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Learning Loop – a personalized discovery platform for learning

Hacker News

Learning Loop – a personalized discovery platform for learning

Problem Learning a new skill online has become impossible for most people: ● There is no universal starting point - There are 400 to 4000 online courses for the topics majority of internet users are searching to learn online, and over 10,000 videos and blog posts. ● Too much content, no effective filters - Too many unsuccessful search attempts for finding the right learning resources make users feel stupid and quit their learning journey. ● People learn in silos and their learning insights aren’t transparent- Millions of people have been learning the same things every day, for years, yet there is no easy-to-discover information on the best ways to learn something Anyone can be great at anything, given the right guidance, but finding guidance has become too hard. Most people live a lifetime and die without learning what they can love and be great at. That aside, learning is generally hard. When you learn something new, you feel temporarily incompetent. The world’s learning interfaces aren’t designed with that in mind, and therefore fail most users in their learning journey. Solution: Learning is acquiring the right knowledge related to your learning goal, in a timely manner. A plethora of factors including your competence level, weekly schedule, headspace and your geographic location and wealth can change the complexion of learning, but learning has an overarching workflow. Learning Loop embeds the best practices from great learners in every field into a product that streamlines the learning process and increases a user’s exposure to hyper local learning insights and steps so that the user can learn like the top 1% of learners. We are building towards: a software product that helps anyone learn anything they want and be great at it. Our starting point: a platform that helps startup founders find hyper local startup lessons documented by seasoned founders, neatly organized in categories such as fundraising, sales, product, growth, hiring, fully searchable with hyper local filters such as funding stage, region, industry and investor.

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Actual performance

3points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
89%89% 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, new · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
12%12% 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.

Incorrect prediction on native model

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