Open Source website to learn anything with maps

Hacker News

Hey everyone. Today we released a new update to our website where we completely redesigned the website from scratch and now anyone can come in and add resources and links of their own. The goal of the website is to visually present the most efficient paths for learning any topic on Earth. Would really love to hear your thoughts and feedback. It's also Open Source ( https://github.com/learn-anything/learn-anything ) so if you know a little React, we would love to get any help we can get. Thank you. The website can be seen live here: https://learn-anything.xyz/

Visit product View on Hacker NewsDec 3, 2017

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual, open · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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