Show HN - 9bro.com (meme comic creator)

Hacker News

This is my first programming endeavor - a simple meme comic creator inspired by my addiction to a friend's wildly successful site - 9gag.com. More than anything else, as my first project, I learned that I have a LOT more programming to learn. And also, that programming is freaking awesome regardless of my current ineptitude. I built the site using JS/jQuery/HTML/CSS and a dash of PHP (for base64 string to file conversions) and the Bing Image API for image searches (not currently working because Microsoft moved to pay model - boo!) all running on micro Linux instance on AWS. For a few month learning all that stuff from scratch, I'm pretty proud of myself. For my next project, I've been playing with Node.js and MongoDB to try to sort out server-side magic. Anyway, Hacker News has been pretty inspiring for me - I started reading it about 10 months ago just out of curiosity and understood about 1% of what everyone was talking about. Now I understand...10%? That's progress at least, right? :)

Visit product View on Hacker NewsAug 15, 2012

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Actual performance

7points

Made the leaderboard

Launch Intel predictions

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

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

71%71% 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: model, new, using · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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

22%22% 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.

Correct prediction on native model

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