Well-net – a friends-only IPv6 network with no central server

Hacker News

Hi HN, I built well-net: https://github.com/remoon-net/well Think tsnet, but without Tailscale or any central server. It’s for secure friend networks — chat, small games, private services. Existing systems suck in subtle ways: lose your domain → lose your identity and contacts. Delta Chat and Mastodon show the problem. I just want something that works without any central coordination. Each node gets a stable IPv6 in 2001:00ff::/32 from its MAC using EUI-64. Real MACs → no collisions, no central server needed. NAT is only to avoid overlay address conflicts. Tech: WireGuard + WebRTC → runs in browsers. Once WebRTC DataChannel works in Service Workers, private services can be accessed directly from the web. Planned: minimal mail-based chat using Delta Chat with IP-literal addresses like remoon@[2001:ff::1] → DNS-free identity. Project is experimental. Would you use this for small friend networks?

Visit product View on Hacker NewsFeb 25, 2026

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

41%41% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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