Open-source AI assistant for Kubernetes troubleshooting

Hacker News

I built this because I was tired of context-switching between clusters and grep-ing through logs. It's a browser-based tool that uses LLMs to diagnose Kubernetes issues. Key technical bits: - Fast cross-cluster search with flexible filtering - LLM-powered root cause analysis for pod failures - Proactive cluster risk scanning with AI-driven fix suggestions - AI translation of complex YAML configurations into plain English Website: https://karpor.kusionstack.io Product Hunt: https://www.producthunt.com/posts/karpor

Visit product View on Hacker NewsJan 15, 2025

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1points

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

77%77% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% 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: 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.

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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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