Between OpenClaw and VLMs getting better much at computer use – the loom recording to automated workflow dream is nearly ready for prime time
LLMS
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Gemini refuses to download web pages citing security compromise
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I just tried using Gemini in Chrome for the first time. I asked it to go and download a few web pages for me, but apparently it can't download data to my computer because that would "involve compromising system security by circumventing security features". I practically begged it
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Qwen3-Next-Coder: Local Model for Agentic Coding Tasks
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Agreed! But in the meantime, there was also Qwen3-Next-Coder, which is even more attractive as a local model for agentic coding tasks:
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Post-Training as Key Differentiator in Modern AI Development
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Good question. I would say the post-training is usually one of the biggest distinguishing contributors these days
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DeepSeek V3.2 and Arcee Trinity Release Timeline Updates
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It's been two whole months since DeepSeek V3.2 and two whole weeks since Arcee Trinity!
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GLM-4.5: Notable benchmark performance between o3 and Claude
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I think 4.5 was the first really notable one. Benchmark-wise it sat between between OpenAI o3 and Claude 4 Opus.
I have a brief section about it in my Big LLM Architecture Comparison article: https://
magazine.sebastianraschka.com/i/168650848/11
-glm-45
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New Open-Weight Models Coming Soon for Analysis
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And so it begins. Looks like / I hope there will be some fresh open-weight models to analyze and write about soon… It's been a while!
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Model-Agnostic Approach Recovers 90% Sequence Packing Gains
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5/5 Because this approach is model-agnostic, it applies to any architecture. Even on transformers (like Qwen2.5-7B by @alibaba_cloud
, this method recovers ~90% of the gains of sequence packing, without relying on specific attention implementations. Full breakdown + -
Model-Agnostic Approach: Minimizing Padding with Truncation and Batching
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4/5 So we took a different, model agnostic path: minimize padding before it reaches the model using truncation combined with padding-aware batching.
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Sequence packing: limitations for hybrid models like Jamba
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3/5 Sequence packing helps for transformers, but it relies on architecture-specific support that is often missing and introduces implementation risks for non-transformer or hybrid models like @AI21Labs' Jamba.