HeyOctopus – Building a knowledge graph of learning content

Hacker News

Some friends and I developed HeyOctopus because we were looking for a better way to capture learning recommendations. Currently, people share learning paths in e.g. blog posts, Google Docs or Twitter threads, but such formats are not really suitable for providing feedback and improving such recommendations in the long run. HeyOctopus is a (hopefully) easy-to-use platform where all users contribute to a single content graph that is optimized over time based on user actions. For each content (e.g. video, article, ...), you can add relations to other content that you have found helpful (e.g. prerequisites, follow-ups, ...). Or you can structure content into sequences and share them with others. All contributions are used to optimize the content graph in the background ;) At the moment we are mainly focusing on topics like AI and web development, but we are very open and everyone is welcome to contribute! We would love to hear your feedback and any feature requests / bug reports!

Visit product View on Hacker NewsApr 22, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, single · Missing: mac, agents, macos

91%91% 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.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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, users · Missing: plus, intuitive, reviews

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

15%15% 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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