Kubently – Debug Kubernetes Clusters Agentically

Hacker News

Kubently is an open-source tool for troubleshooting Kubernetes agentically - debug clusters through natural conversation with any major LLM. Why I built it: kubectl output is verbose, debugging is manual, and managing multiple clusters means constant context-switching. Agents debug faster than I can half the time, so I built something around that. ~50ms command delivery via SSE Read-only operations by default Native A2A protocol - integrates with systems like CAIPE, LangGraph/LangChain Runs on any K8s cluster (EKS, GKE, AKS, bare metal) Multi-cluster from day one Docs: https://kubently.io GitHub: https://github.com/kubently/kubently

Visit product View on Hacker NewsNov 25, 2025

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

88%88% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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