Ai-Work

Cory Doctorow on centaurs versus reverse centaurs: how AI can expand worker judgment or become a system for speedup, surveillance, cost-shifting, and impossible review workloads.

A Brookings paper models the immense cost of AI data centers and asks who bears the risk when hyperscalers finance them through leases and project debt.

A data engineer concedes that AI writes average code, then argues the real loss is that no team remembers the architecture or why any decision was made — plus what 108 HN commenters added.

A reliability engineer's case that cheaper code generation misses where software actually costs money — maintenance and accountability — plus what 288 HN commenters added.

Sunil Pai on recovering momentum through smaller work. Rhys Sullivan revisits YC and shows how clearer contracts made his coding agents more useful.

Sentience founder Sam Kececi on building a hyper-accurate simulation of a person: where off-the-shelf models are fine and where your taste has to be in the weights, the perception-to-memory master plan behind the product, and why he thinks AI slop is really a uniformity problem.

A Haskell developer's anti-burnout workflow: hand the agent the planning, research and review, keep the keyboard — and treat an empty token budget as a service outage.

Two applied mathematicians use the history of compressed sensing and faster MRI to argue that solving a problem is not a substitute for understanding why a solution works.

A developer who quit AI coding agents after they turned him 'dumber, lazy, and a worse developer' — plus what 181 HN commenters added.

The security researcher argues AI is about to dissolve the boundary between programmers and users, which would make the operating system's core job — isolating applications from each other — mostly vestigial. Plus what 305 HN commenters added.

A guest post on Terence Tao's blog argues that the scarce resource in an AI-driven mathematics is not ideas but humans who can understand them — and proposes funding an intellectual reserve to do it. Plus what 253 HN commenters added.

A URL-to-MP4 agent, its local fork, and the useful distinction between generating animation code and generating video.

NVIDIA's CEO told Ezra Klein that kids no longer need to learn math now that LLMs exist. Jürgen Geuter argues these boasts work as loyalty tests — and the only way to pass one is to fail it.

A long-time Google engineer quits over the speed of AI progress — and the thread argues about whether his reasons hold together, since his next project is AI debugging tools.

Anthropic's Claude apps team spent two weeks with an agent in every Slack thread: 3,000+ merged changes, thirteen measurements, and a benchmark ratchet behind every fix.

A CMU professor argues that cheap-to-run local models and autonomous agents are about to hand the next decade of AI research to university labs — and the 94-comment thread is mostly about whether the claims add up.

Alexandru Nedelcu's case against treating code as disposable output: maintainability has no reward signal a model can learn from, and the developers who stop reading code stop learning from their own mistakes.

Design docs, PR notes and meeting summaries now arrive generated instead of written. A systems engineer explains the context gap that makes them unreadable — and the narrow set of jobs where AI genuinely improved his writing.

Open model weights alone do not give countries or institutions control over their AI. The dependencies run through chips, cloud, agent harnesses, data systems, and observability.

Murali Swaminathan, CTO of Freshworks: 70%+ of net-new code is AI-written, commits capped at 500 lines because review is the new bottleneck, deflection moved from 40% to the high 70s, anything under 50% confidence goes back to a human, and agents get priced per session instead of per seat.

A security researcher who learned to code by typing BASIC out of magazines argues LLMs turned sharing your work from a gift into a liability — and 232 HN commenters split hard on whether that complaint holds.

Maddy Zhang connects reusable project instructions, design review, isolated agents, self-checking loops and risk-based code review into one daily workflow.

Ethan Bloch, product lead for ChatGPT Finances, demos a 90-day subscription audit that emails the biller for you, tax optimization, ETF fee swaps, credit-card reward analysis, and interactive decision sites for rent-vs-buy and taking time off.

An AI researcher who uses models daily argues the writing itself is the thinking, models are vague and wrong in ways that are hard to catch, and unlabeled AI prose breaks the deal with the reader. Plus what 94 commenters added.

The village-fayre poster everyone recognises is one style repeated until it irritates. Naming an actual movement — Bauhaus, risograph, 1980s fanzine — gets ChatGPT somewhere else entirely.

Dan Luu's argument that delegating to an LLM and assuming it worked has no future for the person doing the delegating — plus what 150 HN commenters added about liability, deskilling, and where the pressure actually comes from.

