I

I built an app to organize fragmented learning

Hacker News

I built an app to organize fragmented learning

Hi HN, I built an app to solve a problem I kept running into: I’d read great articles or watch insightful YouTube videos, but within a few days, I’d forget most of what I learned. Taking detailed notes felt like a chore, and I'd rarely revisit them anyway. To solve my own problem, I built Curio. It's an iOS app designed for low-effort capture and active recall. How it works: 1. Share any article or YouTube link to the app. 2. It automatically extracts key ideas as bullet points and links them back to the original context (the specific paragraph or timestamp). 3. The app then generates quizzes from your saved points to help you actually retain the information. The core idea is to move beyond passive bookmarking into active learning, without the heavy lifting of manual note-taking. I'd love to hear your feedback, especially on the quality of the key idea extraction and whether this is a problem you face too. App Store: https://apps.apple.com/app/curio-save-learn-retain/id6745309... More information: https://www.curio-app.com (Currently the app is iOS only)

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1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, ios · Missing: supports, reddit linkedin, podcasting
84%84% 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, context · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs
45%45% 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
39%39% predicted probability of success on AppSumo, 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
39%39% 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 · Strong signals: active · Missing: arr, mrr, revenue
24%24% 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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