Fin de la programmation.
— Stephane Mallard (@StephaneMallard) 21 juin 2026
Les machines qui génèrent leur propre code.
Nous y sommes. https://t.co/sO1JWjn1gN
End of programming.
Machines that generate their own code.
We are there.
GENERATIVE AI
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End of programming, machines generate their own code
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Meta AI unveils Artifacts tab to store presentations and documents
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Meta AI gets a new Artifacts tab on the web. All presentations, documents, web pages and other creations would be stored there. Bridging the gap.
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50 Steps to Master AgenticAI in 2025-2026
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50 Steps to Master #AgenticAI in 2025 – 2026
by @Python_Dv #GenerativeAI #ArtificialIntelligence #MI #MachineLearning -
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 -
Structured prompts for better AI results
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Better prompts = better AI outputs • Structured → precision
• Analytical → research
• Conversational → ideas
• Planning → execution Great prompts are frameworks, not guesses. Via Giuliano Liguori (
@ingliguori
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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.