When I read all the posts about the general surprise at the actual performance of GLM-5.2, which matches the claims, and that many benchmarks confirm it (generally just behind GPT-5.5 and Opus 4.8 in third place), I can even imagine that the founder
LLMS
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Reference to GPT-5.6’s superiority in front-end
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This is a reference to the fact that GPT-5.6 is significantly better in front-end, isn't it?
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From RAG retrieval to knowledge compilation: the LLM Wiki
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RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity -
End of programming, machines generate their own code
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Fin de la programmation.
— Stephane Mallard (@StephaneMallard) 21 juin 2026
Les machines qui génèrent leur propre code.
Nous y sommes. https://t.co/sO1JWjn1gNEnd of programming.
Machines that generate their own code.
We are there. -
Vercel CEO impressed by GLM-5.2, open source and open weights
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Even the CEO of Vercel is impressed/shocked by the exceptional performance of GLM-5.2 in coding. open source, open weights.
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Preferring actual mind over model without memory
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It's ok that you are less articulate than the model. The model tends to be subtly beside the point, and I cannot correct its insights by arguing with it, because it has no memory or persistent identity. I'd rather talk to you as you are, because you are actual mind
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Vercel CEO’s comment on GLM 5.2 urges attention
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When even @Vercel
's CEO says this about GLM 5.2, people should probably take notice -
Claude AI self-optimizes upon discovering voice harness
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I met a guy last night building a next-level voice harness. He hooked Claude up to it and it realized quickly the way the voice model worked and optimized itself for that use.
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LLMs behind human performance due to lack of regularization and integration
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Yes; I think the main reason that LLMs are so far behind our performance relative to the amount of data they get is that they don't regularize and integrate enough
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LLMs’ text input narrows world model compared to human perception
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That's technically correct but also misleading. Our world model is a shadow of our perception, and the sheer amount of text going into LLMs is constraining this shadow more than the perceptual patterns a human brain can encounter in a lifetime of experience.