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Latent Space interviewed Supabase’s founders about agent-friendly backends. Local runtime and compute features discussed include alpha releases.
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Google announced Carbon, initially for selected cyber defenders. Broader access remains planned.
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OpenAI introduced GPT-6.1 Terra for visual reasoning and computer-based tasks. OpenAI reports stronger coding and fewer factual errors; its evaluations do not establish error-free work.
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A review of 28 studies reports that agricultural TinyML deployments largely rely on ESP32, STM32, and ATMega microcontrollers. Some plant-disease detection systems report 90–97% accuracy and latency as low as 7.6 ms, but memory and energy metrics are often missing. The review says models typically run inference on-device in the field while training remains in the cloud. Quantization appears in over half of deployments, whereas structured pruning and neural architecture search are rare.
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Prism ML’s ternary-weight Ternary-Bonsai-2-2B reduces the language model to about 0.52 GB, versus roughly 3.8 GB at FP16. It retains a 262K-token context window and image input, with the vision component loaded separately. Weights take values of −1, 0, or +1; additional scale metadata raises the actual storage cost. Image input requires an optional 0.36 GB Q8_0 mmproj file.
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OpenGVLab released the 0.9-billion-parameter InternVL2-1B vision-language model under the MIT license. It reportedly scores 754 on OCRBench, above PaliGemma-3B at 614 and Mini-InternVL-2B at 654. The model combines an InternViT-300M vision encoder with Qwen2-0.5B-Instruct and has an 8K context window. It targets tasks including OCR, document and chart analysis, and video question answering.
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Crusoe confirmed it sold an unfinished data center project in southeastern Wyoming to Google. Google said it plans to invest $17 billion to develop the campus; the purchase price was not disclosed. Crusoe announced the project last year with Tallgrass, with plans to eventually scale it to 10 gigawatts. Google is taking over development; the report says it had raised concerns about costs and the construction timeline under Crusoe’s management.
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Hugging Face described native TPU support through the TorchTPU backend. The integration brings Transformers, Diffusers and Kernels into PyTorch workflows using TPU devices.
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