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Audit your Anki flashcards at flashcardaudit.com

Hacker News

Audit your Anki flashcards at flashcardaudit.com

Hey, my name is Tyler, I made this. flashcardaudit.com is a tool that allows users to upload an Anki collection so that an AI auditor (Gemini 3.5 Flash) can review the factual correctness of each Anki card. I discovered that a non-trivial number of cards contain obvious factual errors; which means people are likely memorizing false information every day. Here is a free example of what the audit results look like: https://flashcardaudit.com/results/sus8yj5wigtu Video of me talking about it: https://www.youtube.com/watch?v=kW7vN_TBr60 You can learn more about my story here: https://flashcardaudit.com/about More on how it works: https://flashcardaudit.com/how-it-works FAQ: https://flashcardaudit.com/faq Documentation and reading material here: https://flashcardaudit.com/library Thanks for checking it out!

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, gemini · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

Incorrect prediction on native model

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