# I am the Watcher. I am your guide through this vast new twtiverse.
# 
# Usage:
#     https://watcher.sour.is/api/plain/users              View list of users and latest twt date.
#     https://watcher.sour.is/api/plain/twt                View all twts.
#     https://watcher.sour.is/api/plain/mentions?uri=:uri  View all mentions for uri.
#     https://watcher.sour.is/api/plain/conv/:hash         View all twts for a conversation subject.
# 
# Options:
#     uri     Filter to show a specific users twts.
#     offset  Start index for quey.
#     limit   Count of items to return (going back in time).
# 
# twt range = 1 9
# self = https://watcher.sour.is/conv/edyzbcq
Using time series as alert thresholds – Robust Perception | Prometheus Monitoring Experts

This is neat, I _must_ try this out one day to let our dev teams define their own alerting thresholds per service. #sre #alerting #prometheus
Using time series as alert thresholds – Robust Perception | Prometheus Monitoring Experts

This is neat, I _must_ try this out one day to let our dev teams define their own alerting thresholds per service. #sre #alerting #prometheus
cc @deebs
cc @deebs
I loved that one too : https://about.gitlab.com/blog/2019/07/23/anomaly-detection-using-prometheus/
@tkanos Oh that's cool! I'll have a play with this 👌
@tkanos Oh that's cool! I'll have a play with this 👌
@prologic yep very good to play, and to understand some concepts, but I did not find it much useful in my area.
@prologic But after that what you can also do is : a python script that get prometheus metrics, do a bit of machine learning, and then inject the result on prometheus (very fun, but again it's hard to have a sufficient big training set, except if your app is buggy :D)