On

Onri, the Google Map for micro-learning

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Onri, the Google Map for micro-learning

Hi, happy to share Onri, a micro-learning platform I've been building for intellectually curious people. Website: https://onri.ai # What is Onri Onri offers 2 things: 1. Self-learning: You enter a learning goal (e.g. tariff and globalization), Onri gives you a unique learning plan with 30 key concepts, combined with the best learning materials for each key concept. Future learning plans are optimized by what you've already learned. 2. Knowledge map: all key concepts of human knowledge on a 2D map for you to freely explore. Related concepts tend to be closer on the map, with path-finding between any 2 concepts. Onri is most useful for industry analysts, students, and life learners. # Why build Onri I'm often surrounded by smart and curious people, who spend much time learning from various sources and industry experts. It works great for people with high-quality networks or impressive backgrounds, but it's challenging for others without great educational support or mentors. How to even start learning without knowing what's missing? It's an "unknown unknowns" problem. I want to fix that by providing a guide of "what to learn": 1. Learning path depends on the gap between your knowledge and your learning goal, and the path is unique for everyone. For example, to learn about self-driving car, the learning path is wildly different for a car mechanic vs. a software engineer vs. a control engineer. 2. For every key concept, there exist free and high-quality learning materials online. They may come from various sources though (blog/Youtube/MOOC/podcast), so discovery and aggregation is important. # Onri vs. other learning methods Onri is meant to supplement other learning methods: - formal schooling: most in-depth, high credential, but costly in time and money - MOOC: high quality content, leaning towards academic topics, lower cost, but completing a course could be overwhelming - book: high quality, but often no more than 1 chapter is needed, may include outdated information - podcast: mixed quality, time-consuming to find key moments - Youtube videos: mixed quality, great visuals, time-consuming to find key moments - experts/mentors: expensive, constrained by personal networks and geo-location - LLM: real-time tailored answers, great for rabbit-holing but tend to miss the forest for the trees # Asking for feedback If you have any feedback (positive and negative) or how Onri could be specifically useful to you, please feel free to comment below or reach out to me (contacts on my profile). Would really appreciate that :)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: google, visual · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
51%51% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, video, google · Missing: mobile apps, ios, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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