Curate, Connect, Collaborate: Join TreeVed's Journey as an Early User

Hacker News

Hello early adopters, About TreeVed: Treeved.com is a community browsing tool to Organize and Discover content with people. We help users to express and connect with people who value similar content. Humans have always discovered information through people but now we get most of our recommendations from AI. TreeVed enables users to get link recommendations through people you can trust. Key Features: - Seamlessly curate web content with our innovative browser extension. - Create curated lists that resonate with your passions. - Connect with like-minded individuals through shared interests. - Collaborate in shaping a platform that puts users at its core. Our Quest for Feedback: We're reaching out to the Hacker Rank community for valuable insights during our early stages of development. How You Can Help: Your feedback will shape the future of TreeVed. Join us in refining a platform that resonates with content enthusiasts worldwide. Get Involved: Experience the future of content curation and join us in transforming how we engage with online content. Visit https://treeved.com to dive in. Experience Community Browsing! Grow Together!

Visit product View on Hacker NewsAug 9, 2023

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2points

Did not reach leaderboard

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

55%55% 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: platform, users · Missing: plus, intuitive, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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: collaborate · 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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