Mood Surf – Discover great Twitter threads by topic

Hacker News

The purpose of Mood Surf is help you find the best content within your niche by connecting you to the top experts. We believe that the best content for long-tail topics is written by real people, not SEO-optimized sources. We’ve started with Twitter threads as a rich source of insights on a variety of topics, from programming languages to self-improvement, and hope to expand to index all written content (including blogs, personal homepages, and more). Technical details: We ran graph analysis on the tech/startup-adjacent subset of Twitter graph to find the top contributors within a given niche. We extracted each user’s threads and embedded them using sentence transformers [0]. The “dive deeper” functionality is powered by approximate nearest neighbor lookup using Pinecone [1] We hope you like the product — share with us any feedback you have! [0] https://www.sbert.net/ [1] https://pinecone.io/

Visit product View on Hacker NewsAug 28, 2022

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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