Individual learning plan generator from what you like on YouTube

Hacker News

We are creating a platform that helps find a future career by analyzing Youtube interests. After that, we compare these interests with careers and wanted to suggest learning projects and content for each skill. The best solution seemed to be a marketplace where people share their skill's knowledge and earn money. However, we failed to find mentors for 367 skills. So we replaced user-generated content with best-rated YouTube videos associated with each skill. Every cloud has a silver lining: we've got the ability to generate an individual learning plan for every person, skipping the skills that the person has built up on YouTube and focusing on blind spots. People can update their interests and upgrade their career learning plan and list of blind spots. In addition, people can build up specific skills on demand as profoundly as they want. Here you can explore a free demo learning plan for various careers — https://unschooler.me/tutorials/

Visit product View on Hacker NewsNov 16, 2022

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

53%53% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

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

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

14%14% 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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