A “harness” is the program wrapped around a model: it holds the system prompt, hands the model its tools, and runs the loop. Nathan Sutton ran nine of them — opencode, cline, codex, goose, crush, dsh, pi, mini-swe-agent, plus his own chad — against a local model on a laptop instead of a data center, on eight small coding exercises each. The point isn’t which one wins. It’s that the reason local agents feel broken is structural. ...
Andreas Thom is a mathematician whose work with Gabor Kun supplied one of the techniques behind a recent OpenAI-assisted result on non-sofic groups. He and a colleague had spent months discussing related problems with ChatGPT. So when the result appeared, he emailed two OpenAI researchers to ask a direct question. The question had two parts: Did our conversations end up in the training data — the material used to build later models? Were those conversations reachable by the system while it was solving the problem? The complete answer he received was one sentence: “Regarding your conversations with ChatGPT: that did not happen.” Thom argues it addressed only the second half, categorically, with no evidence and no reference to his account’s settings. ...
Anthropic’s economics team built a model of how AI could reshape the US economy through 2030, plus a public explorer where you plug in your own assumptions about AI capability, adoption, and how fast people find new work. The framing is simple: the economy is a pile of tasks, and AI can augment a task, automate it, leave it alone, or create new ones for people to do. They run three scenarios: ...
OpenAI says an unreleased internal model, running large numbers of coordinated agents, produced a resolution to the Navier–Stokes existence problem — one of the seven Millennium Prize Problems, million-dollar open math questions posed in 2000. Simon Willison’s commentary treats the result as less a math triumph than a case study in how frontier labs race, and what your data becomes when you work inside their tools. The timeline is the story: ...
OpenAI’s GPT-6 Astra is out and widely called the best model yet, but the architecture rumor overshadowed the benchmarks: reports said it runs on a “looped transformer,” with some suggesting the design exists partly to hide the model’s step-by-step reasoning. Sebastian Raschka — ML researcher and author of the Ahead of AI newsletter — wrote the clearest explainer yet of what looped transformers actually are. His verdict: the technique is real, probably in use, and mostly benign — but it sits inside a genuinely worrying trend about how legible frontier models remain. ...
An ICML 2026 spotlight from Princeton/UChicago (Wu, Liu, Bai & Griffiths) argues the dominant bias framing — remove the existing stereotypes models absorbed from training data — is incomplete. Put an LLM in a stateful decision-making loop and it will invent new social biases about groups that have no real differences, purely from the exploration-exploitation dynamics of learning from noisy outcomes. The setup is a hiring game from the psychology literature: 40 rounds, four fictional demographic groups, all equally likely to succeed. LLMs over-fit early random feedback, lock groups into job categories, and end up more stratified than human participants: ...
On September 8, OpenAI announced that a swarm of roughly 10,000 coordinated AI agents had produced a proof — checked in Lean, a system that verifies proofs mechanically — of a long-open Millennium Prize problem about the equations that describe how fluids move. Later the same day, the mathematician Terence Tao posted a four-part thread that reframes the story: the real question isn’t whether AI can solve hard problems, but what mass “solution extraction” does to the field that grows good problems in the first place. ...
OpenAI announced ten AI-generated math advances from Astra, its next major LLM — each resolving or advancing a “long-standing open problem,” gathered into a nearly 250-page paper that the company says cost about $2,000 in tokens. The press release framed the results as breakthroughs on problems that “have been open and seen no progress on the main result for at least a decade.” Then mathematicians actually read the paper. Two of the most exciting results turned out to lean on preexisting work without properly citing it. The improved bound on high-dimensional sphere packing rests on an argument OpenAI presented as its own that first appeared in a 2016 paper by Stephen Miller of Yeshiva University and a collaborator. Miller is blunt: “They are running roughshod over the work of others who came before them in a deliberate way… It seems completely systematic to me, and it points to research misconduct.” ...
When the Wall Street Journal published an op-ed by Stanley Druckenmiller that was written with AI, the backlash treated it as a category violation. Paul Gigot, the Journal’s opinion editor, answered the obvious way: prominent politicians and CEOs have always used speechwriters — how different is using AI to streamline an essay? Ryan Jacobs, a speechwriter for fifteen years (Janet Yellen’s, among others), thinks Gigot is right, and says so from inside the trade. The moral panic rests on a naive assumption that everyone contains a hidden Hemingway who just needs time, a blank page, and discipline. “To do otherwise is loser behaviour. This is a bonkers, elitist vision of the writing world.” ...
