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
Production-grade Python patterns for the Claude API. Covers async patterns, connection management, structured outputs, dependency injection, testing with pytest, and deployment strategies for Python-based AI applications.
Complete guide to using Claude's vision capabilities for image analysis, document processing, and OCR at scale. Covers image formats, multi-image analysis, PDF processing, prompt engineering for vision tasks, and cost optimization.
Google's Project Mariner brings AI agent capabilities directly into Chrome as an extension. How it compares to OpenAI Operator and what it signals about the future of web interaction.
How custom silicon from Groq's LPU and Cerebras' wafer-scale chips are achieving 10-50x faster LLM inference than GPU clusters — and what it means for real-time AI applications.
Programmable voice APIs enable sub-800ms AI agent response times with streaming ASR and TTS. Build human-like conversational AI infrastructure in 2026.
Learn how AI agents are automating content marketing strategy, keyword research, content optimization, and performance tracking for digital marketers worldwide.
Build an autonomous research agent that searches the web, reads documents, synthesizes findings, and produces structured reports. Covers architecture, tool integration, source verification, and iterative deepening strategies.
Comprehensive guide to handling every error type in the Claude API. Covers HTTP status codes, SDK exceptions, retry strategies, circuit breakers, graceful degradation, and production monitoring patterns.
Production-ready TypeScript patterns for the Claude API. Covers SDK setup, type safety, error handling, streaming, middleware patterns, testing strategies, and deployment best practices for TypeScript applications.
