Async AI Pipelines in FastAPI: Streaming, Queuing, and Long-Running RequestsSep 27, 2026·5 min read·5
Prompt Versioning and Cost Control: Running LLMs Responsibly in ProductionThe monthly bill was ten times the estimate. Investigation took two hours. Three things were wrong. A retry loop was calling the model on every validation error — including schema mismatches that woulSep 29, 2026·6 min read·30
Provisioning GCP Services with Terraform: Cloud Run, Pub/Sub, and Cloud SQLS01E04 of Terraform for Application EngineersSep 13, 2026·6 min read·7
Managing State, Secrets, and Environments in TerraformS01E03 of Terraform for Application EngineersSep 11, 2026·6 min read·13
Structuring OpenAI and Gemini Calls in FastAPI: Prompts, Fallbacks, and RetriesThe fallback was supposed to be temporary. When the OpenAI API was unavailable, the service would return the last cached result for that user rather than failing the request. Clean degraded experienceSep 8, 2026·7 min read·12
PostgreSQL Connection Pooling on Cloud Run: The Problem Nobody Warns You AboutCloud Run scales horizontally. That's the point. Under load, it spins up new instances automatically — each one handling concurrent requests, each one maintaining its own connection pool to your CloudSep 1, 2026·6 min read·43
Building AI-Powered Backend Services: Why Most Integrations Break in ProductionThe integration looked solid. In testing, the AI-powered document extraction service worked exactly as designed — pull a PDF, send it to the model, parse the structured output, write to the database. Aug 22, 2026·4 min read·27
How I Structure a FastAPI Project for a Team of 5The first FastAPI project I led for a team had everything in two files. main.py with the routes. models.py with the Pydantic schemas. It worked fine until a second engineer joined and we started steppAug 18, 2026·6 min read·17