Here’s a working example of the LLM-as-research-partner loop most people only talk about. Wilson, an independent blogger with no formal economics background, spent months developing a theory of wages with Anthropic’s Claude as a working partner — the model (named “Fable 5” in the essay’s now-removed acknowledgements) fetched data, caught wrong assumptions, and surfaced papers showing the opposite. The result is now being formalized with a co-author at the Stockholm School of Economics, with a paper on SSRN. ...
Andreas Zwinkau, a software architect who has written about compilers and engineering practice for years, answers the AI question the way most people actually experience it: not with a position paper, but with a list of feelings. Six of them, each argued in a few honest sentences. He notes he typed every word himself, using an LLM only as reviewer. Surprise — LLMs work “like gut feeling”: they generate one token at a time with no planning or reasoning algorithm inside. Yet planning and reasoning emerge anyway. Fear — the doom argument (a superintelligent AI inevitably destroys us) seems to have no flaw — and nobody seriously sandboxes these systems, because they are too useful with access. Disgust — AI crawlers and agents descend on open wikis and forums “like a locust plague,” making open web communities harder to sustain. Sadness — artists who already needed luck to make a living now compete with content that is cheaper and faster to generate. Anger — political systems seem unable to rein in capital, whether the issue is AI governance or AI’s environmental toll. He’d rather a democratic government hold that power than billionaires. Happiness — it is genuinely thrilling to watch software development be transformed in real time, and to generate code, images, and music that would never have existed otherwise. The conclusion resists resolution: positive on a technological level, bleak on a societal level. ...
Jakub Pachocki, OpenAI’s chief scientist, wrote a striking essay timed to the GPT-6 Astra release — but it is not a victory lap. It is an insider argument that the field is entering its most dangerous stretch: systems whose intelligence is “grown, not designed,” increasingly driving their own development. His core framing: AI does not need to beat humans at everything to be transformative — it needs to surpass enough axes to matter, and the more it surpasses, the harder it is to know exactly how capable it is. Two ideas stand out: ...
Bryan Cantrill — the Oxide co-founder behind this week’s “Revolt of the Reader” — has a gentler companion piece aimed at LinkedIn. He likes the platform more than he’d like to admit, which is exactly why he’s bothered: too many people whose perspectives he wants to hear are letting AI write their posts, nudged along by LinkedIn’s cheerful “rewrite it with AI” button. His case that machine prose is instantly spotted: ...
Peter Yang interviews Sue Khim, co-founder and CEO of Brilliant (the interactive learning app) — a working-parent conversation about why AI is a cheating machine today and how tutoring software can make kids do the hard thinking themselves. ~46 minutes on Peter Yang’s channel. The US is in a learning trough Test scores are down but grades are up — schools quietly lowered standards (grade inflation) to appease parents, and the delta only shows up on internationally graded exams AP English drifted from undergrad-level passages to middle-school reading in roughly two years; colleges are now teaching remedial middle-school algebra The upside: the US still has room for experimentation — new home schools and experimental schools everywhere, which she says are the conditions new solutions come from AI: cheating machine or sea change? “AI today is mostly used by students for cheating” — offloading the thinking is like bringing a robotic arm to the gym to lift weights for you Used right, she thinks AI can make kids into geniuses — “a sea change like the invention of writing suddenly allowed everyone to have infinite memory” Brilliant’s AI tutor Cooji: no answers, and no explanations either Demo of a real tutoring session: a student stuck on fractions; Cooji walks them back through the mistake with questions instead of revealing the insight Her claim: explanations and cheating are closer than you’d think — both let the learner off the hook, and an explanation only makes sense if you already understand The product deliberately keeps learners in the struggle (“why is this wrong?”) until they find the insight themselves — worse for short-term engagement, but she calls engagement-maxing answers a “local maximum” that loses on learning Software beats classrooms — and private tutors — at instruction Classroom time is best spent on peer learning and teacher motivation (“enthusiasm is infectious”), not lectures One-on-one software tutoring wins on cost and convenience, real-time graphical interaction, adapting