Matthew McConaughey was on Joe Rogan two months ago describing the exact AI he wanted: a private model trained only on his own writings and experiences. I built it — and added outcome-based learning.

On 130 adversarial scenarios designed so the query semantically matches bad advice better than good advice:

→ plain vector search: 0–3% correct

→ Roampal: 100% correct

Efficiency: 63% fewer tokens — retrieves 1 outcome-verified result vs RAG's top-3 semantic matches.

Core mechanism

• AI marks outcome → success +0.2, failure −0.3 (explicit or auto-detected from conversation)

• New memories: 70 % embedding / 30 % outcome score

  Proven memories (5+ uses): 40 % embedding / 60 % outcome score

• Over time, “sounds right” gets demoted, “actually worked” gets promoted

Key difference from Mem0/Zep

They update on relevance/consistency. Roampal updates on real outcomes.

Reproducible results (JSON in repo):

                Plain Vector   Roampal

Finance (100) 0 % 100 %

Coding (30) 3.3 % 100 % ← p=0.001, Cohen’s d=7.49

Learning curve: 58 % → 93 % accuracy as memories accumulate (p=0.005, d=13.4)

I’m not a programmer — psychology degree, MBA, day job managing $6.5 M contracts. Nine months of nights & weekends with only Cursor, Claude, and copy-paste.

100 % local · runs offline with Ollama, LM Studio, or Claude Desktop · MIT license · no telemetry · no signup

GitHub (full benchmarks + all 130 adversarial scenarios):

https://github.com/roampal-ai/roampal

Website + demo video:

https://roampal.ai

Happy to answer technical questions or take brutal feedback in the comments.