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9 articles tagged #Production.
P.01Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.02Rolling back a bad API endpoint takes seconds. Rolling back a bad LLM integration is harder — the damage may already be in your logs, your users' inboxes, or your clients' feeds. Feature flags are how you ship AI features without betting everything on launch day.
P.03AI features ship fast. Then the monthly API bill arrives. Here's a systematic approach to understanding and reducing LLM costs without breaking the product.
P.04ALTER TABLE locks your database. Your migration takes longer than expected. Users get errors. Here's how to handle schema changes that don't interrupt production traffic.
P.05Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
P.06EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.07EditorPickWe migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
P.08EditorPickMost AI agents fail in production. Here are the architecture patterns, error handling strategies, and guardrails we use to build agents that actually ship.
P.09EditorPickMulti-agent systems sound great in demos but break in production. Here's how to architect, orchestrate, and monitor AI agent teams that reliably handle complex workflows — patterns from real deployments.