Public SSH Jump and Port Forwarding Server

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I've made a neat service to access SSH over NAT. Unlike ngrok and alternatives with registration, custom software, non-preserved random URLs and ports, this solution is just a remote SSH server which does not require anything more than an SSH client to both host and connect. Technically it allows to publish any service over SSH (web, proxy, any TCP port really), but only with SSH as a destination you'll preserve end-to-end encryption and won't allow my server to snoop on potentially unencrypted traffic. The service is mostly intended to give remote access to the device as fast as possible, given it already has SSH client and server. To begin, just execute: ssh ssh-j.com Would be grateful for any feedback. ----- Looking for ways to expose SSH behind NAT? Search no further! Publish SSH server port of your laptop, which is behind NAT: ssh any-username@ssh-j.com -N -R laptop-behind-nat:22:localhost:22 ╭─╯ unique ╰─╮ │ device name ╭───╯ │destination│ │ namespace │ │ and port │ │address and│ │for your hosts│ ╰──────────────╯ │ port │ ╰──────────────╯ ╰───────────╯ Connect to the published SSH: ssh -J any-username@ssh-j.com laptop-behind-nat ↖ It's THAT easy! Published hosts are bound to the username. Other usernames can't access them. Pick a unique username (not root ) and get access to your devices anywhere!

Visit product View on Hacker NewsMar 18, 2021

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, 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

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

nativeThis product was originally launched on this platform.

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

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

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

42%42% 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

37%37% 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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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