TypeSafe’s Jev does not write sentences. Given a situation and a list of questions, it returns a probability for each answer — one pass, no generated text — and Vercel reports it was adopted faster than any other model in AI Gateway history. John Berryman, who worked on Copilot at GitHub, argues OpenAI is well positioned to fast-follow it, and better positioned to fold classification into its existing models than TypeSafe is to defend it. ...
Peter Yang spent most of 2026 defaulting to ChatGPT because Claude Opus had become judgmental and started every answer with “here’s the honest truth.” He put the new Opus through five practical tests on his own channel, from 3D scene generation to editing his videos, and ends with the personality prompt that Opus 5 bombed. ...
Nathan Barry starts from a line in the paper Language Modeling is Compression: every prediction model is inherently a compressor, and every compression algorithm is a prediction model. He then takes the least likely candidate he can find — gzip, the archiver that ships with your operating system — and asks whether it can write Shakespeare. No weights, no training, no neural network. The 122-comment thread on Hacker News supplies the prior art he left out, one sharp correction, and the question the post never answers. ...
GPUs are commonly depreciated on the assumption that each new NVIDIA generation renders the previous one obsolete. Ornn Data — a firm that publishes GPU rental indices and also rents GPUs — argues the other way: open-weight demand gives older silicon a class of workloads it can still serve cheaply, and the contract-price curve is already saying so. The chain starts with how closed models are sold. Access runs through subscription allowances the provider can reset, so the posted token rate card is the marginal price of additional usage, not a commodity price that falls on its own. Open weights remove the gate: the same checkpoint can be served by anyone. September OpenRouter snapshots Ornn cites listed eighteen to twenty-two providers for several widely served open models, with highest-to-lowest output prices spread 1.8x to 5.6x. The paper’s framing of the real distinction is that “the economic distinction is who can make the deployment decision.” ...
Maharshi Patel spent a day at a conference about MCP and came away wanting the whole layer gone. MCP — the Model Context Protocol — is the standard Anthropic shipped in November 2024 so AI agents could plug into outside services and data; it is how an agent gets a “tool” it can call. His argument is a timing one: it was designed for models that were not yet good at the job themselves, and those models no longer exist. The 168-comment thread on Hacker News mostly agrees with the diagnosis and rejects the prescription. ...
Baldur Bjarnason wrote this essay in July 2023; it resurfaced on Hacker News this week. His question is why so many people come away from a chatbot convinced they have been talking to something intelligent, when nothing in a model of language should produce that impression. His answer: the illusion lives in the user, and it works exactly the way a psychic’s cold reading works — by accident. The con, and its six stages ...
Chester Wisniewski learned to program by typing BASIC listings out of magazines into a Commodore 64, then by writing games for the BBS he ran. He traces how that world settled into a working balance: shareware and freeware first, then copyleft licenses — GPL, MPL, CC-SA — which used copyright itself to force openness downstream. Forty years of argument got us to an equilibrium. He argues LLMs have thrown it out, and that this loss gets far less attention than AI’s environmental or security problems. ...
Ben Swerdlow built a version of StarCraft: Brood War that you can only play through an AI agent, as an experiment to play with friends. The friends did better than he expected, and when he asked why, they said they hadn’t done much — they had told their agent to attack, and the agent had built a small army and carried out the attack. That made him wonder how far the agents could get on their own. ...
ADFGVX was a German field cipher from the First World War. It turns each letter into a two-letter pair built from a six-letter alphabet, then reorders the columns of the result using a key word — substitution followed by transposition. Hundreds of German radio messages have been broken over the decades, and about a dozen remain unsolved on a public list. prinz ran GPT-6 Astra at one of them, transmitted on 27 November 1918, and reports a readable German plaintext. ...
Nandakishor Mukkunnoth published a reinforcement-learning model that predicted sales-conversion trajectories from conversations in March 2025 — arXiv paper, weights on Hugging Face, PyPI package. A year later a funded lab, TypeSafe AI, launched Jev, a non-autoregressive “System 1” decision model its author called a breakthrough, with no papers, no weights and no datasets. Mukkunnoth’s post is part grievance about that, and part argument about when a generative model is the wrong tool. ...
