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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Learn to use Python context managers for reliable resource management in AI applications including API client lifecycles, database connections, and async session handling.
Compare Python dataclasses and Pydantic models for AI agent state management including performance benchmarks, validation capabilities, serialization, and practical use cases.
Master Python generators and async iterators for building efficient streaming AI response pipelines with memory-efficient processing, backpressure handling, and real-time output.
Learn to define robust agent interfaces using Python's abc module with abstract methods, interface contracts, plugin patterns, and registration hooks for extensible AI architectures.
Configure production-grade logging for AI applications using structlog and loguru with structured JSON output, context binding, correlation IDs, and cost-aware filtering.
Master Python dependency management for AI projects using Poetry, uv, and virtual environments with reproducible builds, lock files, and Docker integration strategies.
Learn to identify and fix performance bottlenecks in AI applications using cProfile, py-spy, memory profiling, and optimization strategies for LLM pipelines and data processing.
Master PostgreSQL JSONB for storing variable-structure agent outputs including tool call results, LLM responses, and agent metadata with proper indexing, partial updates, and query optimization.
