Deep dives into the technology behind AI voice agents — LLMs, speech-to-text, real-time voice processing, and more.
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How to design APIs for AI agent platforms — comparing REST, GraphQL, and gRPC for agent invocation, streaming responses, tool registration, and multi-agent orchestration.
How to build responsive AI applications using streaming, WebSockets, and SSE, with practical patterns for token streaming, agent status updates, and real-time collaboration.
A practical guide to building production-grade AI pipelines using LangChain and LlamaIndex, covering when to use each framework, architecture patterns, and lessons from real deployments.
How vector databases and semantic search power AI agent memory, RAG systems, and knowledge retrieval with practical guidance on embedding models, indexing, and query strategies.
A technical guide to modern LLM inference optimization techniques — quantization, speculative decoding, KV-cache optimization, continuous batching, and PagedAttention. Make models faster and cheaper.
Design patterns for building a production LLM API gateway — including intelligent rate limiting, semantic caching, provider fallbacks, and request routing for multi-model deployments.
A guide to observability for LLM-powered applications, covering tracing frameworks, key metrics, debugging techniques, and the emerging tooling ecosystem.
A practitioner's comparison of the leading AI coding agents — Cursor, Windsurf, and Claude Code — covering architecture, capabilities, pricing, and which tool fits different workflows.
