part of why i am screaming for Local AI and having your own infra they CANNOT win
OPEN SOURCE
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LLAMA 5 Vision: Dense Multimodal Model Across Four Sizes
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if it were up to me, there would be a LLAMA 5 it would be dense. it would be multimodal. it would come in four sizes: 1B, 8B, 80B, 800B. it would be iterated upon until the 8B LLAMA 5 totally outperformed the 80B LLAMA 3. this would be a huge asset to the community.
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EXL3 Quantization Development for AI Models
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working on an exl3 quantization for it right now i am more excited for this though (quote tweet)
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Inference Provider App Now Supports Responses API for Open Models
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Inference Provider starter app now supports the Responses API Best part: you can run any open weights model with the Responses API – even if the official provider doesn’t support it. Starter app in the next tweet – check it out!
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xAI Launches Grokipedia Open Source Knowledge Repository
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Join @xAI and help build Grokipedia, an open source knowledge repository that is vastly better than Wikipedia! This will be available to the public with no limits on use.
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Terminal-Bench Gains Recognition Across Major AI Models
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The industry is taking notice Terminal-Bench scores now appear on: DeepSeek-V3.1-Terminus model card Qwen3-Coder release Claude Sonnet 4.5 announcement
800+ GitHub stars | ~100 developer contributors -
Open Source Agentic Models Deserve More Attention Now
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been saying this since late July,
but especially with GLM-4.6 now; people need to pay closer attention to SOTA OSS Agentic models -
GLM-4.6 Released: Best Open Source Agentic Model
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GLM-4.6 is out i've had early access it's easily the BEST opensource
Agentic Model out there PERIOD Z AI cooked -
NumPy’s Simple File I/O API and PyTorch Tensor Limitations
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Numpy’s fromfile()/tofile() make me smile — no typing or byte order metadata, they just write/read the raw bytes. A joyful API! I wish PyTorch had them natively on tensors, so I wasn’t doing so many tensor.cpu().numpy() operations.
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DeepSeek V3.2 Sparse Attention Preparation for V4 Support
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DeepSeek V 3.2 is basically just a heads up for inference tools devs to implement their new sparse attention mechanism so they can add V4 support on day 1