Daily Brief

An agent is a harness orchestrating a model and context. If you want to own your intelligence, you probably want to own all three. LangChain co-founder and CEO Harrison Chase focuses on the harness: the loop that brings the right context to the model at the right time, and how to customize it with middleware, hooks, and sub-agents. At Sequoia Capital's Own Your IntelligenceI event, Harrison explains when an off-the-shelf harness is enough and when to build your own (the more out-of-distribution your task, the more customization you'll want) and why even custom harnesses should keep in-distrib

Today's AI models keep getting smarter, but every conversation still feels like their first day on the job. Trajectory co-founder Arjun Karanam calls this the experience gap: models are improving on IQ, but not on experience. Continual learning aims to close it, turning the trillions of tokens of agent interactions that get thrown away into signal that makes agents better with every use. At Sequoia Capital’s Own Your Intelligence event, Arjun lays out four goals for companies that want to get there: full traceability (including sub-agents and corrective behavior like edits and retries), evals

Full episode: https://www.youtube.com/watch?v=-RXD4bTuFTo Me on twitter: https://x.com/dwarkesh_sp

Preparing this talk got Corey Gallon a warning from OpenAI that his account faced a ban for cyber abuse with a web browser. The offending work was an agent clearing Cloudflare Turnstile, two image captchas, and finally reCAPTCHA v2 with no human in the loop. His premise fits on one slide: a browser driven through the Chrome DevTools Protocol is just a meat bag with a mouse, because the agent's clicks and keystrokes travel the same path inside Chrome that yours do. Chrome stamps every event as trusted or untrusted, which is why a synthetic JavaScript click that works fine in Outlook gets silent

Asked to file an expense, the agent gets signed out mid task, reasons that it can infer the password, guesses twice, and locks the account. In a second run it clicks a sponsored button styled like the real submit button, lands on a different site, and begins typing personal details into it. Both are real trajectories from early browser training runs at the Amazon AGI Lab, and Gaurav Mishra's summary is that RL worked while the world was a game, and IRL starts when the game fights back. The talk catalogues what a reward function meets on contact with a real login screen. Observability is parti

Just past 15,000 queries, renting context stopped being the cheaper option. Omer Primor gets that number from a small experiment he is careful to call a test rather than a benchmark: enrich one company across 25 fields, run it 100 times against this event's sponsors, and compare AI search products, context as a service vendors, and a scraper pipeline built in roughly a day. Pricing a week of setup at $5,000, the build it yourself path crossed over a little above 15,000 entities, and he expects the real crossover sits lower than most teams assume. The argument underneath is about frequency rat

Minutes into a call to demo a search API rebuilt to answer in under a second, the system got blocked, badly, in front of the client. Patricija Žemaitytė treats that as the useful distinction: something that works in development, something that passes tests, and something that survives reality are three different systems. The rebuild had no trick to it. Browsers are slow, expensive, and incompatible with low latency, and they were unavoidable, so the team went hunting for time across layouts, parsers, sessions, and proxies until the seconds were gone. It averages 550 milliseconds now, against a

Full episode: https://www.youtube.com/watch?v=-RXD4bTuFTo Me on twitter: https://x.com/dwarkesh_sp

In this presentation from Startup School Paris, Y Combinator General Partner Tom Blomfield shares findings from his research on how companies are being built with AI infrastructure placed in from the start. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 — Intro 01:53 — Why Roman Legions Built Your Org Chart 03:56 — Humans as the Bottleneck 05:28 — What a Real AI Loop Looks Like 07:51 — The Data Agent That Changed Everything 08:39 — The Self-Improving System 10:32 — Office Hours → Living User Manual 12:22 — The AI