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vNetMap – A zero-knowledge E2E encrypted network topology mapper

Hacker News

vNetMap – A zero-knowledge E2E encrypted network topology mapper

I built this because I was tired of manually maintaining Excel sheets and Visio diagrams for my homelab documentation. The core idea is automated discovery via Nmap, but without sending plaintext network data to a hosted backend. It uses strict client-side Zero-Knowledge End-to-End Encryption. The Architecture: The Scanner: A lightweight Python agent running locally in a Docker container. It uses Nmap to scan the local subnet. The Encryption: The payload is encrypted locally before it ever leaves the network. The backend (FastAPI/PostgreSQL) only receives and stores encrypted blobs. The Frontend: Decryption happens entirely client-side in the browser (Angular), rendering the topology using vis-network. Technical Hurdles & Limitations I'm currently facing: Hostname Discovery is a nightmare: Across different vendors, devices handle broadcast differently (mDNS, NetBIOS, pure DNS). The agent catches what it can, but often falls back to raw IPs because reliable local resolution is incredibly inconsistent. Physical Connections: While Nmap easily discovers the devices and open ports on the subnet, mapping the exact physical connections (which switch port goes where, device types, Wi-Fi details) still requires manual linking in the UI. Links: Live App: https://app.vnetmap.com GitHub (Docs & Agent Setup): https://github.com/vNetMap/vnetmap-issues I'm curious to hear how you handle automated discovery in your homelabs, especially regarding the hostname resolution issue, or if you spot any flaws in this E2E approach.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, dock, physical · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
42%42% 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: ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, scanner · Missing: mobile apps, personal, entrepreneurs
32%32% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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