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An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More

Hacker News

An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More

Hey folks, Elias here. Excited to unveil my latest project. Why I Built This: Traditional keyword search isn't cutting it. I've used LLM-embeddings to provide more nuanced, relevant results. How It Works: LLM-embedding similarity on curated datasets for semantically similar results. No need to iterate over keywords any more. Current Datasets: - YC Companies - Show HN Posts, - Ask HN Posts - ProductHunt Startups - Github Top 200k Repos Use Cases: - Validate a product idea's existence - Check if someone already Asked HN something - Have fun - search random terms and see what pops up Want to see other datasets? Got one in mind? What would you use it for? (I'm even thinking of adding all PG's tweets - so feel free to be creative.) Eager for your feedback and ideas. Hope you find this useful and fun!

Visit product View on Hacker NewsOct 25, 2023

Actual performance

7points

4comments

Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · 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.

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

70%70% 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 · Missing: supports, reddit linkedin, podcasting

52%52% 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

44%44% 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

26%26% 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

12%12% 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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