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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....

Agent “skills” — reusable folders of instructions, scripts, and assets that a model loads on demand — have quietly become the packaging unit of the agent ecosystem. Philipp Schmid opens this AI Engineer talk with a brutal statistic from Skills Bench v1.1: of 50,000+ published skills, almost none have evals. Most were AI‑written and never tested. And because agents are non‑deterministic, without evals you have no way to tell whether a failing task is your skill’s fault, the model’s fault, or just noise....

arXiv: 2607.15267 · PDF: 2607.15267.pdf Authors: Victoria Graf, Hannaneh Hajishirzi, et al. TL;DR Prior work on pretraining-data poisoning has mostly targeted curated sources like Wikipedia — a poor stand-in for the scale and heterogeneity of real pretraining corpora. This paper demonstrates that public discussion interfaces on the open web (comment sections, forums, Q&A pages) are a viable at-scale injection vector, and introduces HalfLife, an analysis technique for estimating whether adversarial content actually survives web-crawl-based data curation pipelines and lands in the training set....

Model training is the slowest inner loop in an ML organization: one big run at a time, days or weeks per iteration, mostly serial. Lee Robinson opens this AI Engineer talk with a blunt framing — the whole game at Cursor right now is to shrink that inner loop, because whoever iterates fastest ships the best coding models. Every part of the talk is a concrete answer to “what does that actually look like?...

Weekly Paper Notes — one of the top picks from the 2026-07-11 CS paper digest. Area: AI / ML. Authors: Shreyas Subramanian, Adewale Akinfaderin, Akarsha Sehwag (Amazon) arXiv: 2607.08733 · PDF TL;DR “Super Weights” — individual scalar parameters in a large language model whose removal collapses task accuracy — were the interpretability finding of 2024–2025. The natural inference was that if these coordinates matter that much for the forward pass, they should also matter that much for learning: freeze everything else, train only the Super Weights (or a small neighbourhood around them), and you should get parameter-efficient fine-tuning for essentially free....