Wanleung's Workshop

Adding Prometheus metrics and expanding test coverage made pipeline failures visible and gave each reliability fix evidence behind it.

Original Article was published on Medium:...

原文原刊於Medium: https://medium.com/hong-kong-linux-user-group/how-to-type-chinese-cangjie-quick-on-pop-os-24-04-with-the-cosmic-desktop-178627ac335f好多朋友升級到Pop!_OS...

In short: A coding rule from a 1990s university computer science class — where the hard constraint was the size of a CRT screen — turned out to be one of the most practical pieces of advice I’ve ever...

https://www.youtube.com/watch?v=LKHf54jqwvABecause of AI, I could finally complete this song.The Lyrics I had written for some years but I couldn't complete the whole song because lack of melody...

Code: github.com/wanleung/ai-dev-teamIn short: The pipeline’s own AI agents opened a pull request with 7 critical bugs — code that couldn’t run at all. This post dissects exactly why it happened and...

As the system grew, every new pipeline type — bug fixes, documentation, features — needed its own Python script and GitHub Actions workflow. This post covers how all of them were unified into a single watcher process where a GitHub label determines which pipeline runs. Adding a new pipeline now means writing one YAML file.

Standard mode and TDD mode cover most use cases, but sometimes you want a custom sequence — run two review loops in a row, skip deployment tests, or run a domain-specific agent you added yourself. This post covers pipeline.yaml: a separate config file that lets you define any stage sequence with explicit loop blocks, and a drag-and-drop GUI that builds it without hand-editing YAML.

The standard pipeline writes code first, then tests. This post covers a TDD mode that flips the order: QA writes tests before the engineers see the problem, then engineers implement against those tests, then a fix loop runs until the suite is green. It also covers how this forced a proper stage registry — replacing hardcoded stage sequences with a configurable system.

The original pipeline was hardwired to GitHub Models. This post covers how I extracted every LLM backend into its own class with a shared interface, added a relay that automatically falls back to the next backend on connection failure, and what I learned about building resilient AI infrastructure around unreliable upstream APIs.