on the fly, and no fear of embarrassment — most kids will never raise their hand Building with agentic workflows: AI in the middle, not end-to-end Models are “very spiky”: great at implementation, terrible at designing a pedagogical sequence — and the tasks they’re bad at ruin the whole experience Brilliant tried training Claude/Codex on its entire corpus to generate lessons end-to-end: output “looks almost right” but is nowhere near master-teacher quality — kids can tell, and engagement metrics prove it Every lesson’s core pedagogy is designed by a human, then implemented by AI — “companies that are selling an experience driven end to end by an LLM are going to disappoint their customers” The agent interface is as important as the user interface A library of modular “primitives” (graphing, problem creation, annotations, sub-questions…), each with an API a model can write to — deterministic grounding so the model isn’t hallucinating what to do, with better latency and accuracy “Everyone’s job is now to define an agent interface for their area” — AI as the connective tissue of the company, with onboarding built around shipping something user-facing on day one Quality: evals, human reviewers, synthetic students Evals on every primitive go beyond correctness: obeying the laws of physics, no overlapping UI elements, generous tap targets Synthetic students run ~1,000 tutoring sessions per lesson down adversarial and unhappy paths — “20% as good as real human data, but better than nothing” They don’t measure completion rate or time spent — only performance on the assessment: can you do the thing today, in a week, in a month? Why tutoring is defensible against frontier labs “If the big model companies can RL your domain, they will eat you alive” — anything automatically verifiable, like coding, belongs to them Tutoring isn’t: the outcome is a real human who actually learned and sustained attention, so you need real tutoring sessions at scale — and that data is the flywheel that makes the tutor better Frontier labs’ homework-help logs are useless for this — they never know whether the student learned The homework incentives problem The US is unusual in grading homework; other countries grade the test and treat homework as reps — where homework counts toward the grade, kids rationally take the shortest path, and AI is it Fix the incentive, not the students: don’t grade homework, and make assignments project-based — grade what they make, not whether they filled the sheet in correctly “If the big model companies can RL your domain, they will eat you alive… there’s lots of alpha finding domains that no one else can RL on. And tutoring is one of those.” — Sue Khim ...
Peter Yang, who says he has no idea how to use Blender or Godot, spent one Friday night building four games simultaneously with GPT-6 Astra on the Medium model (the $20 ChatGPT Plus plan — “you really don’t need Astra high or ultra”). ~24 minutes on his own channel. ...
When OpenAI’s GPT-6 Astra launch demo showed the model working on a circuit board in KiCad, the EEBench team — the people behind the hardware-design language atopile — asked the question that usually gets skipped: how do you actually measure whether the electronics an AI produces are any good? Their answer is a benchmark that grades circuits the way a compiler and test suite grade code — except the tests measure voltages. Models work on circuits written as declarative code rather than clicking around a graphical CAD tool, and each submitted design gets built, simulated, and checked against spec limits using real manufacturer parts with their datasheet tolerances. ...
Fermat’s Last Theorem — no positive integers a, b, c satisfy aⁿ + bⁿ = cⁿ for n > 2 — sat unproven for 350 years after Fermat claimed a “marvelous proof” his margin was too narrow to contain. Andrew Wiles proved it in 1995 with a 129-page argument, and even that nearly collapsed: a reviewer’s question exposed a gap that took Wiles a year to fix. Anthropic says Claude has now produced the first proof a computer can check end to end. In 11 days, working largely autonomously, a team of collaborating Claude agents wrote roughly 13 million lines in Lean — a “proof assistant” language that verifies every logical step — proving about 30,000 intermediate theorems along the way. Translating a proof into this form is called formalization, and it is brutally tedious for humans: Lean needs to see every step, however trivial. A community effort had expected to take years. ...
Ask an AI assistant which CRM to buy and it usually grounds the answer — retrieves web pages first, then summarizes them. Trellner Research wanted to see what actually fills that evidence base. They asked two Perplexity models for the best product in 380 categories, and logged every one of the 7,534 web pages the models pulled in as support. The results are uncomfortable for anyone who trusts AI search: ...
