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I made a spaced repetition tool to master coding problems

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I made a spaced repetition tool to master coding problems

As you solve LeetCode questions, you can mark them as hard, medium, or easy. The tool will then recommend questions you should review based on (1) how hard the question was for you and (2) how much time has passed since you last reviewed it. I'd recommend normally attempting LeetCode problems and just marking them as hard, medium, or easy for you at first so the tool knows which problems to recommend you review! Here's the theory behind spaced repetition and learning if interested: https://www.codecademy.com/article/spaced-repetition

Visit productView on Hacker NewsApr 26, 2024
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: coding, code · Missing: mac, agents, macos
17%17% 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
14%14% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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