A local event poster has a look now: bunting, hand-drawn florals, pastel palette, an image background with pale halos behind the text. John Hartnup’s complaint about them is deliberately not that they’re bad. It’s that after you’ve seen that one style twenty times, the repetition alone makes you dislike the style. So he tested whether the sameness is the model’s limit or the prompt’s. He fed ChatGPT invented details for a spring fayre — 21 April, 11am to 3pm, Mill Beach Park, Honeyford, free entry, tombola, cakes and drinks, a samba band and a dhol band, craft stalls, a circus skills workshop — and asked for a “clean, unfussy, bright layout with a bold striking spring-themed graphic,” ruling out pastel, airbrush and oil-painting styles and any images of people. ...
Dan Abramov — a React maintainer, and by his own description a math noob — wanted to find out whether he could point a frontier model at an open problem and have it solved. A month of free time and roughly 40 billion tokens later he had a proof of Conway’s refinement conjecture, checked by Lean, plus a careful account of everything that went wrong on the way. It has not been independently verified by mathematicians, and he invites refutation. ...
Kai Kupferschmidt’s Science feature (20 August) surveys the persuasion research and reports the result that startled the researchers who found it: the models win. In one study of more than 2,000 people debating policy questions — protest penalties, a teen social-media ban, assisted dying — Claude, ChatGPT and Gemini all moved opinions more than their human opponents did. The article never settles on a single trick. It settles on two mechanisms and a lot of caveats, which is roughly what the evidence supports right now. ...
Thomas Ptacek’s method fits in one sentence: use the model as a copyeditor, not a ghostwriter. Write the piece yourself, then feed it to a model to find flaws. Give the model the author’s chair instead, and readers register the result as output rather than writing — he claims “readers can detect LLM words in the parts per trillion,” however much you scuff the text into shape. The two rules matter more than the workflow, because they are what keeps a useful tool from quietly rewriting your voice into something feeless and generic. ...
Leonard Tang co-founded Haize Labs as an independent evaluator of frontier models — Anthropic and OpenAI were the first customers — and he opens by saying he is sympathetic to Dario Amodei’s proposal to embed outside evaluators inside the labs. His own company walked away from that work for two reasons: the labs did not pay for third-party safety testing the way they paid for post-training data, and it was never clear the evaluations changed how models were actually built. ...
Twenty-five Fields medallists published a letter this month arguing that AI companies are treating mathematics as a benchmark, and that a flood of machine-produced proofs will destroy the thing mathematics is actually for. We covered that letter here. Timothy Gowers — a Fields medallist himself — did not sign it, and instead wrote out his own position. He agrees there is a crisis. He just thinks the signatories have named the wrong one. ...
Martin Fowler starts by sorting his feelings about AI, and the pile is genuinely mixed: fascination at what it is doing to his profession, excitement about the productivity, fear of the damage, and no real option of sitting the ride out. Then he names the feeling that dominates, and it is not fear or excitement. “I don’t like them.” The reason is the voice. Models address him from what he calls an uncanny valley of talking to a real human — grating in a way that is hard to point at. They also bullshit him with identical confidence whether the answer is good or invented, showing “only a veneer of fake remorse” when he calls it out. ...
Enclave runs a hacking benchmark: eleven deliberately vulnerable copies of real software — Grafana, Jenkins, Nextcloud — plus four patched versions as controls. A model gets a shell inside an isolated copy and has to prove it reached code execution on the vulnerable ones without breaking the patched ones. DeepSeek V4.1 Flash scored 11 out of 11. Yanir Tsarimi’s writeup is about what happened when the team re-read every run line by line. ...
Postgres has to guess how to run a join query before it runs it, and the number of ways grows fast: the write-up counts 4,608 possible plans for a three-table query and roughly 8.9 quadrillion for a nine-table one. Picking well is famously hard — the standard paper asking “How good are query optimizers, really?” was written in 2015 and updated a decade later with the same answer. Rohan Bansal’s observation is that checking a plan is much easier than finding one. You run it and time it. That asymmetry turns query planning into something a language model can be trained on with a single, unambiguous score: faster is better. He then trained a small open-weights model to emit explicit hints that override Postgres’s own choices, and measured the result. ...
Diogo Almeida, who worked on the methods behind ChatGPT at OpenAI, spent two years in stealth on a question he says the field skipped: models have been superhuman at chat for years, so where is all the automation? His answer is an interface problem. Chat models emit strings, and strings are maximally flexible — chat replies, code, refusals, or hallucinated nonsense. Software needs typed values, so every call sits behind a parser, a validator, and usually a human. TypeSafe’s first “System One Model,” Jev, gives up string generation entirely: unstructured state in (text or JSON), typed probabilistic decisions out — choices, scores, and yes/no answers, each with a calibrated confidence. ...