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 how to compile AI models to WebAssembly for browser-based agent inference, covering WASM compilation, model loading strategies, browser constraints, and progressive enhancement patterns.
Design a hybrid agent system that runs fast local inference on edge devices for simple tasks and routes complex requests to cloud models, with seamless fallback and synchronization patterns.
Build AI agents that work fully offline using local model caching, request queuing, and intelligent sync strategies that reconcile state when connectivity returns.
Build a complete voice-controlled AI agent on a Raspberry Pi, covering hardware setup, model selection, audio input/output, wake word detection, and tool integration for home automation.
Build client-side AI agents using WebGPU acceleration and the transformers.js library, covering model loading, GPU inference in the browser, performance tuning, and privacy-first agent design.
Build an AI agent that processes IoT sensor data locally for real-time anomaly detection, with intelligent cloud reporting for aggregated insights and alerts.
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.
Learn how to design multimodal AI agent architectures that route inputs across text, image, audio, and video modalities. Covers fusion strategies, modality-specific processors, and unified reasoning pipelines.
