Ces derniers jours donnent le vertige : Claude et GPT franchissent un nouveau cap, ouvrant un champ des possibles sans précédent. Google lance ses agents IA. La Chine fait courir un semi-marathon à ses robots humanoïdes. Des maladies jusqu'ici redoutables sont sur le point
LLMS
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Scaling Test-Time Compute for Agentic Coding
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Scaling Test-Time Compute for Agentic Coding Kim et al.: https://
arxiv.org/abs/2604.16529 #ArtificialIntelligence #AIAgents -
User Switches from Kimi 2.5 to GLM 5.1 Over Performance Issues
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Not just slow but poor performance on tasks. Kimi 2.5 has had some task and tool following problems too. I've shifted to GLM 5.1 for now.
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First Use Cases for GPT 5.5 When Available
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What is the first thing you will be doing with GPT 5.5 when it becomes available?
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Reference to AI model version 5.5
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base64.b64decode("NS41") = 5.5 pic.twitter.com/OVvARoD7Eq
— Defend Intelligence (Anis Ayari) (@DFintelligence) 23 avril 2026base64.b64decode("NS41") = 5.5
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LLM Emotions: Simulated Empathy Without Consciousness or Fear
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Emotions evolved to keep us alive, but an LLM has no pulse and no fear of being turned off. Simulated empathy is a superficial property of the training data, not a sign of an internal conscious life.
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Stanford Paper on Legal RAG Hallucinations in AI
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Paper: https://
law.stanford.edu/wp-content/upl
oads/2024/05/Legal_RAG_Hallucinations.pdf
… Check out http://
AlphaSignal.ai to get a daily summary of top models, repos, and papers in AI. Read by 280,000+ devs. -
Stanford Study Exposes Hallucinations in Legal AI Platforms
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Stanford proved "hallucination-free" legal AI is a marketing lie. Researchers ran 202 legal queries through LexisNexis and Thomson Reuters' legal AI tool platforms and compared them against GPT-4. Every response was manually reviewed by legal experts. Lexis+ AI hallucinated
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GPT-5.5 Spotted on Open Router: New AI Era Begins
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GPT-5.5 spotted on open router. Just a few more hours friends! And a new era awaits
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Multi-Agent LLM Systems Show Diversity Collapse Over Time
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Cool paper on diversity collapse in AI agents. It's a common issue with all the deployed multi-agent systems. New paper shows that multi-agent LLM systems converge on near-identical outputs over time, even across different architectures and different starting prompts. They call