Large language models are now generating and auto‑formalizing mathematical counterexamples faster than human experts. Systems like Logical Intelligence's autoformalizer and OpenAI's Sol have turned raw AI‑written proofs into Lean code within days, reshaping how mathematicians verify results.
In a live fireside chat, Anthropic’s Cat Wu and Thariq Shihipar unpack Claude Code’s latest updates, Claude Tag, the Fable model, and hardened agent security. They also share real‑world evaluation practices and tool design lessons that will shape how developers integrate AI coding assistants into production pipelines.
At an AI Engineer World’s Fair fireside chat, Anthropic’s Cat Wu and Thariq Shihipar detail Claude Code’s latest upgrades, Claude Tag now powers 65% of internal PRs, Fable’s prompt size cuts 80%, and automated code review handles outer layers. They also explain security review shifts and why “don’t do X” lists hurt modern models, reshaping how developers adopt AI coding agents.
Stencil shows that a “plan‑then‑execute” chain with a frontier model and a cheap executor actually costs more, because the expensive model reads the whole codebase twice. By limiting the frontier LLM to a single, decisive edit, you cut token usage and spend ~14% less while keeping the same success rate.
A new design lets an AI agent sit alongside you inside an app, editing the same document you’re working on in real time. In a music‑making demo, the agent changes chords, adds bass lines, or transposes a song while you keep tweaking. This flips the usual chat‑box model, turning AI into a collaborative co‑author for any content.
Cursor’s latest swarm, guided by planner agents and cheap worker models, built SQLite from its docs and passed 80% of a held‑out SQL test suite in four hours, far outpacing its predecessor. The new architecture lets the spec drive the swarm, keeping planning context light and execution cheap, promising scalable AI‑assisted development.
In an email to OpenAI’s board, Sam Altman revealed the company is planning an open‑source language model with roughly GPT‑3‑level capability that can run on consumer hardware. He said the move aims to undercut rivals and make it harder for new, high‑cost AI ventures to attract funding. The proposal will be discussed at the next board meeting.