25 summarised stories about Embedding Models, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 1 week ago 17 ● 7 sources
A commenter on Hacker News said EmbeddingGemma 2 being under the Apache 2.0 license matters because embedding apps often generate and store thousands to millions of vectors for later comparison.
The New Stack 1 week ago 7 ● 7 sources
Google released EmbeddingGemma 2 to enable on-device search across text, code, images, video, and audio using a shared embedding space and modular encoders. The model reaches 740 million parameters and is tested at about 567MB of active RAM on a Pixel 11 Pro. Developers can deploy smaller embedding indexes by truncating vectors to 512/256/128 dimensions without retraining and add new modalities later without re-embedding existing data, with LiteRT/MediaPipe Tasks now and Android ML Kit integration coming soon.
Product Hunt 1 week ago 19 ● 7 sources
Google DeepMind launched EmbeddingGemma 2, an open embedding model that maps text, code, images, video, and audio into one shared representation for cross-modal search. The full multimodal model is 740M parameters and needs about 567MB of RAM on a Pixel 11 Pro. Model weights are now available on Apache 2.0 via Hugging Face and Kaggle, and the MTEB Code score rose from 68.76 to 78.68.
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19 October 2026
The day’s biggest throughline wasn’t just smarter AI—it was governed AI. Atlassian CEO Mike Cannon-Brookes made the point bluntly: “the accountability is with Mike,” meaning the end user, not a shrug at “the AI did it.” His reference to the Oct. 6 rollout of an “Agentic Multiplayer Protocol” signals what enterprises actually need next: audit trails where agent contributions show up in document history with attributable edits, not vague logs. Pax8’s new 8os goes after the same reality from the other side of the org chart: IT partners can deploy and govern agents as a single workforce across customers, with per-customer separation, metered usage, and hard caps. Even Austria’s Agentic Firmenbuch—plain-language querying of the Firmenbuch company register with sources—fits the pattern of verifiable outputs that a compliance team can live with.
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