Node-Based Flashcards (POC)

Hacker News

Hi HN! I want to share my progress on this idea that I have been working on: Flashcards, but dynamic, and with node-based templates. The demo is pretty limited, but I just wanted to try out if it even works. The app itself is very modular and allows adding extra nodes / pages etc easily. The final goal would be to have many different nodes (that users can download from a plugin store), which can be used to for example: - generate image / audio - create interactive areas in flashcards (e.g. graphs) - dynamically interact with other flashcards (e.g. reference related cards) ... I would love to hear some feedback! On the GitHub page you can find a demo video as well as a link to the POC deployment.

Visit product View on Hacker NewsJun 15, 2024

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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.

best fitHighest predicted score across all platforms for this description.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal

35%35% 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

31%31% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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