A self-described math noob spent a month and roughly 40 billion tokens getting LLM agents to produce a machine-checked proof of a 50-year-old Conway conjecture. He failed twice along the way, and the failures are the useful part.

Peter Yang's hands-on tour of Meta's Muse: $40/month off AT&T by finding an autopay discount, an agent that phones Comcast and negotiates in its own voice, native connectors to Meta's apps, a personalized feed with no ads yet, habit artifacts with streaks, and Marketplace listings it can haggle over.

A new language has an AI write the proofs that its own code is correct. The essay's real target is the trap underneath: you can now build an entire compiler before you learn what the field already solved.

Put the model in the copyeditor's chair and never the author's chair — plus the workshop system prompt the author posted in the 141-comment thread.

The Fields medallists say AI is flattening mathematics into a pile of undisgested proofs. One of their own number explains why he didn't sign: the danger is not too many theorems, it is the disappearance of the people who wanted to be mathematicians.

Martin Fowler's complaint is not that LLMs are useless. It is that they are useful, over-confident, and talk like someone he would leave the room to avoid.

Discovered Labs' founders distinguish retrieval from citations and argue for clear positioning, genuine corroboration, and measurement that accounts for uncertainty.

Ali Parandeh on why heavy industry is 2-3 years behind on AI, why Copilot is the floor and not the strategy, bespoke predictive-maintenance models on bespoke sensor data, the dark-room-with-a-lantern case for time-boxed AI R&D, the four ways agent projects fail, scaling consulting through software and forward deployed engineers, and why handcrafted work gets more valuable.

A reader built a large TypeScript system with LLMs, then realized he couldn't maintain it. Mark Seemann answers in public — and admits he doesn't have a program to offer.

Peter Yang's 15-step podcast pipeline, split into three ChatGPT Skills (prep, edit, production) that call each other; pastes his manual edits back into the skill as a numbered diff so it one-shots next time.

An engineer's structural case against frontier-lab valuations: the flashy wins are the best case knowledge work will ever get, and the bottleneck is specification, not reasoning. Plus what a 302-comment HN thread pushed back on.

vals.ai gave Claude Fable 5.1 an unsolved 370-year-old cipher, a pep talk and no help. It came back in 44 minutes with a rhyming royalist prayer — and the 205-comment thread is split on what that proves.

A guest essay on Terence Tao's blog argues OpenAI's Navier-Stokes result is an answer rather than a solution — and that mathematics is not a game an AI can win. Plus what 141 HN commenters added.

Smartling's VP of AI Olga Beregovaya on why one-to-one translation is dying, generating target content straight from briefs instead of a source, why models hallucinate more in under-resourced languages, interpretation outliving translation, translateese and modelese, the build-versus-buy line, and the 12 metrics her team scores translation quality with.

Brex CEO Pedro Franceschi on scoping agents as virtual employees with a job, skills, manager and budget — the Jim recruiting demo, CrabTrap's LLM-as-a-judge proxy, Magpie for token spend, and the OpenClaw harness he runs his own life on.

A game maker's honest account of what generative AI took from him — not his job, but his pride in the work — and the three paths he sees left, plus 394 HN commenters splitting sharply over which one is survivable.

A step-by-step deploy guide for Cole Medin's open-source software factory: GitHub issue in, validated merged PR out, running 24/7 on a remote Ubuntu box with Codex on GPT-6 Astra. Plus his blunt read on the AGI hype — not AGI, but the first model he says actually gets him.

Twenty-five Fields Medalists argue that turning mathematics into an AI benchmark is destroying the thing math was actually for: understanding.

An operator running 18 million messages through routed open models documents what the router hides: providers serving identical weights that differ by 20 points on tool calling, vision endpoints that blind-read your images, and 200 OK responses with no answer in them.

A publishing executive's argument that frictionless tools are thinning out the collaboration research depends on — and that our incentives make choosing the chatbot the rational move.

A veteran programmer names the real damage of the AI shift: not lost jobs, but the loss of respect for a craft people built their identities on.

When AI makes competent execution abundant, shipping an app stops being impressive. The scarce thing is the habit of inventing instead of replicating.

OpenAI shipped a machine-checkable Lean 4 proof alongside its Navier-Stokes result. Formal verification just got about four orders of magnitude cheaper.

Demetrios Brinkmann's everyday automation examples lead to harder questions about permissions, model-harness compatibility, and whose interests shopping agents represent.