In July 2025, a developer's Claude Code instance hit a recursion loop and burned through 1.67 billion tokens in 5 hours, generating an estimated $16,000 to $50,000 in API charges before anyone noticed. The agent did not crash. It did not throw an error. It just kept calling tools, getting confused, calling more tools, and silently accumulating cost. Old software crashes. LLM agents spend. This is
akm 0.7.0 is out. This is the last pre-1.0 ship in the v1 cycle. The headline features are a durable proposal queue that routes all agent-suggested changes through a single reviewable path, three new CLI surfaces (reflect, propose, distill) that write into that queue, a lesson asset type for synthesized knowledge, per-call-site LLM feature gates that are all off by default, and a paired-run benchm
This is part eight in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part one introduced progressive disclosure. Part two unified your local assets across platforms. Part three added persistent memory. Previous parts addressed teams, distributed stashes, and community knowledge. This one is about a different problem: knowledge accumulation
This is part nine in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part one introduced progressive disclosure. Part two unified your local assets across platforms. Part seven covered shared team skills via Git repos. Ask an agent to ship a release and it will start confidently. It runs the build, opens the changelog, checks the branch. Th
You're in another app and there's a timer counting down at the top of your phone. You lock the screen and the same timer is sitting there. You swipe down to the Notification Center and it's there too, still ticking. It looks like a notification, but a notification can't tick. That's a Live Activity. It looks like three different surfaces (Dynamic Island, lock-screen banner, Notification Center ent
Long-running agents tend to fail in the second half. The first step is often fine. Fix a CI failure, open an app, tap a button, search for a keyword. Models can produce a reasonable first action. The trouble starts around step ten: what has already happened, where the task is stuck, what the original boundary was, and when the task is allowed to stop. Those details slide out of context. Codex CLI
I finished an English series on the way I think ordinary people can start using AI for real work. The point is not to become an AI expert first. The point is to have one place where you can say what you want, give the tool access to the right folder, and check the result. Anything important still needs a human pause: publishing, deleting, paying, or authorizing. My preferred starting point is simp
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