I made an app to centralize my saved post and remember why I saved them

Hacker News

Hey HN, I worked on this app alongside my 9-5 last month, As someone who spends way too much time scrolling through Instagram and TikTok, I always struggled to stay organized with all the amazing content I saved. Recipes, DIY ideas, travel inspirations… it quickly turned into chaos! That’s why I built this app to centralize, organize, and remember why I saved posts. Here’s how it works: - Save links from Instagram, TikTok, and more using the share feature (no need to quit the app). - Add notes to remember why they’re important. - Find everything in one place The last app I made took me 3 years to launch. This year I plan to launch 6 apps and this is the first one. Thanks for checking it out, and happy 2025! Adrien

Visit product View on Hacker NewsJan 28, 2025

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2points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, notes · Missing: mac, agents, macos

88%88% 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 · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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 · Missing: plus, platform, intuitive

31%31% 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

29%29% 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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