Thrive – a productivity app built by a junior dev and featured by Apple

Hacker News

Hey HN, I wanted to share a side project I've been working on for the past couple of months, how it got featured on the App Store in the US and 12 other countries and got the #1 product of the day badge on PH even though it's not very technically impressive and it's just a side project, so I couldn't invest to promote it. I wrote a case study for my portfolio (I'm a designer) here: https://meet-cristian.com/projects/thrive.html But I'll also share the key lessons: - First, I think applying all the things I learned as a designer over the years made the biggest difference. The app doesn't only explain everything clearly but all the supporting materials (website, promo video, app store page) try to pitch it in a way that will resonate with people looking to improve their lives, and I guess this worked. - Secondly, Apple is always looking for apps that make the App Store look good, so providing a well polished experience with nice animations (long live the Hero pod) and a message that they can get behind got it noticed. - I've learned a lot working on this project, but the hardest lesson was to never ever force unwrap optionals again and prepare for high traffic better. The app uses the Dark Sky API and when it got featured (Apple doesn't give you any kind of warning) the free plan I was on reached its limit pretty quickly and because I was force unwrapping the value the API returns, the app crashed on launch for 10 hours straight. I've since been working to bring the rating back up in the US and it's at 4.0, which is reasonable. I'd love to know what you think or chat about the whole process. I'll also attach a few promo codes (the app is paid): HWPW73J6HLY6 9E3A6MH64M64 PE93W3TMYWRR WYYFW67WX36R NWFTAYPHKJKM It's available here: http://get-thrive.app

Visit product View on Hacker NewsOct 21, 2019

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

93%93% 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: apple, apps, code · Missing: mac, agents, macos

89%89% 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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

50%50% 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

35%35% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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