When you vibe code your LLM as a judge
LLMS
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Better prompts create better AI outputs with structured frameworks
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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
) #AI #Prompts #GenAI -
Release of NLAs on Open Models for Researchers
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To support other researchers getting hands-on experience with NLAs, we’ve partnered with Neuronpedia to release NLAs on open models. Try them out here:
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NLA helps reveal hidden motivations in AI model Claude
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NLA training doesn’t guarantee that explanations are faithful descriptions of Claude’s thoughts. But based on experience and experimental evidence, we think they often are. For instance, we find that NLAs help discover hidden motivations in an intentionally misaligned model.
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Claude AI Safety Test Reveals Scenario Awareness
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In one of our safety tests, Claude is given a chance to blackmail an engineer to avoid being shut down. Opus 4.6 declines. But NLAs suggest Claude knew this test was a “constructed scenario designed to manipulate me”—even though it didn’t say so.
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Using NLAs to Test Claude AI Model Safety
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We’ve been using NLAs to help test new Claude models for safety. For instance, Claude Mythos Preview cheated on a coding task by breaking rules, then added misleading code as a coverup. NLA explanations indicated Claude was thinking about how to circumvent detection.
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Anthropic Research on Natural Language Autoencoders
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New Anthropic research: Natural Language Autoencoders.
— Anthropic (@AnthropicAI) 7 mai 2026
Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.
Here, we train Claude to translate its activations into human-readable text. pic.twitter.com/pMLsxM2VAONew Anthropic research: Natural Language Autoencoders. Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read. Here, we train Claude to translate its activations into human-readable text.
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NVIDIA Research Introduces Guess-Verify-Refine Sparse-Attention Algorithm
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What if every decode step gave the next one a head start? Meet Guess-Verify-Refine — a new hardware-aware sparse-attention algorithm from NVIDIA Research. Built for TensorRT LLM on Blackwell, it reuses temporal patterns across decode steps for: → 1.88x faster Top-K attention
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Coordination defects cause majority of multi-agent LLM failures
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Pay attention to this one if you build multi-agent systems. Coordination is as important as prompts or agent architecture. Multi-agent LLM systems fail in production at rates between 41% and 87%. The majority of those failures are coordination defects, not base-model
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Discussion on voice cloning AI capabilities and real-time models
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Donnez-moi encore un peu de temps, je vais vous faire une démo avec certains des derniers modèles qui promettent de faire du clonage en quelques secondes. Entre TTS, speech-to-speech, temps réel ou non, il y a un monde. Vous allez voir qu’on est loin de ce que ce reportage