Sankofa – Chat with the Browser History

Hacker News

Hey Hackernews, I'm Ofer, head of developer relations at Vectara. Some of us read a lot of articles on the web, like those on HN, and it seems useful to be able to search the contents of the text we read (using semantic search), or ask questions about it (ChatGPT style). Sankofa is a new browser extension that allows you to index web pages you visit and search / ask questions about that content. We built it using Vectara's retrieval-augmented-generation (RAG) platform and hope this is useful to folks. This is an initial release - we would love to hear any feedback and suggestions. BTW, Sankofa which means "to retrieve" in the Twi language of Ghana.

Visit product View on Hacker NewsJan 30, 2024

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

93%93% 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: io · Missing: https docs, excited, just released

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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

66%66% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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