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Doctor Droid – AI Slack Bot for Kubernetes Troubleshooting

Hacker News

Doctor Droid – AI Slack Bot for Kubernetes Troubleshooting

Hey HN, we’re excited to share Doctor Droid, an AI-powered Slack bot for faster Kubernetes troubleshooting. After two years of iterating, we’ve built a solution that lets you check pod statuses, restart pods, run custom commands, or diagnose complex issues — all from Slack. Why we built it: We noticed SREs often spend a lot of time jumping between terminals, dashboards, and docs to diagnose K8s issues. Our goal is to bring debugging directly into your team’s workflow (i.e., Slack), guided by an AI assistant. How it works: Sign up Invite the bot to your Slack channel Connect your Kubernetes cluster Start chatting (e.g., Doctor droid restart unhealthy pods in cluster x) Key features: AI-driven assistance for diagnosing and troubleshooting Simple UI within Slack to check logs, restart pods, etc. Proxy option for clusters not publicly accessible (GitHub repo) What’s next: Expanding beyond K8s to 40+ cloud-native integrations (private beta live) Leveraging existing SOPs, docs, tribal knowledge (upcoming feature) Automating postmortems, documentation updates, and scripts Free Credits: Feedback is super valuable to us—please let us know what you think! Grab 500 free credits (worth $50) if you sign up before January 31. Docs & more: Docs: docs.drdroid.io ProductHunt: https://producthunt.com/ Who we’re looking for: Anyone dealing with K8s day-to-day—your feedback will shape our roadmap! Thanks for checking it out. We’d love to hear your thoughts, critiques, or questions in the comments!

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack · 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, exist, lua · Missing: https docs, just released, open source
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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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