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We built semantic search that never embeds your data. @polygres · XDL

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We built semantic search that never embeds your …

We built semantic search that never embeds your data. @polygres

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Today we’re launching WonderSearch (YC F26) Search millions of unstructured documents without pre-embedding the corpus. With comparable/better retrieval recall than embedding-based search. WonderSearch searches at query time: > No corpus-wide embedding step. > No vector index

Introducing dots, powered by GPT-6 Astra. Remarkably capable, always-on agents built to handle everything.

Our Ultra and Pro subs have been using info monitoring capabilities in AI Mode in Search, and now we’re rolling it out to everyone globally. Just tell AI Mode exactly what to look for & Search will continuously check across changing info on the web like sites, forums and social

Google secrets they don’t tell you🥹

@Know Your Computer

Introducing Expert Intelligence ✨ A cross-@Google initiative that helps you engage with your trusted sources, starting with eligible @GooglePlay ebooks in Gemini Notebook. You can now combine expertise from your favorite authors with other sources and engage with your books in

@Gemini Notebook

Mia Milkers Tired of filters? What if you could create freely? I found a platform with transparent pricing and great support. Curious to try uncensored AI video generation? Check it out here:

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a

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