P.01Most technical proposals lose because they describe what will be built, not why the client should trust you to build it. Here's how to write one that actually wins.
P.02A few hours of optimization can cut CI time 40-60% and your GitHub Actions bill by a similar margin. Here's exactly how, with the configs that do it.
P.03Unit tests confirm your code runs. They don't confirm your AI feature gives good answers. Here's how to build an eval pipeline that catches real failures.
P.04HashiCorp's 2023 BSL relicensing split Terraform into OpenTofu, while Pulumi took a code-first approach. Here's how to choose between them in 2026.
P.05Valkey is the BSD-licensed Redis fork backed by AWS, Google, and the Linux Foundation. Here's how it diverged from Redis and when to migrate.
P.06AI IDE rules files inject project-specific context into every completion. Here is how to write rules for Cursor, Windsurf, and Copilot that change generated code.
P.07LLM calls are slow and expensive, so caching is the obvious fix. Here's when it backfires and how to implement exact-match and semantic caching.
P.08Feature flags let you ship AI features to a small slice of users first, watch for quality problems, and kill the feature in under a minute if something breaks.
P.09HTTP/3 is in production at every major CDN and supported by all modern browsers. Whether it actually helps your application depends on factors most guides don't explain.
P.10Getting valid, schema-conforming JSON from an LLM takes more than a JSON mode flag. Here is what works in production, from native APIs to instructor.
P.11Most AI project failures start at scoping: nobody defines what 'AI integration' means before a price is quoted. Here's how to scope AI projects properly.
P.12Every AI budget starts with API costs and ends in surprises. What production AI features really cost once evaluation, observability and prompt rot are counted.