Lauren Tan, a former Cursor engineer now building Grok Bot at SpaceXAI, says she merged 1,000 PRs to production in July and 2,462 in August. The story spread as 'she doesn't look at the code', but she answered that she reads what her agents land after the fact and turns what she finds into lints and structure. The job pre-merge human review used to do is split across three mechanisms: evidence from running the real app, a verdict from an agent that did not write the code, and a codebase where rules fail CI. I read the transcripts of two talks, her posts on X, and the code and 90-commit history of pstack, her open plugin, to see how each mechanism is built, and reproduced the patch-id rule that ties a verdict to a patch. After covering token usage and the numbers nobody has audited, the last section lays out the order in which a team on a normal budget could adopt this.

Around September 20, Google published AX, an agent orchestrator written in Go. The README promises 'billions of autonomous agent workloads in a cluster', and a 661-point Hacker News thread split over that number and the prerequisites. Reading the design doc and the code shows AX is not an agent framework. It treats one agent as one sandbox, declares it with four manifests (Task, Workspace, Gateway, Model), and applies, suspends and resumes it the way kubectl does. Execution belongs to a separate project, Agent Substrate. This post checks how far the code goes on what people wanted from AX (idle-agent cost, isolation, repeated environment setup, an auditable stack), what is still on the roadmap (idle detection, exit status, workload identity), and which teams can try it today.

As complaints pile up about coding agents forgetting their own work after context compaction, more 'lossless' memory projects are appearing. Reading the code of lossless-memory (Show HN, September 21) and the OpenClaw plugin lossless-claw shows both store every original message but cut or summarize what goes into the context. They differ in where the loss happens and on what basis. And Claude Code already saves every message and tool result locally, deleting them after 30 days. Loading 2,000 lines of synthetic log into lossless-memory and searching by date returned 650,000 characters at once. When you keep everything, the real decisions are retention period, what to keep, how things enter the context, output limits, and cache placement.

How do you engineer an LLM agent harness for a domain where wrong answers are expensive? Using Korea's ISMS-P (102 controls) as the vehicle, Claude Code reviews and audits through a domain-experts → interviewer → auditor pipeline. 10 reusable harness patterns + honest limits.

Public domain novel characters as AI agents. RAG + cognitive engine over the original text -- talk to Sherlock Holmes.

A native macOS meeting notes app. Real-time recording, automatic transcription, and AI-powered summaries -- all processed locally with on-device STT and LLM.