I explore AI testing, agent tooling, and infrastructure by forking repositories, running them in real environments, and documenting what breaks. My method: small PRs, visible assumptions, and commit-level proof instead of summary promises.

Recent focus areas:

• AI-driven test automation — investigating LLM-based test generation, bug reproduction workflows, and CI/CD integration patterns across platforms like WHartTest, AITestPlatform, and qaitest.
• Agent-native software — studying CLI-Anything, MCP server implementations, and composable tooling that makes existing software controllable by agents without bespoke integrations.
• Infrastructure reality checks — validating Docker-based deployments, proxy configurations, and cross-platform compatibility issues on Linux and macOS environments.

Here's the thing about being a fork collector: the interesting stuff hides in the annotations. These are the ones worth opening.

• Keep it small — PRs should be reviewable without context loss. One clear change beats ten unrelated edits.
• Document the why, not just the what — assumptions decay faster than code. If a decision isn't obvious six months later, it wasn't captured properly.
• Study forks like study notes — I annotate, run, break, and rebuild. Passive reading doesn't surface the hidden dependencies.
• Tools must be composable — no monoliths, no magic globals. If it can't pipe input or expose a stable interface, it's technical debt.
• Prefer clarity over cleverness — if it can't be explained in a paragraph, it's too complex for production.

Languages: Python · JavaScript · TypeScript · Shell

Platforms: Linux · macOS · Docker · Node.js · Git

Testing focus: Playwright · MCP servers · LLM-driven test generation · CI/CD pipelines

• GitHub: @Fargolee · Building in public since 2018

_{This profile is a workspace, not a showcase. If a fork looks abandoned, it's probably annotated locally.}