Daily Brief

Full episode: https://www.youtube.com/watch?v=f6cjAEr08qk Me on twitter: https://x.com/dwarkesh_sp

Full episode: https://youtu.be/LwQQ7nBCGSs Me on twitter: https://x.com/dwarkesh_sp

A browser agent that's 99% accurate per step succeeds only about 36% of the time over 100 steps. Here's how to beat that math. Derek Meegan, software engineer at Browserbase, explains what separates a browser-agent demo from thousands of unattended production runs. He covers how agents see the web (text, screenshots and code), why cost builds up with every step while value only comes at the end, and why you should measure success per transaction, not per run. Then he walks through a real architecture for automating a health insurance portal: deterministic tools, OCR verification, pulled-out a

AI writes code faster than humans can review it. Verification is now your bottleneck. Gabriel Spencer-Harper, co-founder and CEO of Meticulous, explains why assertion-based tests can't keep up with AI-generated code, and what near-exhaustive frontend verification with zero developer effort looks like. He demos how Meticulous records real user flows, replays them on every pull request and shows visual before/after diffs. He also explains the three technologies that make it work: fully mocked network traffic, a deterministic browser and coverage-guided flow selection. In this talk: • Why the s

The web has roughly 30 trillion tokens of usable text. Frontier pre-training needs more. Here's how DatologyAI generates the rest. Bogdan Gaza, co-founder and CTO of DatologyAI, shares the engineering lessons from running synthetic data jobs at trillion-token scale, including a recent run of about 12 trillion tokens across web, math and code. He covers BeyondWeb, DatologyAI's rephrasing-based synthetic data recipe, and the move from a split Slurm/Kubernetes setup to a single Ray, KubeRay and vLLM pipeline on EKS on HyperPod. He then walks through four bottlenecks: S3 metadata, GPU failures, c

Coding agents made development faster. They also made production a lot harder to debug. Eric Schwartz, product manager at Traversal, explains why more AI-written code means more time spent troubleshooting, why observability dashboards tell you what broke but not why, and why root cause analysis is a causal problem rather than an observability problem. He lays out five levels of 'self-driving production' and shares case studies on alert intelligence for a PepsiCo supply chain team and incident root cause analysis at American Express. He closes with five questions to ask any AI SRE. In this ta

Halluminate (YC S25) is building reinforcement learning environments and benchmarks to help AI models do knowledge work beyond coding, starting with finance. With a team of fewer than 10 people, the company works with four of the top five closed-source US AI labs and recently raised a $30 million Series A led by Oak HC/FT. In this Fireside, co-founders Jerry Wu and Wyatt Marshall sit down with YC Partner Jon Xu to share how testing browser agents led them to building simulations where models practice tasks like completing financial spreadsheets. They explain why accurate scoring and expert re

From GPU kernels and KernelBench to Recursive Language Models, agent harnesses, and massive multi-agent swarms, Alex Zhang is exploring how much capability we’re leaving on the table by wrapping increasingly powerful models in primitive systems. In this episode, the MIT researcher behind RLMs joins swyx to unpack why Claude Code, Codex, and most coding agents are more similar than they look, why better harness design could unlock capabilities already latent in today’s models, and what happens when you can throw 10,000 agents and tens of millions of dollars at a single problem. We go deep on G

Gumloop (YC W24) is an AI agent builder used by companies like Shopify, Gusto, and Instacart. It lets employees build and share agents that automate work across their existing tools, while IT controls security, data access, and costs. In this Fireside, co-founders Max Brodeur-Urbas and Rahul Behal talk to Y Combinator General Partner Gustaf Alströmer share how better models transformed their visual workflow builder into an agent platform, what they learned from 1,100 customer calls, and how one power user helped them land Instacart. They also explain why bringing agents into tools like Slack

Which GPU clouds are worth trusting with your AI workloads? This Lightning podcast episode explores SemiAnalysis’s ClusterMAX 3.0 and the questions that matter when evaluating managed GPU clusters. About the research: ClusterMAX evaluates configuration, workload performance, reliability and response to failures through hands-on testing, documentation review and customer feedback. Explore the research: ClusterMAX 3.0 report: https://newsletter.semianalysis.com/p/clustermax-30-the-industry-standard Rankings: https://www.clustermax.ai/v3 Methodology: https://www.clustermax.ai/overview Evaluati