Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build function-calling agents with Claude's native tool use. Learn how to define tools with JSON schemas, handle tool_use and tool_result blocks, and orchestrate multi-step tool conversations.
Master Claude's tool calling patterns including sequential chains, parallel execution, nested tool calls, forced tool use, and auto vs any mode for complex agent orchestration.
Build multi-modal agents that process images, PDFs, and diagrams using Claude's vision capabilities. Learn how to send image data via the API, analyze documents, and combine vision with tool use.
Compare Claude and GPT-4 for building AI agents. Understand API differences, capability trade-offs, cost structures, and get a practical migration guide for switching between platforms.
AI-enhanced climate modeling enables kilometer-scale Earth system simulations that were computationally impossible five years ago. Discover how generative AI transforms climate projection accuracy.
Compare cloud, on-premises, and hybrid approaches for deploying AI infrastructure. Learn the cost models, security trade-offs, and strategic factors driving enterprise decisions.
AI coastal flood prediction models map inundation risk at meter-scale resolution with 90% accuracy. Learn how machine learning improves flood mapping, erosion forecasting, and sea-level rise planning.
Establish effective team workflows for collaborative prompt development. Learn review processes, approval gates, documentation standards, and shared library patterns that scale across engineering teams.
A practical comparison of the major foundation models — GPT-4, Claude, Gemini, Llama, and Mistral — covering capabilities, pricing, context windows, and guidance on when to use each.
