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Learn Fractions with our Automated Private Tutor

Hacker News

Learn Fractions with our Automated Private Tutor

Hi HN, we are Jurgen (CTO) and Raphael (CEO) from amy.app We have been working for several years on a tool (Amy) that emulates a private tutor. Amy is based on Raphael's experience as a tutor and university lecturer in Mathematical Modeling. Today we want to show you Amy in action by teaching you fractions. Why fractions?: Because fractions form the foundation of many more advanced topics and is often the first major barrier to student success in maths. Mastering fractions is an important indicator of long term success. Why tutoring?: Tutoring has been shown to be one of the most efficient ways of learning. What's different about Khan Academy, etc.?: The main difference is, Amy identifies knowledge gaps in students and then tries to fix them. This differs greatly from most other tools which simply ask you to repeat the current exercise if you fail it. A word about AI/LLM: First, all our math is human-curated! Second, we experimented a bit with LLMs but their math constantly failed us. Also, we are looking for pedagogically relevant math, not scientific math. Here is a direct link to the Fractions course: <https://learn.amy.app?inviteCode=pef1d> In case it's not working you can use this code: pef1d Contact: jurgen@amy.app Discord: https://discord.gg/N6Kcegr7 Facebook: https://www.facebook.com/mytutoramy/ If you want to hear about our upcoming work on SATs and other topics, please use this form: https://share.hsforms.com/1MG8hGNpDRDyULjMOyLiaMw3lmuk Thanks for your time and we apprecate any feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, code · Missing: mac, agents, macos
38%38% 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
35%35% 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 · Strong signals: efficient · Missing: plus, platform, intuitive
17%17% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
10%10% 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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