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I made an app for developers to manage their deeplinks

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I made an app for developers to manage their deeplinks

Hello everyone! As a developer, I use deeplinks a lot to test my app. Before I build Deeplink Buddy, here's my workflow whenever I want to run a deeplink on a simulator: 1. Save all deeplinks in a note 2. Manually find and edit the param of the deeplink I want to run 3. Copy the deeplink and paste it to the terminal or the Calendar in the simulator 4. Run the deeplink Now with Deeplink Buddy, everything will be super simple and easy: - All your deeplinks are stored in one app and synced with iCloud, so you never lose them - A clean and intuitive interface that helps you quickly find the param and update it - Easily select the simulator you want to run the deeplink. You can also run the deeplink on your mac to test your mac app. - Run your deeplink with just one click If you have any feedback/questions/suggestions, please let me know below!

Visit productView on Hacker NewsOct 25, 2022
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Actual performance

5points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac · Missing: agents, macos, agent
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
23%23% 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
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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