We're Fixing "Learning" with AI

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Hey folks, I was frustrated by how static and passive most AI-generated educational content is. A perfect explanation is great, but it doesn't guarantee "learning". To fix this, I built a system to dynamically inject and render interactive elements into the LLM's response, turning what would rather be walls of just text into a live learning environment. An example of it in action is this super cool explanation on the rules of basketball - https://thirdpen.app/article/FN3IbjLa/lens Or a simple DSA question of how bubble sort algorithm works: https://thirdpen.app/article/QRV7MYmu/lens We are able to see in this examples that "learning" with AI does not have to be just about text. I would love to confirm whether or not we're innovating in the right direction with this. you can try it here: https://thirdpen.app

Visit product View on Hacker NewsSep 27, 2025

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

69%69% 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 · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% 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.

Correct prediction on native model

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