Wondering which SDKs are in iOS and Android apps & how they were made?

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I started SourceDNA because I wanted to build a highly scalable, cross-platform binary similarity engine. We can dump in libraries and apps from all over and discern patterns in their code. We've been scanning thousands of mobile apps and finding what's inside, and we wanted to make this data available now for others to explore. Clickable link: http://sourcedna.com/stats/ This interface lets you see which SDKs (ads, analytics, optimization, etc.) or cross-platform tools (Unity, Adobe AIR, Xamarin, etc.) were used to create the top 500 free apps on both iTunes and Google Play app stores. You can select an individual SDK vendor and the apps containing their code will be listed at the bottom. You can also click on an individual app to see what's inside it. I'd love to hear how you'd use something like this and if you have suggestions on how to improve it. If you're interested in the technical details of how we managed to do all this, I'm happy to talk about them here. Nate Lawson, Founder

Visit product View on Hacker NewsDec 20, 2013

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

92%92% 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, code · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: mobile apps, ios, apps · Missing: personal, entrepreneurs, video

72%72% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

42%42% 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

23%23% 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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