GCP-ops-bot – Ask your GCP infrastructure questions in natural language

Hacker News

Hey HN, We just open-sourced `gcp-ops-bot` — a Python-based, agentic monitoring utility for GCP that lets you interact with your infrastructure using natural language. It combines Google GenAI and native GCP APIs (Monitoring, Logging, Compute, IAM) to answer questions like: - "What VMs are running in us-central1-a?" - "List all custom service accounts in my project" - "Show logs from the last hour for compute instances" It’s meant to be easy to set up, intuitive to use, and useful for devs, SREs, and students learning cloud infra. Would love your feedback, thoughts, and suggestions. Happy to answer questions here! GitHub: https://github.com/Retailogists/gcp-ops-bot

Visit product View on Hacker NewsMay 21, 2025

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

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

53%53% 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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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

16%16% 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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