Whoa, why are they now blocking API users? Is the API also so heavily subsidized that they are losing money on that?
@rasbt
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Apple Intelligence as primary AI touchpoint for average users
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The gap is even wider. For many people in my friends and family circle, the main touchpoint with AI is Apple Intelligence on the newer iPhones. Many heard of ChatGPT but are not super interested in that. That's because, like you said, they used the free version at some point and
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GLM-5.1 Real-World Usability Compared to GLM-5
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There is no reason to believe that they made GLM-5.1 worse than GLM-5 in real-world usability?
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GLM-5.1 Active Parameters Comparison with DeepSeek V3.2
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GLM-5.1 has 40B active parameters per token versus 37B in DeepSeek V3.2, so that can't be it.
Regarding quantization, sure, but quantization general technique that could also be applied similarly to DeepSeek V3.2. -
Questioning embedding size and code optimization explanations
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Interesting. Is there an explanation for this? I mean the embedding size is smaller but that's probably hardly it? So more like a code optimization?
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GLM-5.1: New Flagship Open-Weight Model with Superior Benchmarks
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Strong release! GLM-5.1 is a DeepSeek-V3.2-like architecture (including MLA and DeepSeek Sparse Attention) but with more layers. And the benchmarks look better throughout! Looks like THE flagship open-weight model now.
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RSS Feed Added to LLM Architecture Gallery
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Added an RSS feed to the LLM Architecture Gallery so it is a bit easier to keep up with new additions over time: sebastianraschka.com/llm-arc…
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Coding Agents: Markdown Skills and Registry Extensions
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Good suggestion. A coding agent can already read markdown files from the main repo, but you could add an explicit skills extension with a dedicated folder and registry.
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Learning Path: LLM Architecture, Reasoning Models, and Production Systems
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I would probably start with 1. my Build A Large Language Model (From Scratch) book to understand the basic architecture and basic pipeline. Then maybe 2. Build A Reasoning Model (From Scratch) for inference scaling and reinforcement learning
3. Maybe one of the "production"