AI Success Anecdotes (funcall.blogspot.com)

Anecdotes aren’t data, but they can show what’s possible. The author shares two practical success stories using AI assistants: first, a cascade of tools where a high-level AI reviewed a vague JIRA feature request, designed a solution (an extra API endpoint) and broke it into stepwise instructions, which were then executed and unit-tested by a low-level coding assistant (Copilot). The second was feeding a PNG wireframe to a coding tool that not only recognized the UI mockup but also suggested useful workflow improvements. In both cases the human vetted AI-generated plans before executing them. The significance for AI/ML practitioners is twofold: method and implication. Method — a hierarchical, system-instruction-driven pipeline (planner AI + executor AI) can decompose vague requirements into precise, non-conflicting code changes and tests, improving reliability and developer productivity. Implication — multimodal assistants can meaningfully analyze UI images and propose UX/workflow fixes. Caveats remain: these are single anecdotes, not benchmarks; results don’t prove typicality but do validate feasibility. The takeaway: structured, human-in-the-loop prompt engineering and tool chaining are promising practical patterns worth experimenting with.