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Runtric – Turn any topic into a chapter-based learning path

Hacker News

Runtric – Turn any topic into a chapter-based learning path

Hello. We built an AI learning service that lets you create a curriculum on any topic you want and study without worrying about hallucinations. While existing AI learning services have mostly focused on how much text inside a PDF the AI can read and answer from, we focused more on how the AI should explain and guide so that real learning actually continues. That’s why we aimed to make it feel less like a simple Q&A tool and more like studying with a teacher who knows how to teach well. It’s also simple to use. 1. Enter what you want to learn, choose your level and the number of chapters, and a curriculum is generated. 2. Click the curriculum card you want and start learning right away. 3. Each chapter comes with both a tutorial and a chatbot, and the chatbot continues the conversation while understanding the context of the current chapter. 4. So even when a user gets a quiz question wrong, they don’t have to explain everything again from the beginning; the AI can reflect the situation and help immediately. Lastly, to briefly explain why we built this service: just because the AI era has arrived doesn’t mean the quality of education has automatically improved. In fact, we felt that with AI added on top of short, stimulating content consumption like Reels or Shorts, people often lose focus when trying to study. So we focused on reducing the real problems that get in the way of studying with AI, like hallucinations, unstructured learning flow, and the limits of traditional education methods that still haven’t changed. Thank you. Service link: https://runtric.com/

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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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, plain · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way, education · Missing: mobile apps, ios, personal
63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
44%44% 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
41%41% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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