Digest
Six deep reads on agent harnesses, secret boundaries, human-centered workflows, WAL-RUS, open-model incentives, and AI economics.
GLM 5.2 scores 39% F1 on IDOR detection, ahead of Claude Code's 32%, but Semgrep's own multimodal harness reaches 53-61%; the useful comparison is the full system of model, context selection, output parsing, and execution loop.
Ignore files reduce noise and express intent, but if the agent process can still read a secret, tool output, search results, and logs can leak it; the real boundary has to come from the OS, containers, VMs, or least-privilege credentials.
Jon Udell argues against reducing people to approval buttons; the better design keeps human plans, queues, review, and history as the main loop, with agents joining through visible, recoverable small steps.
ClickHouse's Rust rewrite of its WAL archiver matters less as a generic speed story than as a resource-predictability story: under WAL-heavy load, virtual memory falls from nearly 2.8GB to under 1GB.
Open-weight releases are no longer a single movement led by a few players; pure model makers, Big Tech, product companies, and sovereign AI efforts all open models for different economic reasons.
Gary Marcus reads China's model catch-up as a no-moat story: more competitors, lower token prices, thinner margins, and a costly paradigm whose capability lead may not become a durable business moat.