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
Design effective AI agent onboarding experiences that set accurate expectations, guide users through their first interaction, and progressively reveal capabilities over time.
Learn how to export AI agent models to ONNX format, optimize them with ONNX Runtime, and deploy cross-platform for consistent inference performance on any hardware.
Select, deploy, and optimize open-source embedding models for RAG-powered agents. Compare Sentence-Transformers, BGE, and E5 models with benchmarks, fine-tuning strategies, and deployment patterns.
Navigate open-source licensing for AI agent projects including license selection, model cards, proper attribution, and building ethical community guidelines for agent development.
The gap between open-weight and proprietary LLMs has narrowed dramatically. Compare licensing, customization, performance, and total cost of ownership to choose the right model strategy for your organization.
Learn how to mount OpenAI Agents SDK agents inside a FastAPI web server with session management, concurrent user handling, streaming responses, and production-ready error handling.
Master the lifecycle hook system in OpenAI Agents SDK to add custom logging, metrics collection, request modification, and observability at every stage of agent execution.
Optimize your OpenAI Agents SDK deployments for production with techniques for connection reuse, prompt compression, tool result caching, parallel tool execution, and token budget management.
Learn how to securely manage OpenAI API keys using environment variables, key rotation, organization and project keys, proxy patterns, and secrets management.
