Andrej Karpathy, the influential 39-year-old Slovak-Canadian AI researcher and one of the original 11 co-founders of OpenAI, and former head of Tesla’s AI division, announced on Tuesday, May 19 that he’s joining rival lab Anthropic. As Karpathy posted from his account on the social network X: “Personal update: I’ve joined Anthropic. I think the next …
OpenAI co-founder Andrej Karpathy announces he’s joining Anthropic Read More »
Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents failing not because the models are wrong, but because the data underneath them is scattered, stale and structured for humans rather …
Four supply-chain incidents hit OpenAI, Anthropic and Meta in 50 days: three adversary-driven attacks and one self-inflicted packaging failure. None targeted the model, and all four exposed the same gap: release pipelines, dependency hooks, CI runners, and packaging gates that no system card, AISI evaluation, or Gray Swan red-team exercise has ever scoped. On May …
Enterprises building and deploying agents have a problem: it’s taking their engineers too long to find out that an agent made a mistake, and the loop has continued to perpetuate, especially without a human at every step. LangSmith, the monitoring and evaluation platform from LangChain, launched a new capability in public beta that could make …
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. However, for enterprise domains characterized by highly interconnected data (supply chain, …
Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production Read More »
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. I’d argue that we need to treat …
One of the key challenges of current multi-agent AI systems is that they communicate by generating and sharing text sequences, which introduces latency, drives up token costs, and makes it difficult to train the entire system as a cohesive unit. To overcome this challenge, researchers at University of Illinois Urbana-Champaign and Stanford University developed RecursiveMAS, …
How RecursiveMAS speeds up multi-agent inference by 2.4x and reduces token usage by 75% Read More »
The company formerly known as Intercom just did something that no major customer service platform has attempted at scale: it built an AI agent whose sole job is to manage another AI agent. Fin Operator, announced Thursday at a live event in San Francisco, is a new AI-powered system designed specifically for the back-office teams …
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run. For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus …
Claude’s next enterprise battle is not models: it’s the agent control plane Read More »
Observability startup Raindrop AI’s new open source, MIT Licensed “Workshop” tool, launched today, gives developers something that they’ve likely wanted, perhaps subconsciously, since the agentic AI era kicked off in earnest last year: a local debugger and evaluation tool specifically designed for AI agents, allowing devs to see all the traces of what their agent …