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Briefings and ideas about Agent skills from AI podcast discussions and the research they cite, with links to the original sources.
Modal CEO Erik Bernhardsson says coding agents trained on old examples keep writing outdated Modal syntax, so it retires features more cautiously. Modal also tests whether agents recommend it and clarifies pricing content they misread.
Tenex, which builds AI systems for large companies, says it stores plans, agent decision logs and project context beside its code so agents can read them. In the episode, Tenex said Opus 5.5 is the main coding model for most of its team, and that pairing it with Codex as a reviewer still gives the best results.
Alex Zhang, an MIT PhD student, described Prime Agent, built with Prime Intellect: the agent acts only through Python and keeps its history on disk. He also cited outside RLM work, including Harvey's legal agents and Laude Institute's Headlong.
Anthropic's Thariq Shihipar argues that stronger coding agents make prompting more important. He says rich context up front, effort matched to the task and review of the agent's decisions cut rework. His effort settings are a rough guide.
Anthropic's Thariq Shihipar says Claude Mods, publicly proposed in September, would run user code inside Claude Code for automatic quizzes, assumption logs and model routing. The interview also raised cache-cost and complexity worries.