8- Claude Code sur Bedrock : sans clé API Anthropic export CLAUDE_CODE_USE_BEDROCK=1 Cette seule variable d'environnement permet à Claude Code de fonctionner via le compte AWS existant. – Authentification gérée côté AWS
– Facturation intégrée dans la facture Bedrock du client
LLMS
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Using Claude Code via AWS Bedrock for API-free Access
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The 3-Element Philosophy for Building AI Agents
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5- La philosophie "3 éléments seulement" de Strands
Pour créer un agent, il faut exactement 3 choses : – Model : le modèle IA utilisé (Claude 3.7 par défaut)
– Tools : les outils mis à sa disposition
– Prompt : les instructions Tout ce que d'autres frameworks exigent -
Amazon Bedrock and Anthropic Integration: Claude Access, Security
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2- What the Amazon Bedrock × Anthropic integration means Amazon Bedrock = AWS's AI model manager Access to the entire Claude family (including the latest models) via a unified API
Built-in safety guardrails by default
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No change expected in graph after xAI gives Colossus 1 to Anthropic
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i dont expect this graph to change in the next month(s) now that xai gave their collossus 1 to anthropic
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AI capability to analyze environmental source code
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I like this comparison but… cars can’t open up and read the source code of their environment… AI can.
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State Space Models in Gallery Updates
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I have models with state space layers in the gallery (e.g., Nemotron 3). Anything since April I missed though?
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Top AI Research Papers of the Week (May 4 – 10)
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The Top AI Papers of the Week (May 4 – 10) – Conductor
– HeavySkill
– Horizon Generalization
– 1,000 Synthetic Computers
– Self-Improving Pretraining
– Coordination as Architecture
– Connect Four AlphaZero from Scratch Read on for more: -
Personification of Claude AI and its medium-term consequences
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The personification of Claude — in name (the only AI with a human one), in training, in Anthropic’s philosophy (see Claude Constitution), in fanfiction (see the Claude cartoons), etc — feels quite consequential in the medium term, for better and for worse.
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Meta: AI verifies code fixes with 93% accuracy without execution
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Meta's AI can now verify code patches with 93% accuracy without running them. LLMs read code the way you skim a book. They recognize familiar patterns and fill in the gaps based on learned knowledge. A new paper tests whether AI models can validate code changes without executing the software.
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RLHF vs RLVR: From Likable to Useful
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tldr: RLHF taught models to be likable. RLVR is teaching them to be useful.