PYTHON / TEST INFRASTRUCTURE / AI ENGINEERING
From test infrastructure to verifiable AI engineering.
Notes on Python, test infrastructure, and AI engineering. Here I document the systems I have worked on, engineering decisions, and ideas still being tested.
Project delivery and ongoing study are clearly distinguished.
01 / WORK
Three connected efforts: domain test infrastructure, reusable execution tools, and AI-assisted delivery.
Share request behavior across business APIs while handling protocol changes, existing callers, and retry conditions.
Contributed to APIs and led the merchant-funds SDK refactor and compatibility work.
Follow discovery and report validation through concurrent failures, deadlines, and missing results.
Defined scope and execution behavior; collaborated with AI on implementation and code review.
Connect staged LLM generation, human review, and execution checks to turn generated code into useful test cases.
Build a staged LLM test-generation pipeline at work, combining AI-agent delivery with human review gates.
02 / WRITING
Implementation details, trade-offs, failure modes, and verification.
工具可以随时换，正在使用工具的插件怎么办？从一个视觉服务的例子，讲清 Cordis 的依赖、清理、共享和并发，以及 Python、Go 能否实现同样的机制。
Chinese original用一个可运行的 Python 工程，逐步建立需求识别、规则审阅、测试剧本生成、骨架填充和结果验证流程，附完整 Prompt、Skill 和公司接入方法。
Chinese originalUse multi-business reuse and existing subclass compatibility to explain request layering, calling contracts, and the limits of return-code retries.
Compare method and file subprocesses through shared fixtures, then follow discovery isolation, checkpoints, and parent-driven termination.
Interested in the reasoning behind the projects?