Swift Language Resources

Hacker News

I passed 2000 Urls in my collection of Swift Urls. I tried to add tags for many urls to make it easier to search a topic. For example, if you're interested in AppleTV or functional programming: http://www.h4labs.com/dev/ios/swift.html?q=AppleTV&age=10000 http://www.h4labs.com/dev/ios/swift.html?q=functional&age=10000 The age parameter will let you filter by days. Say all blogs written in the last 3 days, for instance: http://www.h4labs.com/dev/ios/swift.html?q=&age=3 Here's the main site: http://www.h4labs.com/dev/ios/swift.html Finally, the raw data is on Github in tsv format: https://github.com/melling/SwiftResources

Visit product View on Hacker NewsOct 7, 2015

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3points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: 000, 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: apple · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: para, ios · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% 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

30%30% 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

21%21% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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