Moocable – find people studying the same online course/book

Hacker News

Hey, everyone I made a website that lets you find study partners/groups based on the specific course/book you are studying. It's 100% free + no registration required. Why: I know there are many subreddits for finding study partners. But, one of the biggest reasons we can't find the right partner/group is lack of clarity. Most posts that I read on Reddit were something like: "I'm interested in programming. Let's study Python together..." - That's not a clear goal. But, when you say "Let's study the Python for Everybody course on Coursera . I'm from Mumbai, India, and I'm looking for study partners in the* timezone (UTC+5:30). I'm fluent in Hindi language. I have 2 years of experience in C++ , so I understand the fundamentals of programming. Happy to host the group and contribute " - that will lead to higher quality engagement. It's also difficult to search + filter posts on Reddit, based on my learning requirements. Keeping all these pain points in mind, I've built Moocable, where you can search & filter posts based on the course/book you're learning + timezones + languages + level of experience. Lastly, you can use it to discover new learning materials. Some of you might have used https://www.classcentral.com/ I'm building a similar library for Moocable, you can easily search and discover new learning materials. If you are looking for study partners, give it a try! P.S.: I've manually filled the first ~100 posts. Each post is 100% authentic, scraped from subreddits/Coursera forums/niche websites.

Visit product View on Hacker NewsJul 21, 2024

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

68%68% 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: host · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% 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: new · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% 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

16%16% 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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