Ai-Work

Tim Dettmers opens with a classroom: asked who is afraid of not getting a job after graduating, roughly 120 of 150 students raise their hands. Then a second story, arriving by email — PhD students counting the years until they can leave academia for a frontier lab, convinced that research in universities is meaningless. He thinks both are wrong, and wrong for the same reason: they assume the future of research belongs to whoever has the most GPUs. The 94-comment thread on Hacker News spends most of its energy arguing with the specifics. ...

Alexandru Nedelcu opens with three sentences he has heard in the past month: “I haven’t written code since 2025”, “Code reviews are dead”, “People no longer read code”. His claim is not that the tools are weak. It is that the thing being traded away has no way of showing up on a scoreboard until it is far too late to fix. The mechanism is the argument worth keeping. Code maintainability and good architecture have no good measurements, because their effects take months or years to appear. Any reinforcement learning needs a reward signal that can be measured immediately. So the signal models train on is not maintainability — it is rules from rulebooks written for beginners, plus patterns from code in the wild, which is mostly bad. His sharpest line is the falsification test: if maintainability had a discernible fitness function, “it would’ve been baked into our linters”. ...

Colin Breck is a systems engineer who writes about databases, observability and reliability. His complaint is not that AI writes badly — it is that people who never produced original writing are now producing design proposals, business plans, tickets, pull requests and meeting summaries in volume, and the rest of us are expected to read them. His own use of AI, he says, has made his writing faster and better. Almost everything he is asked to read has become worse. ...

Murali Swaminathan is the CTO of Freshworks — 15 years old, publicly traded, ~4,500 people — and he spent this hour (58 min) describing what it takes to rebuild that org for agents while the plane is still flying. Almost none of it is about model capability. It’s about the machinery around the model: review budgets, confidence thresholds, access control, and pricing that survives agents replacing the seats you used to bill for. ...

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. ...

Ethan Bloch runs product for ChatGPT Finances at OpenAI. He founded Digit (the SMS money-saving chatbot, acquired 2021) and a second company that OpenAI acquired in April 2026, and he walks Peter Yang through six ways to actually use the product — then talks about how his team ships. ...

Erich Grunewald thinks you should almost never use AI to write — and he is explicit that this is not an anti-AI position. Transcribing audio, analyzing data, searching, brainstorming and commenting on your drafts are all fine, as is line editing, provided “all the edits are deliberately accepted or rejected by a human.” What he objects to is the writing itself: the part where you type words on a page to convey an argument. The 94-comment thread on Hacker News takes the argument seriously enough to attack it from both ends. ...

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 Luu’s new post is about a habit he started noticing in early 2025: handing an LLM a task — summarize this text, write this code — and simply assuming it worked. Back then it usually produced silly results. By September 2026 he says the practice has spread and improved to the point where “for loop meat proxying” (accept the model’s output; if it fails, ask the model to fix it) produces software that sort of works. He’s careful to say he’s impressed by how far that’s come. ...

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. ...

Peter Yang walks through ten things he actually does with Muse, Meta’s consumer personal agent, a week after launch. Two of them have a dollar figure attached, and both come from the same trick: the agent does the research and the phone calls, the human does the irreversible bit. (17 minutes, his channel.) ...

Bend 2 is pitched as a language for the AI coding era: a human writes “laws” the program must obey, an AI writes the implementation and the proof, and the compiler checks that the proof holds. Liam Powell’s objection is not that the idea can’t work. It is that Bend looks like a clean example of a trap vibe coding sets — you can now build a substantial thing long before you know enough about the problem to see that a much better approach already exists. ...

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. ...

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. ...

Ali Parandeh — a chartered mechanical and software engineer, O’Reilly author (his book covers building generative AI services with FastAPI), previously head of engineering and now running his own AI advisory for heavy industry — interviewed on TwoSetAI (30 min). His market is construction, automotive, energy, aerospace and infrastructure: sectors where a bad model is not a bad decision, it is a physical failure. He came back to the show to talk about how those companies actually buy AI, how to scope projects that cannot be allowed to fail, and the job title his business model ends up needing. ...

Mark Seemann’s blog has spent most of 2026 circling AI, and this post is him answering a reader’s letter in public, with permission, because he says his answers aren’t rigorous — “the situation is so uncertain that I can only answer to the best of my abilities.” The letter is the part that travels. The reader has no formal CS background and, with LLMs, built a fairly large TypeScript system: APIs, PostgreSQL, LLM pipelines, research automation, multi-model workflows. It felt like magic until he tried to make it a product — fix one error with AI, another appears, then a part behaves in a way he doesn’t understand. His conclusion: “I may have built a system that is above my own level of understanding. When everything works, that gap is almost invisible. When it doesn’t, it becomes very real.” ...

Peter Yang walks through the ChatGPT Skills chain that runs his podcast production — 15 manual steps a week, three skills, one orchestrator. (19 minutes, his own channel; prompt pack at behindthecraft.com.) The three-step system Map every manual step. He wrote out all 15 things he used to do per episode — research the guest, build the interview guide, send edit instructions, transcript, upload, title/thumbnail, newsletter, clips, social posts. Just making the list exposed three distinct phases hiding inside it Build one skill per phase — podcast prep, podcast edit, podcast production. Production is the orchestrator: it walks him through thumbnail, title, show notes, newsletter, social post and clips one by one, calling the individual skills Connect the skills end to end. His framing: if you find yourself repeating the same template or process, build a skill for it The first move after the list is to paste the list into ChatGPT and ask “what work from here can you take off my plate?” He runs that in a custom mode so his existing skills don’t shape the answer — he wants to see what the model claims before it sees his setup. ...

Jay Kruer’s argument is not that LLMs don’t work. It’s that the headline wins — the Navier-Stokes proof, the FreeBSD remote exploits, the Hugging Face incident — are the cases where the technology looks best, and the labs’ valuations rest on treating them as typical. His test for the gap is blunt: software firms keep hiring and promoting bottom-quartile engineers who would score far below the models they supervise on the benchmarks of the day. The 302-comment thread on Hacker News is where the argument gets tested — including by practitioners who dispute it from experience. ...

vals.ai gave Claude Fable 5.1 an open-ended assignment: go solve an unsolved cipher. The model picked Sir Thomas Urquhart’s Cyphral Distich, two lines of 32 numbers each printed at the end of his 1653 book Logopandecteision, and the lab reports it came back solved within a day — 44 minutes and 176,000 tokens of work, with no human interjections after the initial prompt. The puzzle had been open for roughly 370 years. It was posed in Notes and Queries in 1899, discussed in 20th-century cryptography literature, and listed by cipher researcher Klaus Schmeh among his Top 50 unsolved encrypted messages. ...