Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Cinematic wide shot of a vast geometric orbital data archive in deep space, crystalline structures with glowing blue pathways extracting luminous information fragments toward a distant tactical station, Ender’s Game inspired military sci-fi aesthetic, high contrast lighting with cool blues and deep blacks, sleek futuristic architecture suggesting strategic knowledge retrieval at massive scale.
Introducing the File Search Tool in Gemini API https://blog.google/technology/developers/file-search-gemini-api/
Memory in AI agents seems like a logical next step after RAG evolved to agentic RAG. RAG: one-shot read-only Agentic RAG: read-only via tool calls Memory in AI agents: read-and-write via tool calls Obviously, it’s a little more complex than this. I make my case here: https://x.com/helloiamleonie/status/1985376609935769930
Hey, AI Devs! Don’t sleep on the new Gemini File Search API! Feels like the easiest way to build agentic RAG systems. I built a little MCP server to analyze codebases with semantic search (Gemini File Search) & agentic search. Fun chatting with @karpathy’s nanochat project. https://x.com/omarsar0/status/1988236096195776683
80% of enterprise data is unstructured, locked in PDFs, reports, and diagrams that traditional tools can’t parse or govern. Introducing ai_parse_document, state-of-the-art document intelligence on Databricks. With a single SQL command, teams can now turn any document into https://x.com/databricks/status/1988271796076912928
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