catarina_eng

3 ms·

1. 1.▲
   Show HN: Aelitium – Git-style verification for LLM outputs(aelitium.com)

   1 points by catarina_eng 8mo ago|0 comments
2. 2.▲
   by catarina_eng 8mo ago

   Fair point — "accountability" implies enforcement mechanisms that aren't here. More accurate: tamper-evident logging. You capture a request/response pair with a hash. Later you can verify the pair hasn't changed. Wh
3. 3.▲
   by catarina_eng 8mo ago

   Same prompt. Same code. Different output weeks later. Was it the model or my code? Built a small tool to answer this: python examples/model_drift_detector.py If the provider changed behavior between runs: STATUS=CHANGED rc=2
4. 4.▲
   Show HN: Detect when an LLM silently changes behavior for the same prompt(github.com)

   1 points by catarina_eng 8mo ago|4 comments
5. 5.▲
   Ask HN: What makes AI agent runtime logs defensible under adversarial audit?

   1 points by catarina_eng 9mo ago|1 comments