Ben's Bites AI Search

Hacker News

I built an open source search across all of the best, highly curated resources in AI. It supports semantic search (powered by OpenAI embeddings + Pinecone vector DB) and traditional keyword search (powered by Meilisearch). Search indexes are updated regularly via cron by crawling and extracting links from the Ben's Bites AI Newsletter ( https://www.bensbites.co/ ), which now has over 60k subscribers. By default, search results are sorted by recency because things move so fast that we found ourselves often wanting the latest news on a given AI sub-topic. But you can also change it to sort by relevancy instead. I built this as a tool for Ben Tossell's newsletter as he had reached out asking for help, and I hope that other AI enthusiasts will find it helpful in staying up-to-date with the latest in AI as well. Source on GitHub: https://github.com/transitive-bullshit/bens-bites-ai-search

Visit product View on Hacker NewsMar 29, 2023

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

52%52% 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: new, openai, open · Missing: mac, agents, macos

50%50% 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: supports · Missing: reddit linkedin, podcasting, created

49%49% 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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · Missing: arr, mrr, revenue

14%14% 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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