$AI: Anthropic <=$15B= Nvidia, Microsoft

more circular money deals... what can go wrong?

Nvidia, Microsoft invest $15 billion in AI startup Anthropic

Nvidia committed up to $10 billion

Microsoft, which owns 27 percent of Anthropic rival OpenAI,
pledged up to $5 billion to the maker of Claude AI models.

The deal was part of a sweeping agreement that saw Anthropic commit to purchasing $30 billion in Microsoft's cloud computing capacity and adopt the latest versions of Nvidia's chip technology.

more headlines: 

Nvidia, Microsoft Investment Raises Anthropic's Valuation to $350 Billion

Nvidia, Microsoft deal takes 'circular' financing to entirely new level

Anthropic to use Google's AI chips worth tens of billions to train Claude chatbot

OpenAI API Agents SDK

OpenAI: a-practical-guide-to-building-agents.pdf

openai/openai-agents-js: A lightweight, powerful framework for multi-agent workflows and voice agents @GitHub

openai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows @GitHub

openai/agents.md: AGENTS.md — a simple, open format for guiding coding agents

Agents - OpenAI Agents SDK

The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives:

• Agents, which are LLMs equipped with instructions and tools
• Handoffs, which allow agents to delegate to other agents for specific tasks
• Guardrails, which enable validation of agent inputs and outputs
• Sessions, which automatically maintains conversation history across agent runs

 Examples - OpenAI Agents SDK python

openai-agents-js/examples at main · openai/openai-agents-js JavaScript

•  Multi-Agent Workflows: Compose and orchestrate multiple agents in a single workflow.
•  Tool Integration: Seamlessly call tools/functions from within agent responses.
•  Handoffs: Transfer control between agents dynamically during a run.
•  Structured Outputs: Support for both plain text and schema-validated structured outputs.
•  Streaming Responses: Stream agent outputs and events in real time.
•  Tracing & Debugging: Built-in tracing for visualizing and debugging agent runs.
•  Guardrails: Input and output validation for safety and reliability.
•  Parallelization: Run agents or tool calls in parallel and aggregate results.
•  Human-in-the-Loop: Integrate human approval or intervention into workflows.
•  Realtime Voice Agents: Build realtime voice agents using WebRTC or WebSockets
•  Local MCP Server Support: Give an Agent access to a locally running MCP server to provide tools
•  Separate optimized browser package: Dedicated package meant to run in the browser for Realtime agents.
•  Broader model support: Use non-OpenAI models through the Vercel AI SDK adapter
•  Long running functions: Suspend an agent loop to execute a long-running function and revive it later 
•  Voice pipeline: Chain text-based agents using speech-to-text and text-to-speech into a voice agent 

OpenAI @GitHub search "Agents"

OpenAI Agents SDK Tutorial: Building AI Systems That Take Action | DataCamp

Tracing - OpenAI Agents SDK