Ts-SSH – SSH over Tailscale without running the daemon

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ts-ssh solves a specific problem: accessing machines on your Tailnet from environments where you can't install the full Tailscale daemon (like CI/CD runners or restricted systems). It uses Tailscale's tsnet library to establish userspace connectivity, then provides a standard SSH experience. Works with existing workflows since it supports normal SSH features like ProxyCommand, key auth, and terminal handling. Some features that proved useful: • Parallel command execution across multiple hosts • Built-in tmux session management for multi-host work • SCP-style file transfers • Works on Linux/macOS/Windows (AMD64 and ARM64) The codebase is interesting from a development perspective - it was written almost entirely using AI tools (mainly Claude Code, with some OpenAI and Jules). Not as an experiment, but because it actually worked well for this kind of systems programming. Happy to discuss the workflow if anyone's curious about that aspect. Source and binaries are on GitHub. Would appreciate feedback from anyone dealing with similar connectivity challenges.

Visit product View on Hacker NewsJun 20, 2025

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

91%91% 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.

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

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

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

50%50% 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 · Strong signals: host, users · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

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

Correct prediction on native model

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