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?...
This week’s Classic of the Week is Rich Hickey’s 2010 Clojure Conj talk “Hammock Driven Development.” It’s one of the most-cited talks in the Clojure community and — read charitably — one of the least dated pieces of software-engineering advice from that era. Sixteen years on it reads almost eerily well as a critique of “just tell the agent to build it” culture: Hickey’s whole thesis is that the important work happens away from the keyboard, and we’ve built an industry that pretends otherwise....
Geoffrey Litt — design engineer at Notion, ex-Ink & Switch — opened the AI Engineer Design Engineering track with what he called a hot take: in 2026, it is still important for humans to understand how their code works. The framing sounds obvious until he lands the actual claim: agents are now writing 50,000-line PRs, and the practices that let a person stay a genuine participant in that project are not the practices that used to work....
Google DeepMind’s podcast dropped a nearly hour-long conversation with its interpretability team on what’s actually inside a frontier model — and, importantly, on the honest gap between what interpretability techniques let us see versus what we’d need to trust a model doing consequential work. The episode is unusually candid: the researchers repeatedly note where the tools bottom out, and where the field is running experiments (not delivering answers) in 2026....
Every so often the seminal-talk slot lands on something older than every framework it critiques. This one runs a full 27 minutes and it is not a re-enactment: it is the actual 1982 Bell Labs promotional film, with Brian Kernighan, Dennis Ritchie, Ken Thompson, Alfred Aho, Stu Feldman, and Lorinda Cherry on camera explaining, in their own voices, why the system they built looks the way it does. The AT&T Archives channel has now cleared it for wide release, so the classic-of-the-week pick this week writes itself....
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....
The Latent Space “Cooking with…” series put OpenAI’s Chief Research Officer Mark Chen in a kitchen and got him to talk through the things research-org leaders rarely say on the record: where scaling laws actually live in 2026, why post-training and RL are the real bottleneck now, how OpenAI structures evals against a moving frontier, and what “AGI” means when you’re inside the org that named the goal. This is one of the higher-signal AI Engineering interviews of the year — partly because Chen is unusually specific, partly because the format (informal, no slides, no PR minder) catches him in mid-thought....
This week’s Classic of the Week is Pat Helland’s 2017 Kafka Summit keynote — a 20-minute tour that maps five great physicists onto the laws of distributed systems. It’s the rare classic talk that you can hand to a junior engineer to teach them what “eventual” really means, and to a senior engineer to remind them why their replication protocol keeps surprising them in production. Helland has been building distributed systems since 1978 (Tandem, Microsoft, Amazon, Salesforce)....
Google DeepMind’s podcast sat down with Nenad Tomašev, senior staff research scientist at DeepMind, to ask a question that’s about to stop being theoretical: what happens when millions of agents start transacting, negotiating, and delegating to each other — not just to humans? The episode is one of the rare ones where a research lab talks publicly about open problems instead of product launches. With Gemini Spark, Antigravity, OpenClaw-style frameworks, and dozens of vendor agent runtimes all shipping, the multi-agent layer is now the part of the stack with the least standardization and the most unknown failure modes....