H-

H-CLI – Manage network infrastructure with natural language

Hacker News

H-CLI – Manage network infrastructure with natural language

Network engineer here. I've been building my own parallel SSH tooling (h-ssh) for years, multi-vendor (Junos, Arista, IOS, NXOS), parallel telnet, parallel REST API calls. It's been my daily driver in production. A few months ago I gave it an AI brain. h-cli lets you manage infrastructure by sending plain English messages in Telegram. Claude Code by default, also works with self-hosted models through the Claude Code framework via API calls to vLLM/Ollama. What it can do: - "Discover the CLOS fabric and document everything in NetBox with cable detail" — 12 routers, full cabling, 4 minutes (GIF on the repo) - Parallel REST calls across APIs in a single job — correlated results in seconds ; copied from h-ssh - EVE-NG lab automation — natural language to full lab deployment, bootstrap, and verification - Grafana dashboard rendering straight into Telegram - Teachable skills — demonstrate a workflow, it learns it - Chunk-based conversation memory (24h) + Qdrant vector memory for your own datasets (I used EVPN docs, worked perfectly for creating templates and troubleshooting) - Redis-based horizontal scaling, designed with future plans to run multiple instances against a shared vLLM backend Safety: a separate stateless LLM (Haiku, also adjustable for local LLMs) gates every command with zero conversation context — can't be social-engineered. Pattern denylist, two isolated Docker networks, non-root, cap_drop ALL, HMAC-signed results. 44 hardening items total. Self-hosted, Docker Compose, 9 containers, MIT licensed. The interesting part might be how it was built: one operator coordinating 8 parallel AI agent teams, zero human developers. The development methodology doc covers the full process, architecture, coordination via git + Redis, conflict resolution between agents. Of course i reviewed the code changes, hence the commit discipline. https://github.com/h-network/h-cli

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, apple
97%97% 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 · Strong signals: para, ios, vpn · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, month, para · Missing: mobile apps, personal, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, calls · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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