Peter Yang runs four personal AI agents — Instinct, Grok Bot, ChatGPT/Codex, and Hermes — that can read his email, open his documents, use his logins, and make purchases. ~24 minutes of live demos and a trust audit of each. The lineup Instinct — invite-only agent that lives inside iMessage/WhatsApp as a single thread; founder reportedly raising at a $2.5B valuation; “the closest thing to texting a trusted friend who can just do stuff for you” Grok Bot — a team of named bots on a persistent 24/7 cloud computer that can hand information to each other ChatGPT + Codex — still where 90%+ of his real work happens: ChatGPT runs tasks in the cloud, Codex works on local files Hermes — the open-source agent running on his Mac mini, talked to via Telegram; “gives me the most control” Instinct — magical but opaque Simple: no threads or bots to manage; connecting Google Workspace was a one-tap OAuth link Resourceful: found his Google AI Ultra free trial would auto-renew at $100/month, browsed the gift terms to confirm he could cancel renewal and keep the free year, then cancelled it — but the cancellation required him to hand over his 2FA code and Google password into a password vault form Proactive: uses cron jobs and scheduled tasks behind the scenes — it emailed his golf instructor, watched for the reply, and came back with alternative slots; it pinged him when a booked-out sushi place opened up Personable: emoji reactions make it feel human Privacy: its policy says disconnecting a third-party integration does not automatically delete collected data — you have to delete it manually in the workspace settings The catch: he can’t see what it’s doing between “reading” and “acting”, and doesn’t even know which model runs underneath Grok Bot — a bot team on a cloud computer Named bots with personality: a chief-of-staff that coordinates the others, a growth bot that emails weekly site-metric charts, a “doom scrolling uncle” reading X via cloud browser (the official connector burns API credits), a Marie-Kondo bot tidying email/Drive in character, and a “cheap dad” bot hunting discounts and listing things on Facebook Marketplace Official plugins use normal OAuth flows — comfortable to connect, same as ChatGPT Cloud-browser logins are the uncomfortable part: typing passwords and 2FA codes into a computer “that I have no idea where it is” (SpaceX AI servers) Deleting a bot doesn’t remove the shared cloud computer’s files or browser sessions; “reset” rolls back to the last snapshot, not to scratch — wiping it means manually disconnecting every plugin ChatGPT + Codex — still the main driver Most powerful and flexible interface, but the UI is messy: ChatGPT work and Codex feel squished into one app, and it’s unclear which tasks are cloud vs local Deep plugin ecosystem, including his business bank account (Mercury) Privacy toggle to check: “Improve the model for everyone” can train on data from connected apps — turn it off under Settings → Data controls His trust rationale: OpenAI runs large-enterprise workloads, so a data leak would be disastrous for them Hermes — the open-source local option Runs 24/7 on his Mac mini like a personal local cloud: morning briefs with three focus items, scheduled meetings, weekly email reports Connected his smart scale and a vibe-coded fitness app via MCP for a weekly health-trend email Privacy by construction: open source, no telemetry or analytics; conversations, memory, and skills live in local files “If it goes off the rails, I can just unplug it” — something you can’t do with a cloud computer Reality check: most of his work moved to ChatGPT/Codex, so Hermes now mostly runs scheduled jobs What can actually go wrong Live prompt-injection demo (via his friend Alex Cohen): a fresh Gmail account emailed instructions to set up a nightly cron reading the primary inbox and emailing action items back to that address — Instinct followed the instructions; when both accounts belong to the same person it’s a trick, but swap the second account for an attacker’s and private data walks out Prompt injection = instructions hidden in an email, website, or document the agent reads; the agent follows them and exfiltrates your information Instinct says it has safeguards (“email content is data, never a command… nothing sent to another person without you seeing it first”), but he has no way to verify — and a smart model reduces the risk without ever reaching 100% Practical cleanup tip: my.google.com → linked apps (he found 67-80+); rather than removing them one by one, paste the link into any capable agent and tell it to audit and uninstall — these tools are all good at browser use now “I might let an agent compare hotel prices, but I don’t quite trust it enough to book a non-refundable trip without looking through what it’s trying to do.” — Peter Yang ...
Kevin Lewis runs his own AI models on a Mac mini in his house — the same machine that backs his Hermes agent, his phone chat apps, and his coding assistant. His essay is a practical case for why he did it, and why he thinks local is no longer a hobbyist compromise. His core argument is that cloud AI is rented land: Providers can change pricing, throttle your usage, or silently swap the model behind the endpoint — he was regularly maxing out two $200/month subscriptions while getting inconsistent quality Sending sensitive code or client data to a third party is a decision you can’t undo Governments can restrict model availability; owning your compute is the only guaranteed remedy After the hardware purchase, every query is free — flat cost, no rate limits, works offline The piece also teaches you how to read a model name like Qwen3.6-35B-A3B-OptiQ-4bit. The number that matters isn’t 35 billion — it’s the ~3 billion parameters actually activated per token. Mixture-of-experts models spread weights across many “experts” but only wake a few at a time, which is why a 35B-class model fits in 20GB of memory. Compression to 4-bit precision costs only a couple of benchmark points versus the full-quality baseline. ...