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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The Model Context Protocol (MCP) has become the default standard for connecting AI agents to external tools and APIs. Governed by the Linux Foundation since early 2025 and adopted by OpenAI, Anthropic, Microsoft, and Vercel, MCP is the USB-C port of the AI ecosystem — one protocol that lets any LLM application talk to any tool server. But there's a gap between reading the spec and building somethi