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Tunn – Manage all your SSH tunnels from a YAML file
Hacker NewsTunn – Manage all your SSH tunnels from a YAML file
I was tired of manually setting up SSH tunnels every time I needed to debug a service or proxy a database. So I built tunn — a lean Go tool that feels like docker-compose, but for SSH tunnels. You define your services in a YAML file and run all your tunnels concurrently with one command. • Written in Go (only depends on yaml) • Simple log output, clean CLI • Supports grouping services and running selectively GitHub: https://github.com/strandnerd/tunn I’d love feedback, feature requests, and ideas for improving DX!
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created67%67% 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: dock · Missing: mac, agents, macos47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive32%32% 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, revenue16%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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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