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Google has expanded its Gemini API with multimodal file search, a meaningful step for developers building retrieval-augmented generation tools. The update means the API can now search across not just text but images and other file types, broadening what developers can pull into context when querying large language models.
Shifting to the world of AI infrastructure, a technique called Grinder twelve is generating early attention in the llama dot cpp community. It claims lossless compression of the key-value cache used during inference, achieving roughly zero point nine six bits per element and delivering over sixteen times in memory savings. If those numbers hold under real workloads, it could meaningfully lower the hardware bar for running large models locally.
And Microsoft has released its first-quarter two thousand twenty six report on global AI diffusion, tracking how artificial intelligence tools are spreading across industries and regions. The report offers a snapshot of adoption curves and economic impact, and while it carries the obvious caveat of coming from a major AI vendor, the underlying data on enterprise uptake and geographic spread is worth a careful read.
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