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Ilya Sutskever opens this Simons Institute lecture by admitting he almost gave the standard talk and decided against it. Instead he presents a set of results from years earlier at OpenAI, never published, on a question that had bothered him: supervised learning comes with a mathematical guarantee, and unsupervised learning does not. His claim is that it can, if you frame unsupervised learning as compression. This is the “Classic of the Week” pick, and it earns the label for a specific reason....

Two papers landed on arXiv the same day, from Cornell and TU Darmstadt, with the same idea: skip the general-purpose query engine and have an LLM agent compile each SQL query directly into specialized C++. GenDB and BespokeOLAP report 10x over DuckDB on TPC-H, 460x over Postgres, and per-query wins up to 1,466x. Alex Kouzemtchenko, CTO of Espresso AI, walked Papers We Love Brooklyn through both — and the interesting part of the talk is the half spent on what the numbers don’t say....

Anima Anandkumar spent 83 minutes on Latent Space explaining a specific architectural claim: transformers are the wrong tool for physical simulation, and the reason is not compute budget but a mismatch in what the architecture assumes about its inputs. She has the receipts — her group’s weather model, FourCastNet, is deployed at a national weather agency and runs tens of thousands of times faster than the numerical models it replaces....

Most conversations about making agents better are really conversations about making models better. Alex Krentsel — a Berkeley systems PhD student working with Martin Casado and Ankur Goyal — spends this Latent Space episode arguing the opposite: the model is fine, and the leverage now lives in the harness, the “body we provide to the brain.” Exo, the project he introduces, is an agent that can safely edit every aspect of itself at runtime....

Andy Pavlo opens CMU’s database systems course with a claim that reframes most of what practitioners argue about online: the problems are not new. The hardware changed; the problems didn’t. This week’s Classic of the Week is his Lecture 1 — course information plus a compressed history of database systems from the 1960s to the present. It’s the single best hour for understanding why databases look the way they do, and it comes with an implicit warning: if you don’t know this history, you will spend your career re-litigating settled debates....

When Anthropic announced it would watermark text output from Claude models, Sebastian Raschka posted a short explanation of the mechanism. The post went unexpectedly viral — not because watermarking is exciting, but because almost nobody could say concretely what it does. He planned a ten-slide follow-up. It became fifty. The result is one of the better explainers of the year, and it doubles as a clean walkthrough of how LLM sampling actually works....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames embedded throughout. Memory was the theme running through the whole conference, and this 19-minute talk is the most empirically grounded take on it. Shlok Khemani, working independently, did something nobody else bothered to do: he sat down and reverse-engineered the memory systems of the major consumer AI products — ChatGPT, Claude, Gemini, Poke — by probing them, extracting raw profiles, and reading the tool calls....

Weekly Video Notes — Classic of the Week. A foundational talk revisited, with source video and key frames embedded throughout. Most Turing Award lectures are a victory lap through the technical contribution. Stonebraker refuses. He shows up without the tie the videographer asked for (“that just isn’t me”), announces that the audience isn’t a database crowd so he’ll have to teach them some database first, and then delivers 75 minutes braiding two stories together: the design and near-death of Postgres, and a tandem bicycle trip he and his wife rode across America....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames embedded throughout. Sara Hooker has done the full tour — PhD, DeepMind, several frontier labs, a research career built on efficiency at scale. Which is what makes this talk land: it’s an insider arguing that the system that produced her is an unreasonably narrow filter, and that the economics of the current moment are finally prying it open....

Fourteen years on, this is still the talk that most reliably rewires how an engineer thinks about their own work. It is remembered for the live-coding demos — and those demos did seed a decade of tooling, from hot reload to Swift Playgrounds to every notebook environment you use. But the demos are the setup. The talk’s actual argument arrives in the last fifteen minutes, and it is not about programming at all....