I built a tool for mobile build distribution

Hacker News

Hello HN! Recently I needed to distribute mobile builds for Android and iOS with my team as we started working on mobile app. There is a bunch of existing over-the-air distribution solutions but they either have a ton of ads, work slowly or have file size limitations. Expo has a great tool for that, but it only works within their ecosystem. So I’ve built one for myself, trying to make it as simple and easy to use as possible. You upload the build, add tester emails, and send it. It also gives you a link and QR code that you can use. Anon usage requires no registration, has high file size limit (150mb) and has simple API. Build retention is 2 days. I may add larger file limit and retention for users with free account soon. Files are hosted on Cloudflare R2 so it costs basically nothing to run it and I don’t plan to monetise it. Any feedback would be greatly appreciated!

Visit product View on Hacker NewsFeb 6, 2024

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

86%86% 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: user, email, code · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, soon, users · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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