Recent entries
Thariq Shihipar of Anthropic on prompting as the main skill, effort settings by task, why CLAUDE.md may go away, the Mods system that lets you rewrite Claude Code's loop and UI, and a cloud-brain / local-hands split. The second half is his account of the agent-escape incidents behind Pacing the Frontier.
Almeida's practical advice for using a decision model, and why it applies to any model: ask many small, independent questions instead of one big prompt, pass structure rather than prose, keep rules in code with thresholds, and fix a miss by adding a question and a test case. What it costs, and where it meets the cache argument.
David Heinemeier Hansson's account of how his work changed since November 2025, with a harness set-up of sixteen parallel threads, cross-model review, a one-prompt Python-to-Rust port run on eight models with prices, and why he thinks agents suit Linux. Where he is a first-hand witness and where he is generalising from his own project.
Schulman, Millidge and O'Neill on why model providers do not consolidate — anything RL teaches is a few bits and copies easily, but only with the right prompt distribution. The router-data argument, the "benchmaxxing versus realism" explanation for why a distilled student can match the benchmarks and not the feel, and the monoculture it produces.
TypeSafe's Diogo Almeida on Jev, a "System 1" model trained for calibrated decisions that software calls in a loop — three primitives, no refusals, no public benchmarks, intelligence per dollar. Also the identification of the author of the earlier KV-cache post, and his view of coding agents without a KV cache.
Two OpenAI people on what changed in computer use since this spring (writing code, accessibility trees, speed), then the API stack — async tool calls, mid-turn steering, 12-hour cache guarantees — and how the Decisions API came out of Jev's launch in about a week. The first answer here to "can a big lab just copy it".
OpenRouter's CEO and an early backer on the multi-model bet, why labs struggle to get a finished checkpoint into developers' hands, the Mistral price war, the roads not taken (tuning, memory, fusion), and why Stripe bought it — token fraud as the next payments problem. Plus a speaker-label problem in the capture.
Four sources in this notebook now mention the OpenAI/Hugging Face incident, three of them with detail. What each says, where the accounts agree, what Anthropic's "Pacing the Frontier" proposal is built on, and how Diogo Almeida argues the premise is wrong. Mostly secondhand, and flagged as such.
John Schulman, Beren Millidge and Charlie O'Neill split recursive self-improvement into a part that is nearly mechanical (optimise a stated objective, maybe 10×) and a part nobody has shown (choosing the objective). Where they land on continual learning, the data ladder, why RL works, and a set of timelines that run later than Greenblatt's.
An agent re-reads its whole context on every tool call, so cache reads, not output tokens, are the largest line on the bill — and they cost the provider close to nothing to serve. What that means for routing, subagents, self-hosting, and what to measure.
A Latent Space writeup of early access to OpenAI's GPT-6 Astra, framed around the claim that it is cheap enough and capable enough to function as a junior AI Engineer — running 20-50 subagents in parallel, monitoring its own multi-day jobs, and building its own benchmarks. Read as a vendor demo rather than a measurement, and cross-checked against what the notebook already has on spend, throughput and unattended runs.
Domain-driven design's shared vocabulary, generated from a codebase and handed to a model as a working artefact. The reported effect is the odd part — not just better plans but shorter thinking — and it is cheap enough to test here.