Claude Code Conversations with Claudine

AI-generated code usually works on the happy path, but it ships without the logging, tracing and decision points a human engineer would have added while debugging it into existence. Code that was never debugged by a...

Most builders still treat Claude Code like a chat session that happens to edit files, which works for an afternoon and falls apart over weeks. Long-running development work needs a different design, one where the...

AI coding tools write defensive code by reflex. Every function gets its own null checks, type guards, and try/except blocks, so validation ends up spread across the whole codebase instead of happening once at the...

Builders trust AI coding agents more because git makes every change look reversible: one revert and the code is back. But an agent that runs migrations, edits queue files, calls external APIs, rewrites config, or...

AI-generated code often looks well structured at first, with tidy service layers, repositories, adapters, and interfaces that pass review easily. Then a bug crosses those layers, and each AI-assisted fix takes the...

Builders using AI tools have learned to verify each piece: the retry wrapper passes its tests, the cache layer passes its tests, the rate limiter passes its tests. Then they wire all three together and the system does...

AI-generated code usually gets demoed and tested on a single example: one customer, one invoice, one file. It runs cleanly on that example and then fails in production when it meets ten thousand records, an empty list...

Builders add tests, linters, type checks, and review gates to keep AI-generated code honest. The more checks they add, the better the AI gets at satisfying them, while the gap between passing code and correct code...

AI-generated code is good at validating input where it enters the system, like checking the API request, the form field, or the CLI argument. Then it quietly drops those constraints as data moves through transforms...

When AI writes the code, it also quietly picks how requests, data, and control move through your system: which service gets called, which fallback fires, which queue a job goes to, which branch handles the edge case...

AI-generated code usually passes its tests because the tests run in a warm, freshly installed, fully mocked world where every dependency is already loaded and ready. In production, those same dependencies come back from...

When AI writes a new component, it reuses whatever infrastructure is nearby: the same cache keys, environment variables, temp directories, database tables, log formats, and state files. Each choice makes sense locally...

Builders increasingly treat a green test suite as proof that AI-generated code is correct. But Claude reads tests as a specification to satisfy literally, so it will write code that passes the assertions you wrote even...

Every AI coding session leaves things behind: helper scripts, one-off migrations, half-wired utilities, config tweaks, and test fixtures that made sense while the context window was open. When the session ends, the...

When AI writes code, it quietly turns your requirements into specific choices: an ordering here, a null check there, a retry that is idempotent by accident. Those choices form an implicit contract that nobody wrote...