Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search.
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Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomalies and trigger the appropriate response. Late one night, it flags an elevated anomaly score across a production cluster, 0.87, above its defined threshold of 0.75. The agent is within its permission boundaries. It has access to the rollback service. So it uses it.
A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.
For decades, the IQ test has been one of the most familiar — and most contested — yardsticks for human intelligence. Now, a startup project called AI IQ is applying the same metaphor to artificial intelligence, assigning estimated intelligence quotients to more than 50 of the world's most powerful language models and plotting them on a standard bell curve.
For the first time since the AI race began, more American businesses are paying for Anthropic's Claude than for OpenAI's ChatGPT.
For AI systems to keep improving in knowledge work, they need either a reliable mechanism for autonomous self-improvement or human evaluators capable of catching errors and generating high-quality feedback. The industry has invested enormously in the first. It's giving almost no thought to what's happening to the second.
For decades, the IQ test has been one of the most familiar — and most contested — yardsticks for human intelligence. Now, a startup project called AI IQ is applying the same metaphor to artificial intelligence, assigning estimated intelligence quotients to more than 50 of the world's most powerful language models and plotting them on a standard bell curve.
For the first time since the AI race began, more American businesses are paying for Anthropic's Claude than for OpenAI's ChatGPT.
AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.
So when Amodei took the stage at Anthropic's Code with Claude developer conference on Wednesday and offered a genuinely striking piece of financial candor, the room paid attention.
