SX

SX – Transfer files from within SSH sessions without reconnecting

Hacker News

SX – Transfer files from within SSH sessions without reconnecting

I got tired of the constant workflow friction when transferring files over SSH. You know the drill: SSH into a server, find the file you need (often with grep/rg), then either memorize the path or copy it, open another terminal, retype `scp user@server:/long/path/file.log .`, re-authenticate, and finally get your file. This became especially annoying during log analysis sessions where I'd discover interesting files with `rg "OutOfMemory" .log` and then have to context-switch just to download them. *What SX does:* SX lets you transfer files directly from within your existing SSH session using reverse tunnels. No re-authentication, no retyping connection details, no context switching. I know VSCode can remotely open files with `code filename`, but this works for larger files, binaries, and when you actually need the file locally (not just to view/edit). *How it works:* - Start SX server locally: `sx-server --dir ~/Downloads` - SSH with reverse tunnel: `ssh -R 53690:localhost:53690 user@server` - Use three commands: `sxd` (download), `sxu` (upload), `sxls` (list local files) *Technical details:* Built in .NET 9 with a simple JSON protocol over TCP through SSH reverse tunnels. The client (on remote server) talks to your local server through the existing SSH connection. No additional ports, authentication, or network setup needed. I built this primarily for my own workflow pain, but figured others might find it useful. It's cross-platform (Windows/Linux/macOS) and includes tab completion for remote files. The code is straightforward – happy to discuss the implementation or get feedback on the approach.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor
76%76% 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 · Missing: supports, reddit linkedin, podcasting
64%64% 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, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
35%35% 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
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.

Incorrect prediction on native model

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