mjbonanno

3 ms·

1. 1.▲
   by mjbonanno 7mo ago

   sniderwebdev, Thank you!
2. 2.▲
   by mjbonanno 7mo ago

   Wanted to follow up on this... I dug into the codebase after your comment. You're right that the data is all there (last_access, access_count, raw Ebbinghaus relevance score, provenance source type) but it's siloed behind secondar
3. 3.▲
   by mjbonanno 7mo ago

   @xing_horizon Thanks! I really appreciate the feedback. You're spot on that downstream agents need clear signals to decide whether to trust, refresh, or ignore a recalled memory. Right now `Activate()` already returns: - Bayesian confi
4. 4.▲
   by mjbonanno 7mo ago

   This is the project I just posted. Happy to dive into any details... the exact ACT-R decay formula, how the Hebbian graph updates in log space, the 6-phase Activate pipeline, or why I went with embedded Pebble. Fire away!
5. 5.▲
   Show HN: MuninnDB – ACT-R decay and Hebbian memory for AI agents(github.com)

   2 points by mjbonanno 7mo ago|7 comments
6. 6.▲
   by mjbonanno 7mo ago

   Go is my Go-to lately :-)
7. 7.▲
   by mjbonanno 7mo ago

   This is cool. I am playing around with Bubble Tea in Go today.
8. 8.▲
   by mjbonanno 7mo ago

   Oof, $82k in 48 hours is brutal. Makes me even more glad I run everything local where possible.
9. 9.▲
   by mjbonanno 7mo ago

   The privacy angle here is fascinating. Curious if anyone has tried running the on-device model locally yet?
10. 10.▲
    by mjbonanno 7mo ago

    This is awesome! Exactly the kind of low-latency agent tooling I've been looking for. How are you handling long-term memory/context between calls?