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Briefings and ideas about Context engineering from AI podcast discussions and the research they cite, with links to the original sources.
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.
Dave Blundin said today's AI forgets mistreatment like a goldfish once its context is flushed. On Moonshots, Alex Wissner-Gross countered that tools like Claude Code already save notes to files, so "I wouldn't count on it."
Goodfire's Eric Bigelow said its Silico agent did the research for his Forking Fast paper within a week. He says researchers still need to supply context, close guidance and the scientific taste that agents don't yet have.
Goodfire's Eric Bigelow argues in-context learning covers nearly everything a model does to adapt as it reads. He expects context, memory and harnesses, not per-user fine-tuning, to drive personalization, but hasn't researched continual learning.
Yash Patil, whose company Applied Compute sells post-training, says teams should first improve prompts, context and tools, then retrain weights for their biggest workloads, tasks that change over time and judgment-heavy tasks.
Keerthana Gopalakrishnan, who leads Gemini Robotics research at Google DeepMind, explained how a reasoning model open to developers directs an action model limited to testers, and where she says failures arise.
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.
MIT's Alex Zhang says Claude Code, Codex and Pi share nearly all their design. He argues code-based recursive harnesses could help models generalize, citing his own early experiments on one open model as support.
MIT PhD student Alex Zhang guesses that about 95% of an agent swarm like OpenAI's Navier–Stokes run burns tokens on dead ends. He still calls OpenAI's approach right and says getting a swarm to converge is the hard part.
At DevDay, OpenAI API product lead Nikunj Handa said GPT-6 lets agents keep reasoning while tools run and take new instructions mid-task. He also described longer cache guarantees, one still in preview, and open questions about the platform.
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.