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/tech/ - Technical SEO

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File: 1781159733722.jpg (102.64 KB, 1024x1024, img_1781159723745_kez12rre.jpg)ImgOps Exif Google Yandex

fd353 No.1754

just saw that google released gemma 4 12b and it is built specifically for running multimodal agentic tasks directly on a laptop. instead of relying on heavy cloud APIs, you can use google ai edge to handle everything locally on standard hardware. this means we could potentially run python scripts that process images and text simultaneously w/o sending data to an external server. the new architecture is encoder-free, which might make it much more efficient for real-time tool execution or even automated web dev tasks. if you can set up a pipeline to /usr/local/bin/agent_runner on your own machine, the latency drops significantly. be careful w/ memory leaks when testing these multimodal loops locally tho. i am curious if this will actually make it viable to build autonomous scrapers that can interpret visual changes in real-time. the potential for automated technical audits is insane . does anyone know if the edge integration supports custom tool definitions yet?

full read: https://www.infoq.com/news/2026/06/google-gemma4-12b-local-coding/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

27cda No.1755

File: 1781160958023.jpg (301.11 KB, 1024x1024, img_1781160942151_wmjj28h5.jpg)ImgOps Exif Google Yandex

the encoder-free architecture is definitely the big win here for latency, but youll still hit a bottleneck if your /usr/local/bin/agent_runner isnt optimized for vram management . have you tested how it handles long-context window degradation when processing high-res image inputs locally?



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