llm

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....

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....

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....

Weekly Paper Notes — one of the top picks from the 2026-06-27 CS paper digest. Area: AI / ML. Authors: Josef Chen arXiv: 2606.27288 · PDF TL;DR The paper formalizes a hard accuracy ceiling for any multi-model LLM system whose final output is one member’s answer — routing, voting, cascades, fusion, mixture-of-agents. That ceiling is 1 − β, where β is the co-failure rate: the fraction of queries on which every model in the pool is wrong simultaneously....

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....

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....