Daily Brief

Ask your own company what it actually contracted for with a given vendor and nobody can tell you. The number exists, negotiated carefully, sitting in a pricing table inside a signed PDF no system ever read back. Hiral Shah puts a figure on the aggregate: roughly two trillion dollars of negotiated value locked in agreements that organizations never return to, because recovering it means human reading, human review, and manual work across disconnected systems. The scale on Docusign's side is its own engineering problem. Close to two million paying customers, a billion users, and about a million

Legora's search latency once went from a 100 millisecond P99 to twenty seconds, and the cause was a packing problem invisible in the schema. Legal work arrives as projects, a project gets worked hard for a while and then is never opened again, and when the team sharded document chunks across four thousand partitions the dead projects landed in the same partitions as the live ones. Every query pulled an enormous partition into memory, evicted the last, and thrashed the cache. Jacob Lauritzen walks the whole migration honestly, from one search cluster for everybody, then one per region once clie

Somewhere between 30 and 50 percent of an agent's tokens get spent on searching, almost all of it up front, before any of the work you asked for. Maximilian David Rumpf treats that number as the whole opportunity. Handing a task to an agent rather than a query to a search engine roughly doubles the odds of finding the right documents, but it costs a hundred to a thousand times more and takes minutes instead of milliseconds. His diagnosis of why the classical alternative cannot close that gap is the sharpest part. A traditional pipeline rewrites the query, hits a backend, reranks, and returns,

Ask a corpus of Seinfeld transcripts for the name of Jerry's favorite church and a 100 token chunk returns it at rank one, while every larger window buries it below rank fifty. Ask the same corpus who Jerry calls his nemesis and pure evil and the small chunks fail completely, because the answer is spread across a scene rather than sitting in a sentence. Same data, same index, opposite requirements. Yuval Belfer uses that pair to make a claim most retrieval teams have quietly assumed away: there is no correct chunk size, because the correct size is a property of the query, and you pick it at in

Full episode:https://youtu.be/PrSf7IOYu-I Me on twitter: https://x.com/dwarkesh_sp

Ali Ghodsi never wanted the CEO job. In 2015, Databricks had a wildly successful open source project, $1.5 million in revenue, and a board quietly interviewing other candidates behind his back. He got the seat as a trial run. They didn't even give him a CEO salary. When I had Ben Horowitz on this podcast and asked him who the best CEO in Silicon Valley is outside his own portfolio, he didn't hesitate. He said Ali. We get into how he actually runs the place: find the single biggest bottleneck in the company, then put an absurd, disproportionate amount of attention on it for two or three years.

John Ousterhout, Stanford Professor and author of A Philosophy of Software Design, turns our attention to the evolving nature of AI networking workloads and why traditional protocols like TCP and RDMA are becoming bottlenecks in modern data center environments! The Shift in Workloads Historical context: AI traffic was dominated by massive, long-running transfers (gigabytes of gradients), where throughput was the primary metric (2:19-2:42). Modern AI: Workloads, especially inference and agentic applications, now rely on frequent, small coordination messages (e.g., KV cache lookups, barrier sy

From their seed led by Nat Friedman and Dan Gross, AIUC has now announced a monster $40M Series A! From being Anthropic’s first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won’t be capability — it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible

Raindrop is building the safety layer for AI agents. As agents get more capable and take on more complex work, Raindrop detects when they go wrong in production, from failed tool calls and hallucinations to problems companies didn’t even know to look for. They’re already used by companies including Vercel, Clay, Framer, and Speak, and just raised a Series A, bringing their total funding to $50 million. In this episode of Founder Firesides, the founders sat down with YC's Diana Hu to share how a tool they originally built to debug their own coding agent became the company, why better AI agents

本篇为世纪风暴系列伊朗篇——告别伊玛目。

法治建设，它不可能一蹴而就，无法毕其功于一役，法治的每一个进步背后都有许多沉甸甸的故事，这些故事不应该被人遗忘，因为牢记历史就是为了避免重蹈覆辙。 参考文献： 1.陈永生，我国刑事误判问题透视———以20起震惊全国的刑事冤案为样本的分析，中国法学2007年第3期 2.对我国死刑复核程序之检讨———以中国古代及国外的死刑救济制度为视角，陈永生，比较法研究2004年第4期。