Mood Surf – A topic-based discovery engine for Twitter threads

Hacker News

Hey HN! We’ve created Mood Surf, a discovery engine to explore topics that intrigue you. We miss the days you could stumble upon weird, wonderful, human-authored pages on the Web. Twitter threads are similarly niche and personal but so much good content gets lost in the algorithmic timeline. So we built Mood Surf to explore the conversations that you might not have found otherwise. Click “Shuffle” to jump into a random corner topic space. Click “Dive Deeper” to go down the rabbit hole of similar threads. 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! — The folks at Re:Search [0] https://www.sbert.net/ [1] https://pinecone.io/

Visit product View on Hacker NewsJun 10, 2022

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

54%54% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

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

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

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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