Posts Tagged "production-ai"

Exponential backoff, dual timeouts, SSE heartbeats, idempotency caches — the unglamorous patterns that keep LLM-powered systems running at 3am.

Real browser, predefined journeys, specialist agents checking every step. How QA findings feed back into the issue tracker to close the development loop.

The AI picks up a planned issue, implements it, tests it, commits, and marks it done. How headless batch mode lets me plan in the afternoon and wake up with code in the morning.

Users ask similar questions in different words. Semantic caching with pgvector turns repeated intent into instant answers — no LLM call, no embedding, no retrieval pipeline.

Context windows fill up fast in long AI conversations. Sliding windows, progressive compression, and token budgeting — the patterns I built before I knew their names.

Vague instructions produce vague implementations. How I sit with the AI to refine rough ideas into concrete, implementable specs — before any code gets written.

The bottleneck isn't the AI — it's me. Three slash commands that form a loop: Plan, Work, QA. How structured phases replaced ad-hoc chatting and made AI development manageable.