Sparse Notes

arXiv: 2607.13276 · PDF: 2607.13276.pdf Authors: Marc Brooker, Marc Bowes, et al. (Amazon Web Services) TL;DR Aurora DSQL is AWS’s new serverless, PostgreSQL-compatible OLTP database designed for multi-region active-active writes. The architecture disaggregates compute (Firecracker MicroVMs running stateless SQL), storage, and transaction coordination into independent horizontally-scalable services. It uses MVCC with precision timestamps for coordination-free reads and optimistic concurrency control for writes, deferring all coordination to commit time via distributed adjudicators and a Journal replication tier....

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....

Original: Leslie Lamport, Paxos Made Simple, ACM SIGACT News 32(4), December 2001. Canonical PDF: lamport.azurewebsites.net/pubs/paxos-simple.pdf Predecessor: The Part-Time Parliament, ACM TOCS 16(2), 1998 (the “island of Paxos” allegory that nobody could read). Why “made simple” Lamport originally described his consensus algorithm in 1998 in The Part-Time Parliament, a paper framed as archaeological reconstruction of the parliamentary procedures of an ancient Greek island. It was a joke. It was also, by broad consensus (pun deliberate), unreadable — reviewers hated it, adoption was near zero for years, and even engineers who wanted to build on it complained they couldn’t....

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....

Jonathan Blow gave this hour-long talk at DevGAMM in 2019, and in the seven years since it has quietly become one of the most-cited critiques of modern software engineering. Blow — the game designer behind Braid and The Witness, and the creator of the Jai programming language — makes a claim most programmers instinctively resist: technological knowledge is not on a monotonic climb. Civilizations lose capabilities all the time. Ours is probably losing them right now, and software is one of the leading indicators....

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....

Weekly Paper Notes — one of the top picks from the 2026-06-20 CS paper digest. Area: Distributed Computing. Authors: Jiasheng Zhou, Longbin Zeng, Clavis Chen, Ruiming Lu et al. arXiv: 2606.20374 · PDF TL;DR ARGUS is a tracing and performance-diagnosis system designed for always-on operation on production LLM training clusters with more than 10,000 GPUs. The central insight is that no single profiler can be cheap, deep, and continuous all at once — so ARGUS decomposes observation along the training call hierarchy into three independent collection channels: CPU call stacks, framework semantics, and GPU kernel execution....