Daily Brief

Conventional wisdom says continuous disagreement is an anti-pattern. Kalshi's co-CEOs made always taking the opposite side a system — the balance a company walking the regulatory tightrope needs.

"How much better do the models have to get before you'll stop reading the code?" Theo asked that question recently and the replies caught fire. Mitchell Hashimoto is calling it agent psychosis. ThePrimeagen's subreddit is in open revolt about people shipping code they never read. Uncle Bob says we have about a year left of looking at code at all. Alex Volkov saw this argument coming three months ago, and gave it a name. At AI Engineer Europe, OpenAI's Ryan Lopopolo opened the conference by saying "code is free." His team shipped over 1,000,000 lines with zero human review. Mario Zechner clos

Full episode: https://www.youtube.com/watch?v=OS1NZLgKM2c Me on twitter: https://x.com/dwarkesh_sp

If you build agents alone long enough, you will independently reinvent five things software engineering solved decades ago. A way to test whether your agent's output is still correct after you changed something. A way to run it on a schedule and know if it failed. A way to prevent one skill's schema change from silently breaking three downstream skills. A way to roll back when today's run produces garbage. A way to validate outputs before they hit production. You just reinvented regression testing, cron monitoring, contract testing, version control, and staging. Badly. Without realizing it. T

Local AI has crossed from interesting to useful, driven by stronger open models, better hardware, and a maturing ecosystem for running intelligence outside the cloud. This panel explores what that shift unlocks for sovereignty, defense, regulated industries, privacy, cost, and resilience, and why open-source AI may be central to who benefits from the next wave of intelligence. Moderator: Nader Khalil (NVIDIA). Panelists: Joseph Nelson (Roboflow), Alex Cheema (Exo Labs), Ahmad Osman (r/LocalLLaMA).

Most vertical SaaS teams are doing the same things: chasing higher accuracy, building better model harnesses, shipping more features. And their customers are saying the same things: the AI got this wrong, it hallucinated, the accuracy is not good enough. So teams go back and push the numbers higher. We did the same at Filed. We built AI data entry for tax firms and hit 80%+ accuracy against an industry baseline of 50-60%. Many users still complained. Same model, same stack, different outcomes. So we dug in. The unhappy customers were not experiencing worse AI. They were reverse-engineering eve

Most AI demos are built around a toy workflow. Ira was built around a factory. This talk is the story of how a third-generation Indian machinery company built a multi-agent operating system that helps run sales, business development, recruitment, quoting, marketing, production context, email workflows, and organizational memory. Ira is not a chatbot and not a wrapper around a single framework. It is a company brain: 39 bounded specialist agents, Athena as orchestrator, a 17-stage request pipeline, Qdrant for document memory, Neo4j for relationships, Mem0 for long-term semantic memory, Postgre

chatting with our friend Matt!

a trip into the cognitive science inspired research of Amazon's new AGI Lab!