Docker for Backend Engineers Who Aren't DevOps
Most backend engineers learn Docker by copying a Dockerfile from the internet, running it locally, and moving on. That works until it doesn't — images that are 2GB when they should be 200MB, builds th
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Latest articles
Deploying AI Workflows on Cloud Run: Latency, Cold Starts, and Scaling
The team set min_instances = 3 to eliminate cold starts on the AI workflow service. Reasonable thinking — LLM calls already add 5–15 seconds of latency, a 4-second cold start on the first request afte
Testing AI-Dependent Code: Mocking LLMs, Evaluating Outputs, and Avoiding Flaky Tests
The test checked whether the extracted invoice total matched the expected value. It passed nine times out of ten. On the tenth run, the model formatted the number differently — "1,234.56" instead of "
Redis Patterns Every Backend Engineer Should Know
Redis gets reached for because it's fast. That's often the wrong reason. Speed isn't a pattern — it's a property. Using Redis correctly means understanding what each pattern is actually good at and, j
Prompt Versioning and Cost Control: Running LLMs Responsibly in Production
The 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 woul
Async AI Pipelines in FastAPI: Streaming, Queuing, and Long-Running Requests
The document extraction endpoint was synchronous. A request came in, the service sent the document to OpenAI, waited for the full response, parsed it, wrote to the database, and returned. Each request
GitHub Actions I Set Up on Every Project
Most engineers using GitHub Actions daily know how to write a basic workflow — checkout, install dependencies, run tests, deploy. That covers 80% of what they need. The other 20% is a set of features