Launching today
ARA
Give AI systems a memory of every decision they make
48 followers
Give AI systems a memory of every decision they make
48 followers
AI systems make decisions, then forget everything: the exact inputs, the model version, the context. ARA is an infrastructure layer that records every decision your ML models make with the exact state that produced it, and can replay any of them months later. Incident debugging becomes a query. Drift shows up per entity. Training data joins without label leakage. Free Community Edition, runs entirely on your own infrastructure in under 10 minutes. SDKs on PyPI and Maven Central.
Set this up in our staging env over lunch and the replay query on a flaky batch job pointed straight at the offending model version. Self-hosted install was painless and the PyPI SDK felt like a normal client, not a framework I had to wrap my head around.
Replay is cool, but a built-in diff view comparing two specific model versions side by side on the same input would save so much time during debugging. Right now I'd have to script that comparison myself across snapshots.
the replay-any-decision-months-later thing is genuinely clever, especially pairing it with per-entity drift tracking. most observability tools stop at aggregate metrics so this feels like it was built by people who actually debugged a model at 2am.
The "forgot everything I told it yesterday" pain is real, I work with coding agents across long-running projects and end up maintaining markdown context files by hand just so each session doesn't start from zero. Curious how recall works in practice: does the agent decide what to remember/retrieve on its own via the MCP tools, or do I control what gets stored? The failure mode I'd worry about is it confidently recalling stale info after the project has moved on.