Google has launched its new image generation model, the Nano Banana 2, which is powered by Gemini 3.1 Flash Image. The company says the new model has the capabilities, world knowledge and reasoning of Nano Banana Pro, but it can accomplish tasks at “lightning-fast speed.” That enables rapid editing and the quick creation of various iterations using a single prompt.
“We will continue to investigate,” she added.
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Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.。下载安装 谷歌浏览器 开启极速安全的 上网之旅。对此有专业解读
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So how can a couple co-create their Ones?