My first Windows 8 app, GabberWocky (a gtalk client)

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I've been working on this for a few months since I got my Surface RT, and realized there wasn't a solid gtalk/gchat app. It's a C#/XAML app, and I ended up writing my own XMPP stack on-top of raw sockets since there wasn't a good open source one available (that would run in RT). I'll open source my XMPP library on GitHub shortly once I've cleaned it up a bit. Check it out! Store link: http://apps.microsoft.com/windows/en-US/app/gabberwocky/2d7a19c5-2522-461c-8706-99ff59bd84fe Facebook: http://www.facebook.com/gabberwockyapp

Visit product View on Hacker NewsFeb 14, 2013

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Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released

80%80% 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: apps, open · Missing: mac, agents, macos

71%71% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

21%21% 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

16%16% 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

1%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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