Komihåg – Learn and remember things you read

Hacker News

Hi HN, I’m Colin and I’m building Komihåg ( https://komihag.com/ ), to make it as easy as possible to remember things you read. Just select any text on your iOS device and share the selection with the app, this will transform the shared text into a Q&A flashcard using LLMs. You can then scroll through your decks of cards in the app, or create a widget that displays your cards continuously. I got the idea while reading machine learning books. Sure I could create Kindle bookmarks or copy paste interesting parts, but I noticed that I never really took the time to revisit them, so adding an iOS widget with the things I want to learn and that inevitably reaches my eyes many times per day seemed like a good idea. The app is build with SwiftUI, using Supabase for authentication and storage. The app is pretty bare bones at the moment, some ideas I have for improvement are: - Scheduling for the widget. E.g. show new card every 5 minutes. - Lookscreen widget / Watch widget. These cannot be interactive (I think), so I would have to reformulate the Q&A pair into a combined statement. Let me know if you think the concept is interesting enough to continue working on.

Visit product View on Hacker NewsFeb 28, 2025

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

82%82% 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: mac, new, using · Missing: agents, macos, agent

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on TrustMRR, 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

60%60% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% 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.

Incorrect prediction on native model

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