Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Understand how GPTQ, AWQ, and GGUF quantization compress large language models to fit consumer GPUs. Compare quality tradeoffs, memory requirements, and practical deployment strategies.
Design and deploy a fully private AI agent using self-hosted LLMs. Cover infrastructure requirements, model selection, security best practices, and cost comparison with cloud APIs.
Set up LiteLLM to call OpenAI, Anthropic, Mistral, Ollama, and 100+ other providers through a single API. Implement fallbacks, load balancing, and cost tracking for production agents.
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.
Deploy AI agents on edge devices using Google's Gemma and Microsoft's Phi small language models. Cover resource requirements, agent patterns for constrained environments, and mobile deployment strategies.
Design and implement a model router that classifies query complexity and directs agent requests to the most cost-effective model. Build fallback chains, measure routing accuracy, and optimize per-query costs.
Learn how to model complex agent workflows as directed acyclic graphs with dependency resolution, parallel execution of independent tasks, and topological sorting for correct execution order.
Discover how to build resilient long-running agent workflows using durable execution engines like Temporal and Celery, with activity retries, saga patterns, and persistent state across process restarts.
