Show HN: Roampal – a local memory layer that learns from outcomes

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 https://github.com/roampal-ai/roampal Website + demo video: https://roampal.ai https://roampal.ai Happy to answer technical questions or take brutal feedback in the comments.