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Incident management for Slack with AI-generated postmortems

Hacker News

Incident management for Slack with AI-generated postmortems

I've been an on-call engineer at startups where "incident management" meant someone panic-creating a Slack channel called #fire-fire-fire, or all incidents handled in the #incidents channel at the same time... and the postmortem was whatever someone remembered to write in Notion three days later. So I built a Slack bot to fix my own workflow. Figured I'd share it. What it does: "/incident start sev2 API latency spike" creates a dedicated channel, invites whoever's on-call, pins the details, and starts recording a timeline. When you run "/incident resolve", it uses GPT-4 to analyze the entire channel conversation and generate a postmortem draft: summary, root cause, event timeline, action items. The key insight: the actual diagnosis usually happens in casual messages ("wait, I think the connection pool is exhausted") not in formal status updates. So the AI reads everything, not just what was labeled important. Stack: - TypeScript + Slack Bolt - Prisma + Postgres - OpenAI API for postmortem generation - PagerDuty integration for escalations Other stuff it handles: - Update severity of an incident with "/incident severity <sev1|sev2|sev3|sev4>" - On-call scheduling with automatic weekly/daily rotation - Paging with escalation chains (Slack DMs → PagerDuty if configured) - Jira ticket creation for incidents within slack with "/incident ticket <title>" - Basic analytics (incidents per on-call, MTTR) What I learned building this: 1. Slack's API is actually pretty good now. The Bolt framework handles most of the OAuth/event subscription pain. 2. Getting AI to write useful postmortems required being very explicit about event types. Without context about what's a "status update" vs a "debug message," it would hallucinate causes. 3. On-call scheduling is surprisingly complex. Timezone handling, rotation boundaries, handoff notifications, each is a rabbit hole. Honest limitations: - Only works for teams already living in Slack - AI postmortems need human review, it can miss context from calls/video chats - Only a couple of integrations (the ones I use, but can add more, like Linear, github issues, etc...) Code isn't open source (yet?), but happy to answer architecture questions. Been running this with my own team for approx. 2 months. Landing page: https://incidentops.io Would appreciate feedback, especially from SREs who've built similar internal tools. What am I missing?

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, context, openai · Missing: mac, agents, macos
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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