every project of mine has its own AGENTS.md, instructions specific to that code, and its ai-memory memory with decisions and gotchas. And the pattern I use most day to day isn’t even a skill: it’s pointing at a living example. When I wanted automated AUR packaging for clock-tui, I didn’t write a “how to make an AUR package” skill. I opened the harness and said:

make a similar github action to build and publish AUR packages
as we did in ~/Projects/ai-memory

The agent went there, read the workflow that works, and reproduced the pattern in the new project. A skill for that would be worse: AUR changes, GitHub Actions changes, the example gets stale. A living example in a repo I maintain is always up to date by definition, because when it breaks, I fix it.

Every project becomes its own knowledge base.

And this generalizes, because harnesses can consume any text base. My entire Linux PC is documented and reinstallable from a config repo of mine, and my homeserver is fully described in runbooks. Those two are not on GitHub: they live on a private Gitea server I maintain, because that’s the kind of thing I don’t want leaking in public. When I want to change something at home, I say:

add integration to my Plex server as documented in my homelab runbooks

That’s not a skill. It’s organized, versioned, searchable knowledge. Skills are for procedures that repeat the same way. Knowledge is for everything else.

The recommendation worth more than any skill

If you take a single practice from this article, take this one: have your agent write down everything that didn’t become code. The research it did, the decision you made and why, the comparison with the rejected alternative. Without that, the knowledge evaporates at the end of the session.

My ai-memory, for instance, has a docs folder with competitor research I commissioned before every big decision:

• The Karpathy wiki pattern, the project’s intellectual origin.
• A 2026 landscape survey, covering Zep/Graphiti, Letta, Mem0, and Google’s Open Knowledge Format.
• Deep dives on each competitor: Hindsight (the one with a paper and funding, which ended up validating my design bets), cognee (pretty graph pipeline, ugly gateway bug), agentmemory (I kept the ideas, threw out the substrate), basic-memory.
• And my favorite, the MemPalace autopsy: 47k stars, viral, and a tracker clogged with index corruption and lost data. The fairy tale in reverse that confirmed for me why compiling memory beats stockpiling verbatim.

Dozens of documents like that. Every hour of research an agent did for me became a permanent asset, reusable in any new project. The more you produce and document, the more intelligence you accumulate.

It’s compound interest for knowledge.

Nobody knows what you need

And there the circle closes, back to where we started: why I don’t recommend copying anybody’s skills, mine included.

Because my PR audit skill is molded around my projects, my languages, my risks, my level of supply-chain paranoia. It reflects what I, Fabio, maintaining the repositories I maintain, on the infra I have, with the goals I have, needed to write into it. Your project has another stack, other risks, another tolerance level. You may need checks I don’t, and vice versa. Experience is only relevant to whoever lived through its constraints. That changes from person to person, and that’s exactly why a third-party skill used blind is a gamble.

That’s the thesis of my 2019 video, Don’t Outsource Your Decisions: the most important lesson of your life (in Portuguese): nobody but you knows what you need.

I don’t ask anybody. I research, I build mine, and the skills are the versioned record of my decisions. The beauty of LLMs is they speed this process up tenfold: the research that took a week now takes an afternoon, and the cost of building your own tool fell through the floor.

A good skill is your own. Mine are on my-skills so you can read the reasoning. Then close the tab and write yours. I don’t waste time asking for other people’s opinions. Neither should you.