Daily Brief

Rich Sutton, who helped pioneer reinforcement learning and wrote the seminal AI essay The Bitter Lesson, has now cofounded Oak Lab with his former student Khurram Javed. Their goal: to build agents that continuously learn from their own experience rather than from us. Rich doesn't think he holds a radical view: "I'm not weird. The field is weird." He says all learning is continual, and the field is the one that needed a new name for it. Rich and Khurram argue synthetic data is "a big mistake." Their "big world hypothesis" is that the world is massively more complex than any agent or simulator,

a16z's Joel De La Garza is joined by Nick Warner of Neo and Max Pollard of Cotool to discuss what happens when cybersecurity tools built to defend against humans and malware suddenly have to contend with AI agents. As frontier models become more capable of finding and exploiting vulnerabilities, many of the assumptions underlying traditional security are beginning to break. They explore why guardrails designed to stop AI-powered attackers can also prevent security teams from doing their jobs, why defenders increasingly need access to multiple models, and how agentic software creates an entire

Part of a series of monthly puzzles with MoMath.

An avatar of Teddy Roosevelt holds court in a replica Oval Office, generating video continuously for eight hours with no reset, and a second deployment is being built to run for sixteen. That duration is the hard part. Sidney Primas explains that a real time avatar can only look backward, because the future frames do not exist yet, so every block it generates inherits the errors of the blocks before it and compounds them. LemonSlice trains with an attention mask that enforces this during training rather than discovering it at inference, and collapses roughly 30 denoising steps down to a single

Ten dollars now buys roughly three hours of continuously generated video, and fifty buys fifteen. Keegan McCallum sets that against the room's own habits, since plenty of hands went up for burning that much on coding tokens inside a single hour. His argument is that the interesting axis in generative video stopped being quality a while ago. Put a real time generation next to one that took minutes and the slower clip still has better motion, but it cost on the order of a hundred times more to produce. Helios, the model he serves, is a distillation of a 14 billion parameter open model, and it i

GPU utilization is a lie. It read 100% straight through pretraining while the cluster was nowhere near well used, so Gabriel Jorge Menezes tracks tensor core utilization instead, and watched it climb as training resolution stepped from 128 pixels up to 1024. That is one of several numbers he argues you cannot train at this scale without. InfiniBand counters are exported by nothing off the shelf, and most of their failures turned out to be cross node communication, so they built that collection themselves. Any GPU running hotter than 78 degrees gets pulled rather than debugged, because one warm

Uber could not exist without GPS. Ahmed Ahres uses that to argue real time is a change of medium rather than a speedup: before GPS you consulted a map somebody else had already made, and afterwards your own position became something you could act on continuously. He runs the same argument through film. Once a viewfinder showed you what you were shooting, you could adjust while shooting, and that is the reason Instagram and TikTok were possible at all. Generated video today sits on the wrong side of that line. You prompt, you wait, you get a file back, and there is nothing further to do with i

Ayush Bhardwaj could build the agent. What he could not do was tell whether it was any good. He moved from applied AI at a hedge fund to a pharma tech company expecting a different world, and found the job identical, including the wall. An engineer glances at generated code and knows instantly that it is weak, because years of training built that judgment. Nobody on his team had the equivalent instinct for a trade thesis or a drug candidate. He calls this the point where vertical AI projects quietly die, because the thing looks finished and then nobody buys it. Judging his way out with a mode

Implementation used to be the expensive step, so teams spent weeks settling requirements before anyone wrote code. Dan Feng's observation is that the cost moved. Building takes minutes now, and arguing is what is expensive. Planning at Maven Clinic changed to match. A one year view survives only as direction, assuming models will handle whatever you need by then, while real commitment runs two to four weeks. Long requirement documents gave way to a page or two meant to be argued with. The awkward casualty is the three to six month plan, which he treats as close to unplannable when nobody knows

Roughly 70% of medical communication still moves by fax. What reaches Anterior is scanned fax bundles that can run past 300 pages, carrying handwriting, checkboxes, tables and images across one patient's entire clinical trajectory. Anuj Iravane calls it an observation through a fuzzy lens over a lifespan. It is exactly the data his evals need, and the data he is least allowed to keep: their contracts rule out retaining it, deriving from it, or holding redacted or anonymized copies. Nothing survives into a dataset. In a domain where 95% accuracy is not good enough, that is a real problem. So t

The proof of concept works. It hits the accuracy targets, it is fast, it is cheap, and the room is happy. Then someone from compliance raises a hand and asks to see the audit trail, and the whole thing stops. Christopher Lovejoy and Saul Howard have watched that meeting happen repeatedly, and their point is that an audit trail is not a developer log. Under the frameworks enterprises actually answer to, it is a complete record of every action an agent took, every place it touched data, and the authorization behind each one, durable enough to stand up as a chain of evidence if the decision were

In this fireside from Startup School Paris, PostHog CEO James Hawkins talks about surviving five failed ideas, why going open-source nearly trapped the company in enterprise on-prem debugging hell, and how a hackathon side project by one engineer turned into a multi-product strategy neither founder had planned. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 — Intro 00:46 — The Rule: Sleep on It Once 02:13 — Why Pivots Were the Real Education 02:31 — Birth of Open Source Analytics 05:14 — How They Knew This Idea W