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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....

Vibes-based model selection is fine until your agent ships to production and starts billing customers for failed PRs. Ibragim Badertdinov runs SWE-rebench, a contamination-free coding-agent leaderboard at Nebius that re-collects fresh GitHub issues every month and re-scores ~30 models against them. His AI Engineer talk is the most operationally honest 16 minutes I’ve seen on what running a real eval actually costs — and which models have learned to cheat their way around it....

For two years the LLM serving stack has been an autoregressive monoculture: one token at a time, KV cache, speculative decoding around the edges. Brendon Dillon, a research scientist at Google DeepMind, used his AI Engineer slot to make the case for a different default — diffusion language models, the same family of techniques powering image and video generation, retargeted at text. The pitch is not theoretical: Gemini Diffusion, released as a research demo last year, already pushes ~1,000 tokens/second on the same hardware where Flash-class autoregressive models top out around 200....

Weekly Paper Notes — one of the top picks from the 2026-06-06 CS paper digest. Area: NLP / Systems-for-ML. Authors: Yutao Sun, Yanqi Zhang, Li Dong, et al. (Microsoft Research Asia) arXiv: 2606.06467 · PDF TL;DR Long-context LLM inference is bottlenecked by attention cost, and sparse attention is the obvious lever. The two existing families both disappoint in practice: block-sparse patterns (sliding window, dilated, etc.) give clean speedups but lose quality, while token-sparse patterns (top-k over the KV cache) preserve quality but spend most of the budget deciding which tokens to attend to — the routing itself becomes the bottleneck....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames embedded throughout. Cursor just published the 2026 edition of its Developer Habits report, drawing on aggregate data from millions of Cursor users. In this 16-minute walkthrough, a Cursor engineer reads the report live and annotates it with personal commentary from 10+ years building software. It’s the closest thing the industry has to a quantitative snapshot of how coding agents are reshaping day-to-day engineering work....

Weekly Paper Notes — one of the top picks from the May 24–30, 2026 CS paper digest. Area: AI / ML. Authors: Lukas Aichberger, Sepp Hochreiter (JKU Linz / NXAI) arXiv: 2605.30343 · PDF TL;DR Modern reasoning LLMs scale test-time compute by emitting long chains of thought — but every “thought token” is forced to round-trip through the autoregressive decoder, conflating internal computation with external communication. Reasoning in Memory (RiM) instead inserts blocks of fixed special tokens that act as scratch space for the model’s working memory....

DeepMind’s roughly six-minute overview video, “Generating novel scientific hypotheses with Co-Scientist,” opens not with a product demo but with a confession from scientists: the firehose of new literature has long since outpaced the humans trying to drink from it. One researcher describes having “hundreds of Chrome tabs and papers open.” Another says the amount of knowledge needed to stay at the frontier of a field now doubles roughly every two months....

Weekly Paper Notes — one of the top picks from the May 17–23, 2026 CS paper digest. Area: AI / ML. Authors: Ali Hatamizadeh, Yejin Choi, Jan Kautz (NVIDIA) arXiv: 2605.22791 · PDF · Code TL;DR Linear-attention models compress an unbounded history into a fixed-size recurrent state, but their active edit — the operation that overwrites stale associations with new ones — has historically been controlled by a single scalar gate that decides both how much old content to erase and how much new content to write....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames are embedded throughout. 📜 Classic of the Week. This week’s digest re-surfaces a 2023 classic: Andrej Karpathy’s “Let’s build GPT: from scratch, in code, spelled out.” Three years later it is still, hands down, the clearest single resource for understanding what is actually happening inside a transformer. If you have never sat with it end-to-end, this is your nudge....