3 ms·
Using LLMs to classify noisy alerts is a really clever approach to tackling alert fatigue! Are you fine tuning your own model to differentiate between actionabl
by sanj001 2y ago
Using LLMs to classify noisy alerts is a really clever approach to tackling alert fatigue! Are you fine tuning your own model to differentiate between actionable and noisy alerts?
I'm also working on an open source incident management platform called Incidental (https://github.com/incidentalhq/incidental https://github.com/incidentalhq/incidental), slightly orthogonal to what you're doing, and it's great to see others addressing these on-call challenges.
Our tech stacks are quite similar too - I'm also using Python 3, FastAPI!
- jobtemp 2y agoWhy not use statistics? Been reading about xmr charts recently on commoncog. That might help for example.
- aray07 2y agoThanks for the feedback! I saw the incidental launch on HN and have been following your journey!
- Jolter 2y agoI wouldn’t say it’s particularly clever. It’s a fairly obvious idea to anyone who has worked with alerting through IMs. What it is, is /difficult/, because you really really need to avoid false positives. Probably lots of hard work involved. So kudos for making this work (if it works)!
- david1542 2y agoI'm curious about incidental :) how are you going to compete with other, well established IM tools like rootly, incident.io, firehydrant.com?