StrainSignal helps teams spot early burnout signals in Slack by detecting recovery erosion, after-hours creep, weekend spillover, and responsiveness drift. Unlike surveys or sentiment analysis tools, it uses behavioral metadata, compares each person only to their own baseline, and alerts only on sustained patterns. No message text interpretation. No rankings. Built for teams that want earlier signal without crossing into surveillance.

Sydekiq — Your own AI agent, deployed on a private server, 24/7

Your own AI agent, deployed on a private server, 24/7

Hi Product Hunt — I’m the solo developer behind StrainSignal. I built this because most burnout tools I came across seemed to fall into two buckets: surveys and sentiment analysis. That never felt like the right starting point to me. What I kept coming back to was a simpler question: what if the earliest useful signal isn’t self-report or message meaning, but sustained work-rhythm drift? That led me to build StrainSignal around patterns like shrinking recovery windows, after-hours creep, weekend spillover, and changes in responsiveness in Slack. A few boundaries mattered a lot while building it: no cross-person ranking no sentiment analysis no storing or interpreting message text alerts only on sustained patterns, not one weird day The goal was to make something that feels useful and explainable without crossing into surveillance. This is live and usable, but still early, and I’d genuinely love feedback — especially on the positioning, the trust model, and what feels clear vs unclear when you first land on it. Happy to answer anything.

Sydekiq — Your own AI agent, deployed on a private server, 24/7

Your own AI agent, deployed on a private server, 24/7

StrainSignal

Early burnout signals for Slack-native teams

Early burnout signals for Slack-native teams