Numerikos – A personalizable digital math workbook

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Numerikos is a math practice platform where students can work through distributions of practice problems or setup their own study routines. Try the demo (no signup required, data stored locally) at https://demo.numerikos.com I designed this for my 4th-grade son and so far he really likes it. The main features: 1. Practice problems: generated by rules I've designed particularly for each problem type. They try to avoid trivial problems and create an interesting distribution of problems. 2. An editable dashboard: you can track different metrics or setup different collections practice problems (timed or not timed). 3. Review page: click the star while you are practicing to review a particular problem later. 4. Currently available in English and Chinese (the translation still needs improvement). I wanted something that allows users to personalize the areas of math that they want to practice more. I will eventually make an algorithm that helps users to decide what to study next, but it will have an escape hatch where the user can override some of its assumptions. What's next: 1. Support 5th grade math. 2. Release the production version. 3. Support Spanish language version. My main source of inspiration is Chris McMullen's collection of math workbooks (followed by Khan Academy and Math Academy). My long term goal is to support math at all grade school levels and some college level mathematics and physics. I'd be curious to hear your thoughts on the UI and any constructive criticism you have.

Visit product View on Hacker NewsNov 20, 2025

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

78%78% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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.

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