I

I think I re-imagined IRC on mobile devices

Hacker News

I think I re-imagined IRC on mobile devices

I have hosted my own IRC server, for a small community of coders and friends, since 1999. I never really strayed from using mirc or irssi, when I'm on a desktop or have a keyboard. On my iphone, the options were pretty lacking, though. The best true IRC client for iphone is one called IRC999, in my opinion. These clients all bugged me though because when the phone would go idle or shut off the wifi, the client disconnects. Sometime in early December, an idea hit me about an IRC client that would function sort of like Twitter's mobile client. It'd defer the task of maintaining the IRC connection off to a server and it'd expose an HTTP-based API for the mobile app to use. Anyway, so that's what I've done. I wrote a web server / IRC client that logs chat data to a database. Specifically formatted data is exposed via HTTP so that it can be easily consumed by a mobile client. I used node.js and couchdb for these two components. I also have the basics of a iphone app. The app can be configured to point to any URL / port to use as an API. These components are all open source because I think that's important. Since the server component is just an IRC client, it could be connected to BNCs like ZNC, and should continue to function just the same. Might need some BNC-specific commands, though. I just wanted to show this and see if others thought it was a neat idea. The project is still hacky but I'm actively building it. Screenshots: http://imgur.com/a/yMWVs Client source: https://github.com/ryancole/pound-client Server source: https://github.com/ryancole/pound-gateway

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, open · Missing: mac, agents, macos
68%68% 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
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
61%61% 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 · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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