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
Learn how to implement optimistic updates in AI agent chat interfaces to provide instant feedback, handle rollbacks on failure, and manage loading states for the best user experience.
Learn how to minimize tool call overhead in AI agents through batch execution, parallel tool calls, result prefetching, connection pooling, and smart retry strategies for external APIs.
Master the three core techniques for reducing AI model size for edge deployment — pruning, knowledge distillation, and quantization — with practical code examples and quality preservation strategies.
Design and implement multi-server MCP architectures where agents connect to multiple tool providers simultaneously, with namespace management, conflict resolution, and performance optimization.
Learn how to build an orchestrator agent that breaks complex tasks into subtasks, delegates to specialist agents, aggregates results, and delivers a unified response using the OpenAI Agents SDK.
Build an AI-powered outbound calling system for appointment reminders and follow-ups. Covers call scheduling, personalization, voicemail detection, and TCPA compliance for automated outbound calls.
Master techniques for extracting structured data from LLMs — JSON mode, schema enforcement, parsing strategies, and robust handling of malformed responses in production systems.
Build output scanning systems that detect PII leaks, toxic content, format violations, and off-topic responses before they reach your users, with practical Python implementations for each guardrail type.
Implement fan-out fan-in patterns for AI agents to distribute work across parallel sub-tasks, aggregate results, handle partial failures gracefully, and enforce timeouts on straggler tasks.
