Pinterest-like curation and exploration platform for links

Hacker News

Hello everyone, I’m one of the creators of Jouho, a content exploration platform. The “Taste engine” used by Pinterest was a major inspiration behind building this platform. Jouho can be viewed as a "Pinterest for links" that contain good content. Search engines today are extremely good at serving links that: - Have the right keywords - Have good SEO - Have a high number of quality backlinks Jouho takes a different approach to search. The links shown are knowledge pieces that contain good insights around a topic that a user searches for. Similar to how Pinterest shows related topics, Jouho harnesses the power of a knowledge graph to show related topics allowing us to explore search queries in different contexts. Also, links are ranked based on the topics explained in the content and the occurrence of related topics that are relevant to the the search query. Jouho relies on users creating collections and bookmarking links into them to seed its search index. Currently, there are over thousand links indexed across topics like Data Visualization, Design UI/UX and Frontend Development. As more links are saved by users the graph and the search index will be remodelled periodically to incorporate the new topics and links. We’re in beta stage right now, so I’d love some feedback if you can spare the time. Thanks for trying it out. Website: https://beta.jouho.in PS: You can share your feedback at any time through the feedback chat widget embedded in the site.

Visit product View on Hacker NewsFeb 18, 2019

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

83%83% 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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

56%56% 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

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

nativeThis product was originally launched on this platform.

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

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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