MLM Memory Engine shifts to scoped Ollama runtime

The project reads less like a miracle memory engine and more like a smart wrapper around aggressive Ollama tuning. That makes it genuinely useful for local-LLM tinkerers, but the value is in isolation and reproducibility, not the headline-grabbing 10x claim.

• –Moving from global shell edits to a private loopback server is the smartest change; it makes experimentation much less risky.
• –Cross-platform support plus generated artifacts like `analysis.json`, `manifest.json`, and Modelfiles should make runs easier to debug and reproduce.
• –The README is more grounded than the tagline: even the project frames fit as heuristic and dependent on model quantization, prompt length, adapters, and Ollama behavior.
• –The original Reddit post is still at zero comments, which fits an early-stage builder project more than a broadly adopted tool.
• –A later reply on the project is already asking the right question: how does relevance get judged beyond plain similarity?