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appspydr.com - xcode/iPhone programming resource search engine

Hacker News

appspydr.com - xcode/iPhone programming resource search engine

http://www.appspydr.com There are tons of tutorials out there for xcode. Starting out as a self taught iOS developer can be a scary place. A place I'd been in a year ago. A big problem is that we don't have a solid way to go through all the vast libraries of resources. Google, in all its greatness, has its faults as it crawls the entire internet for relevant content, and isn't always the best at displaying them. Appspydr is a search engine that indexes handpicked sites and sources that contain quality tutorials and resources. Users can also request sites/sources to be indexed into Appspydr. I hacked it together fairly quickly (over the weekend) to demonstrate it's purpose and to gauge interests. Everything from suggestions, pledge of interest, ideas, hate mail, critiques, to anything else you guys can think of is appreciated. Thanks! Ash

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Actual performance

7points
3comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, user · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
44%44% 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
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
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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