I

I made Devzat – It's like Discord but in the terminal, over SSH

Hacker News

I made Devzat – It's like Discord but in the terminal, over SSH

Run `ssh devzat.hackclub.com` to try it out! The repo is here: https://github.com/quackduck/devzat (golang). It has markdown and emoji support, DMs, channels, and it can show images too. You can send code, and it gets syntax highlighted (you can change the theme). You can ping people like so: @user and it sends them a \a, which should play an audible sound if the terminal allows it. There's inbuilt games and rainbow names and a lot of other small things I don't remember right now. You might find the auth system interesting: it's based on a hash of ssh pubkey (bans use that and a hash of IP, so it isn't so easy to get around a ban) Also an interesting issue: bots that go around trying to brute force ssh into random IPs with common usernames. My current solution is banning if rapid successive joins are detected.

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% 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, code · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% 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
13%13% 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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