Daily Brief

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

Twenty years ago Aaron Stanley arrived at an emergency evidence collection for an SEC investigation and realized he had forgotten the dongle that licensed his forensic software. Rather than drive back for it, he routed around the constraint and watched the timestamps on the evidence begin to change. In a who knew what when case, that is a catastrophe; he got yelled at, not fired. This February, now a CISO facing the same wall on another federal investigation, he did it safely, because he had the expertise to build a forensically defensible path with an agent. His point: the agents we build tod

Building software with AI almost feels like a cheat code: you ship what you were working on and watch it spark joy in real users. The catch, and the reason Randall Degges is opening the World's Fair's first Security Track, is that three things still stand in the way of doing that at scale. AI writes insecure code just like humans do, autonomous agents in production can go off the rails while you sleep, and access to frontier models keeps getting pulled out from under you for what amounts to geopolitics. It all reduces to one unsolved problem: using AI fearlessly and having it be secure by defa

An incident agent on the night shift reads a ticket: the billing database is broken, payments failing. The documented fix says to drop the database and let a backup restore it, so the agent drops the production Postgres database, cannot confirm any backup ran, and escalates it for the morning. This has happened to real companies. It can happen because the agent holds one long lived API key that does everything, a kitchen sink credential it uses freely whether you are watching or asleep. Kim Maida's fix is not a new invention but an old OAuth spec, token exchange, wired into the agent's execut

Ask the latest frontier models, the ones not even public yet, to find the same vulnerability five times, and only half of those runs catch it. Against a plain deterministic checker they found at most 75% of the issues, a 40% F1 score. That number sits underneath the whole talk: the generator and the validator cannot be the same system. Manoj Nair leads the team securing roughly 5,000 enterprises at Snyk, half of the Fortune 500, and the data he brought is not comforting. Across 4,800 customers, security backlog grew 108% quarter over quarter, because agents writing code faster are also manufac

@TheAhmadOsman shows the power of local AI on stage, running frontier open models on a DGX Station. Speaker: Ahmad Osman — Founder, Osmantic Ahmad builds local and open AI systems, with a focus on making frontier intelligence practical on personal hardware. Links: X: https://x.com/TheAhmadOsman LinkedIn: https://linkedin.com/in/TheAhmadOsman Website: https://ahmadosman.com/ timestamps 0:00 Introduction and the Desktop Frontier concept 0:47 Future predictions: GLM 5.2 on an RTX 5090 1:17 Efficiency over raw size: The move toward compact intelligence 1:51 The concept of impact per parameter

A short history of the right way to build an agent: RAG, ReAct, prompt chaining, orchestrator-workers, MCP, CLI, MCP again... CLI again?? Every time you adopt a trend you rebuild your architecture. In this talk, Dan Farrelly, Inngest cofounder and CTO, is not going to tell you what comes next. He's going to show you how to build so it doesn't matter. He'll cover the core primitives that show up in every production agent, how bringing decisions closer to code provides more stack flexibility, and why the right execution layer unlocks faster iteration. ### Dan Farrelly CTO and Co-founder · Innge

Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO explains how the company survived its "journey in the desert," including the decision to hand nearly all of its revenue back to customers when the product wasn't making developers obsessed. Matan makes the contrarian technical case that a model-agnostic harness beats the model-and-harness co-design that labs like OpenAI and Anthropic favor, because exposing a harness to many models keep

Barr Yaron shares her perspective on the results and emerging state of AI engineering in 2026. Speaker: Barr Yaron — Partner, Amplify Partners Barr backs founders building the AI infrastructure and applications that will shape the future. Links: X: https://x.com/barrnanas LinkedIn: https://linkedin.com/in/barryaron Website: https://barrchives.com Timestamps 0:00 Introduction and Survey Context 2:26 The AI Engineering Workforce 3:21 Current Modalities and Adoption 5:34 Model Strategy: Closed vs. Open-Weight 8:20 Cost as an Engineering Constraint 9:36 The Rise of Agentic Workflows 11:57 Infr

If everyone woke up sick tomorrow, coding-agent usage would drop to near zero. Matan Grinberg of Factory on the shift from copilot mode to agent-native software development.

LLMs are great at writing code. So the question we kept asking was: can they write code that produces a video? We thought it would be easy. The reality was a year of trying. We started with massive prompts to get very mediocre output. We made it more agentic to iterate and improve its output. This worked okay but wasn't production-ready. Eventually we tried Remotion. It got us deterministic video, but the React framework kept boxing the agent in. The more guardrails we added, the safer and more boring the outputs got. When we utilized plain HTML, CSS, and JavaScript, the creativity came back t

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

YC and Together AI are partnering to bring the first dedicated YC GPU cluster online, giving YC startups easier access to the compute they need to build and scale. In this episode of Founder Firesides, YC's Ankit Gupta and Together AI co-founder and CEO Vipul Ved Prakash dig into why compute has become one of the biggest bottlenecks for modern AI companies. They’ll discuss how Together AI is helping more than 8,000 customers, from early-stage research teams to companies like Cursor, Cognition, and ElevenLabs, train, fine-tune, & run inference on AI models, and why flexible access to GPUs is be

Can a model predict how a cell responds to a genetic perturbation it has never seen? Xaira Therapeutics' new virtual-cell model, X-Cell, is a 4.9-billion-parameter diffusion language model trained on X-Atlas/Pisces — the largest genome-wide CRISPRi Perturb-seq dataset ever built, spanning 25.6 million single cells across 16 biological contexts. Bo Wang (Chief AI Scientist) and Ci Chu (Chief Discovery Officer) explain why observational atlases can describe biology but can't predict what happens when you intervene, why they abandoned autoregression for a diffusion "editing" approach, and how a m