Mo

Mobile-Friendly Arithmetic Trainer

Hacker News

Mobile-Friendly Arithmetic Trainer

~20 years ago, HN user sophiebits made a nice arithmetic trainer: https://arithmetic.zetamac.com/ (discussed here: https://news.ycombinator.com/item?id=1728309 ) I think it's great, but my 7yo son doesn't want to use it every day, because the countdown timer creates unnecessary pressure. I thought about sticking a piece of masking tape on the iPad screen to cover the countdown timer, but my son didn't like that idea. So I built something similar, but without the timer: https://math.twilam.com/ The features I plan to add to make it 'complete': 1. Select operation types and number ranges (like sophiebits' site). 2. Select the number of problems you want to do (analogous to sophiebits' timer). 3. A 'power bar' which fills up as you reach the number of problems you wanted to do. I might also add: 4. A way to persist config options. 5. At the start and end of each session, display the length (# days) of your current streak.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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, user, new · Missing: agents, macos, agent
58%58% predicted probability of success on Product Hunt, 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
47%47% 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: way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
47%47% 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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