We write about AI workflows, Claude Code, specifications, automation, and delivery processes from the perspective of people who actually build and maintain software.
AI adoption in development doesn't start with buying a tool. It starts with mapping workflows, risks, review rules, and a clear answer to where the team actually loses time.
The most dangerous modernization is the one that promises a clean slate. In practice, better results often come from clear boundaries, incremental changes, and parallel delivery.
The AI that helped write a feature often shares the same blind spots. That's why for important reviews, I use a separate Claude Code subagent with a clear, read-only task.
Writing a good GitHub issue on a phone is slow. OpenClaw, Signal, and a small custom skill turn a brief note into a structured issue without expanding permissions.
A pile of open tabs isn't knowledge. This workflow uses Claude for source selection, NotebookLM for synthesis, and Obsidian for notes that feed back into daily work.
If every developer re-explains the same conventions to the AI tool, the process hasn't scaled. Skills turn repetitive instructions into a versioned team standard.
A large context window sounds like a solution: throw everything in and let the model decide. In practice, excess context often hides the problem, weakens focus, and increases the risk of wrong conclusions.
AI can speed up development, but without specifications it often accelerates chaos. A spec-driven approach gives the model clear boundaries and the team a better basis for review.
We can help evaluate an AI workflow, improve your software delivery process, or take on a specific part of implementation.
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