Se

Semantic Search for Trends

Hacker News

Semantic Search for Trends

hello everyone! it's pretty hard to tell signal from noise when it comes to social media trends when trying to make short to mid-term inferences about anything. We recently built semantic search around a (semi)real-time trends dataset we've been curating for some time now (~2 years). tldr, search queries are embedded and we perform a similarity search against our vectorized trends dataset, returning X most related trends. There's been some interest in accessing the data directly so we built an API and some ui around this feature - we welcome you to give it a look or try and let us know what you think! I think there could be many interesting applications in leveraging and applying this data - financial/market analysis factoring in real-time trends, AI personas that are "up-to-date" (I'm currently working on something like this), curating information/news feeds for your niche, etc. would love to hear more ideas if anyone would like to share. you can try it out at https://trndgtr.com leave a comment or feel free to email me directly at aurei@trndgtr.com for any questions or thoughts :) hope you all have a peaceful pre-election week! aurei

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email · Missing: mac, agents, macos
81%81% 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
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% 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 · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% 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
13%13% 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.

Correct prediction on native model

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