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....
This is the talk every backend engineer eventually watches. Jeff Dean walks Stanford’s distinguished lecture audience through eleven years of evolution in Google’s search infrastructure — from a single-machine inverted index in 1999 to a planet-scale system serving thousands of queries per second with sub-second updates. The value of the lecture isn’t the specific numbers; it’s how he reasons about each rewrite as a response to one constraint becoming unbearable, and the design patterns that survived across seven major rewrites....
Sierra powers customer-experience voice agents for a large chunk of the Fortune 20, and in this Interrupt-26 conversation Zach Reneau-Wedeen (Head of Product) walks through what their production agent harness actually looks like. The headline: a voice agent in production does not look like the canonical “LLM-in-a-loop calling tools” diagram everyone draws on whiteboards. It looks like a multi-model ensemble pipeline with speculative execution baked in. “Coding agents are good at file systems — let’s materialize everything into a file system” The opening framing is a useful contrarian take: coding agents have a runaway lead on capability because they happen to operate on substrates — file systems, Git, grep — that the underlying models were already extremely good at....
Anjney Midha (AMP, formerly a16z, board member at several frontier labs) sits down with Latent Space for an hour on a question that wouldn’t have made sense in 2023: why are well-funded AI labs with all the compute they need failing to ship? His answer isn’t compute, it isn’t talent density, and it isn’t model architecture — it’s culture, mission alignment, and the boring details of execution. The diagnosis: culture, not capital Midha opens with the observation that has been circulating quietly inside frontier-lab boards for months: many of the best-funded labs of the 2024–2025 cohort have all the cash and all the compute they need and still can’t ship competitive models....
Kuba Rogut, deployed engineer at Turbopuffer, gave one of the more refreshingly direct takes on the “RAG is dead” meme that’s been making the rounds on X. His argument, in one sentence: RAG isn’t dead — what’s dead is the strawman version where RAG means “embed everything once, run a single vector lookup, dump it into the LLM context.” The actual frontier is hybrid, tool-rich retrieval, where embeddings, BM25, grep, glob, regex, and filters are all tools an agent can compose iteratively....
A short on-camera conversation between Sam Whitmore (engineer on Cursor’s cloud-agents team) and Charlie + Harry of Pause AI, recorded at Baseten and posted by Cursor as part of its agent-era publicity push. The framing is intentionally provocative — “I’ve got 64 to 128 agents working on this at any given time” — but the substance is closer to a workshop chat between three practitioners who actually live inside agent harnesses all day....
This week’s classic pick is Joe Armstrong’s 2014 Strange Loop talk The Mess We’re In — a 45-minute polemic from the co-creator of Erlang on why software is getting worse, what the laws of physics say about how fast computation could be, and how we should stop using human-chosen file names. Armstrong died in 2019, but the talk has aged remarkably well: in the era of 128-parallel coding agents, his entropy critique reads less like nostalgia and more like a warning we’ve kept ignoring....