Tunwg – open-source E2E encrypted HTTPS tunnel to local servers

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Hello! To expose servers running on my local network, I created [tunwg]( https://github.com/ntnj/tunwg ). Unlike similar solutions (ngrok, cloudflare tunnels etc.), tunwg doesn't decrypt HTTPS traffic and forwards it directly to your local client based on SNI. It generates persistent subdomains across restarts, and can be used with your own custom domain, without requiring any account. You can self host the server too. The server is stateless and adding new clients doesn't require any server configuration. The default server is bandwidth limited. Tunwg client and server run wireguard interface on gvisor's userspace network stack, so it doesn't require or interfere with your OS wireguard. On startup, client exchanges wireguard public key with server, and outputs a custom subdomain derived from client's wireguard key. The server gets the subdomain from SNI and forwards the TCP stream to the relevant client's gvisor stack.

Visit product View on Hacker NewsJul 31, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, grok · Missing: mac, agents, macos

74%74% 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

68%68% 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 HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, users · Missing: plus, platform, intuitive

39%39% 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

17%17% 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.

Incorrect prediction on native model

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