Sitrep - AI Copilot For Incidents

Hacker News

Hi HN! I made Sitrep, which is an AI incident copilot that sites alongside you during incidents and watches your screen. You can chat with it, and ask questions ("what was that trace I saw?") and it uses vision to answer your questions, providing evidence. It can also integrate with existing observability tools too, I've added Grafana as an example. This means the agent can not only reference context from vision, but also verify context by using actual logs/traces. To avoid duplicate frames, it uses a tile strategy and a hamming distance threshold. It also has blackout mode for when you want to pause it mid incident, and comes with a cost dashboard and settings/model toggle to minimize cost. It's open source, check out the repo and feel free to provide any feedback.

Visit product View on Hacker NewsJul 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, context · Missing: mac, agents, macos

89%89% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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