Tag: embeddings

  • Tomasz Tunguz: Data & AI Infrastructure Are Fusing

    Source URL: https://www.tomtunguz.com/data–ai-infrastructure-are-fusing/ Source: Tomasz Tunguz Title: Data & AI Infrastructure Are Fusing Feedly Summary: AI breaks the data stack. Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users. This architecture worked for traditional analytics. But…

  • Cloud Blog: The new data scientist: From analyst to agentic architect

    Source URL: https://cloud.google.com/blog/products/data-analytics/enabling-data-scientists-to-become-agentic-architects/ Source: Cloud Blog Title: The new data scientist: From analyst to agentic architect Feedly Summary: The role of the data scientist is rapidly transforming. For the past decade, their mission has centered on analyzing the past to run predictive models that informed business decisions. Today, that is no longer enough. The market…

  • Cloud Blog: Agent Factory Recap: Deep Dive into Gemini CLI with Taylor Mullen

    Source URL: https://cloud.google.com/blog/topics/developers-practitioners/agent-factory-recap-deep-dive-into-gemini-cli-with-taylor-mullen/ Source: Cloud Blog Title: Agent Factory Recap: Deep Dive into Gemini CLI with Taylor Mullen Feedly Summary: In the latest episode of the Agent Factory podcast, Amit Miraj and I took a deep dive into the Gemini CLI. We were joined by the creator of the Gemini CLI, Taylor Mullen, who shared…

  • Cloud Blog: Gemini and OSS text embeddings are now in BigQuery ML

    Source URL: https://cloud.google.com/blog/products/data-analytics/use-gemini-and-open-source-text-embedding-models-in-bigquery/ Source: Cloud Blog Title: Gemini and OSS text embeddings are now in BigQuery ML Feedly Summary: High-quality text embeddings are the engine for modern AI applications like semantic search, classification, and retrieval-augmented generation (RAG). But when it comes to picking a model to generate these embeddings, we know one size doesn’t fit…

  • Simon Willison’s Weblog: Quoting James Luan

    Source URL: https://simonwillison.net/2025/Sep/8/james-luan/ Source: Simon Willison’s Weblog Title: Quoting James Luan Feedly Summary: I recently spoke with the CTO of a popular AI note-taking app who told me something surprising: they spend twice as much on vector search as they do on OpenAI API calls. Think about that for a second. Running the retrieval layer…

  • Simon Willison’s Weblog: Quoting Jason Liu

    Source URL: https://simonwillison.net/2025/Sep/6/jason-liu/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Jason Liu Feedly Summary: I am once again shocked at how much better image retrieval performance you can get if you embed highly opinionated summaries of an image, a summary that came out of a visual language model, than using CLIP embeddings themselves. If you tell…

  • Simon Willison’s Weblog: Introducing EmbeddingGemma

    Source URL: https://simonwillison.net/2025/Sep/4/embedding-gemma/#atom-everything Source: Simon Willison’s Weblog Title: Introducing EmbeddingGemma Feedly Summary: Introducing EmbeddingGemma Brand new open weights (under the slightly janky Gemma license) 308M parameter embedding model from Google: Based on the Gemma 3 architecture, EmbeddingGemma is trained on 100+ languages and is small enough to run on less than 200MB of RAM with…

  • Cloud Blog: From query to cart: Inside Target’s search bar overhaul with AlloyDB AI

    Source URL: https://cloud.google.com/blog/topics/retail/from-query-to-cart-inside-targets-search-bar-overhaul-with-alloydb-ai/ Source: Cloud Blog Title: From query to cart: Inside Target’s search bar overhaul with AlloyDB AI Feedly Summary: Editor’s note: Target set out to modernize its digital search experience to better match guest expectations and support more intuitive discovery across millions of products. To meet that challenge, they rebuilt their platform with…