agents

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....

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....

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....

Two and a half years after the Devin demo went viral at 13% on SWE-bench, Cognition president Russell Kaplan sits down with Harrison Chase for the most candid accounting yet of what running coding agents at enterprise scale actually costs — and why the industry’s incentives are quietly pointed in the wrong direction. The central claim: a lot of the ecosystem is structurally motivated to get customers to token-max, and the bill is now coming due....

Jason Lopatecki, founder of Arize, opens by noting that his own team’s first agent “frankly sucked” — and that everything they’ve built since is downstream of debugging that failure in production. His AI Engineer talk lays out a concrete pattern for agents that repair themselves: production signal (traces, evals, human labels) feeds a second agent that opens pull requests against the first agent’s own prompts, tools, and skills. It’s the operational shape of the “self-improving system” idea, minus the hand-waving....

Weekly Paper Notes — one of the top picks from the 2026-07-25 CS paper digest. Area: AI / ML (Agent Training). Authors: Xiao Yu, Baolin Peng, Ruize Xu et al. arXiv: 2607.21557 · PDF TL;DR Modern agents are shaped less by their base model and more by the harness wrapped around it — Claude Code, Codex, OpenClaw, GUI-use scaffolds. But those harnesses are stateful, multi-process, and full of tool orchestration, which means existing open SFT/RL stacks (veRL, TRL, OpenRLHF) can’t natively express a rollout inside one....

Prompt engineering was the primitive of 2023, context engineering owned 2024–2025, and 2026 is settling on a new one: loop engineering — the discipline of designing the feedback loops that surround an agent, not just the agent itself. Sydney Runkle (PM on LangChain’s open-source team) makes the case in this webinar that the durable advantage is never the agent, it’s the loops built around it. Why loops, not agents Runkle opens with a simple framing: a model has some fixed level of intelligence; a harness wrapped around it converts that intelligence into useful work on a specific problem....

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....

Multi-agent architectures have quietly become the default for anything more ambitious than a single-turn chat, but when to reach for them — and how to keep them from blowing up your token budget — is still guesswork in most codebases. In this 26-minute LangChain talk, Colin Flaherty lays out a clean, prescriptive mental model built on top of Deep Agents: sub-agents are just a way to parallelize work and control what returns to the parent context, and there are exactly four patterns worth memorizing....

Latent Space caught Gavriel Cohen, founder of NanoClaw / Nanocloud, at the AI Engineer conference in Singapore for a 23-minute conversation on how to design work agents that don’t fall over the moment they hit real enterprise data. This is a rare interview where the founder skips the vision-slide fluff and just names the four decisions he made and why. Worth a full watch if you are shipping an agent-in-Slack this quarter....