Suggestion: WFGY as a robustness / debugging tool for production LLM & RAG

Hi, and thanks for curating this excellent list on production machine learning. I would like to propose WFGY as a resource in the “Monitoring / Debugging / Robustness” area, specifically for LLM- and RAG-based systems. - Project: WFGY (All Principles Return to One) - Repo: https://github.com/onestardao/WFGY - License: MIT - GitHub: ~1.4k+ stars WFGY is a text-only semantic reasoning engine that you plug into an existing LLM stack. It is used as a “semantic firewall” before engineers start changing infra. Key output: **WFGY 2.0 ProblemMap (16 failure modes)** https://github.com/onestardao/WFGY/tree/main/ProblemMap - Encodes common production failures like hallucination with entropy collapse, vector store index skew, chunk drift, multi-agent chaos memory, bootstrap ordering bugs, deployment deadlock, pre-deploy collapse, etc. - Each page is written in a very operational style: symptoms, how to reproduce, and mitigation strategies. Supporting materials: - **WFGY 1.0 technical PDF** with the underlying math and experiments. - **WFGY 3.0 Singularity Demo** with 131 high-difficulty problems for probing long-horizon behavior. This might be useful for practitioners who already use the libraries in your list and now face real production issues with LLM-based applications. If you think it fits, I would be happy to add a short entry via PR.