The October 2025 DynamoDB disruption spread through AWS after a race condition in automated DNS management erased the service’s regional endpoint. Its published cause was an automation defect, not AI-written code. That distinction matters. If we blame every outage on an LLM, we miss the specific mechanisms that made the outage possible. Continue reading →

I have written about Herdr as a place to see all the agents and about why I give spaces, tabs, and panes specific jobs. A multiplexer helps me find the agent that is blocked on a question. It does not tell me whether the agent should still be working on that question at all. Continue reading →

Two developers editing the same service at once is older than Git. One changes an API, the other writes a caller against the old shape. One migration lands, the other assumes the previous schema. The collision may wait until integration, but nobody invented it with coding agents. Continue reading →

Getting a coding agent productive is less about the prompt and more about whether the machine in front of it can actually build, run, and reach the systems the work depends on. Cursor, Claude, Codex, and the rest are fast when the local world is already honest: dependencies install, the app boots, tests can run,... Continue reading →

The text emphasizes the importance of a structured workflow for coding agents to prevent unintended changes. By introducing a three-step gating process—Ask, Plan, and Confirm—developers can ensure that agents check for ambiguities, outline necessary changes, and obtain explicit approval before implementation. This approach mitigates risks and enhances code quality. Continue reading →