roampal

5 ms·

1.
1% vs. 67%: What happened when we stopped trusting embeddings alone(roampal.ai)
16 points by roampal 8mo ago|7 comments
2.
by roampal 9mo ago
You're right, it is a form of tagging technically. The difference is you're already saying "thanks that worked" or "nah that's wrong" anyway. No extra step, it just listens.
3.
by roampal 9mo ago
Ran a 4-way comparison test across 200 query-memory pairs: - Baseline RAG (embedding similarity only): 10% - RAG + reranker: 20% - Outcomes only (no reranker): 60% - RAG + outcome scoring (mature memories with 20+ uses): 60% "Accuracy&
4.
by roampal 9mo ago
Fair point, the install instructions at the end were meant as a "here's how to try it if interested" but I can see how it reads as pushy. The core of the post is about the outcome scoring approach itself. Should've led w
5.
RAG accuracy jumped from 10% to 60% when I added outcome scoring(roampal.ai)
11 points by roampal 9mo ago|7 comments
6.
Show HN: I made Claude Code learn from its mistakes(github.com)
4 points by roampal 9mo ago|0 comments
7.
by roampal 10mo ago
Absolutely, go for it! Run whatever benchmarks you have. I would love to see how it stacks up against RL methods. Ping me if you need help with anything. Thanks!
8.
by roampal 10mo ago
Dude, you literally wrote the exact motivation paragraph for Roampal right around the same time I posted this Thorndike's Law of Effect is the entire reason I built the outcome-scoring (+0.2 for worked, −0.3 for failed) and shift weigh
9.
Show HN: Roampal – a local memory layer that learns from outcomes(github.com)
1 points by roampal 10mo ago|4 comments