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A year ago Greg Kroah-Hartman told a security conference the Linux kernel was issuing 50 CVEs a week and everyone thought that was insane. The kernel is now issuing 33 a day. His Kernel Recipes 2026 talk is aimed at kernel developers and maintainers, not the general public, and its refrain is “do not panic.” What makes it worth an hour is that he backs the refrain with raw data: he got the full report behind this year’s most publicised AI bug hunt and went through it line by line....

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....

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 Paper Notes — one of the top picks from the 2026-08-15 CS paper digest. Area: AI / ML. Authors: Julian Minder, Viktor Moskvoretskii, Raghav Singhal (equal contribution), Difan Jiao, Andy Arditi, Shaobo Cui, Jannik Brinkmann, Ashton Anderson, Roland Aydin, Robert West, and others — EPFL, MATS, University of Toronto, Saarland University, Northeastern, SJTU, DFKI, Ontocord AI, Hereon/TUHH arXiv: 2608.13482 · PDF · Models & data TL;DR Every production language model today learns what the world is like during pretraining and only learns who it is supposed to be afterwards, during post-training....

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....

Stanford’s CS329A is one of the few graduate courses aimed squarely at the thing practitioners are actually building right now: agents that improve themselves. This first lecture is the map — a compressed tour from GPT-3-era scaling laws to the agentic loop inside Claude Code, delivered by two instructors who worked on the models in question. Akanksha Chowdhery is an adjunct professor at Stanford and researcher at Reflection AI; Azalia Mirhoseini is an assistant professor in the CS department who worked on Gemini at Google DeepMind and on Claude at Anthropic....

Most retellings of the modern AI wave start with ChatGPT arriving out of nowhere in November 2022. Ross Taylor has a different vantage point: he shipped a competing language model two weeks earlier, watched it get torn apart in public, and spent the following four years working out exactly why. This talk is the compressed version of that education — half war story, half technical agenda for what comes after the current generation of agents....

Weekly Paper Notes — one of the top picks from the 2026-08-01 CS paper digest. Area: Operating Systems / Formal Methods. Authors: Qian Cheng, Ruize Tang, Yu Huang (Nanjing University); Saad Mohammad Rafid Pial, Yiming Su, Tianyin Xu (University of Illinois Urbana-Champaign); Emilie Ma, Finn Hackett, Ivan Beschastnikh (University of British Columbia) arXiv: 2607.25333 · PDF · Code TL;DR Formal verification of real systems has always been bottlenecked by the same thing: writing a good TLA+ specification takes a domain expert months, and the specification immediately begins drifting away from the code it describes....