A practical guide to integrating an AI writing assistant via API—architecture, prompt design, code samples, safety, evaluation, and performance optimization.
Build a production-grade semantic search with embedding models: data prep, indexing, similarity, hybrid retrieval, re-ranking, evaluation, and scaling.
Clear rules for naming API resources, fields, and events across REST, GraphQL, and gRPC—with examples, pitfalls, and a practical checklist.
Hands-on guide to reliable, secure tool calling for AI agents: architecture, schemas, control loops, error handling, observability, and evaluation.
Compare Copilot, Amazon Q Developer, JetBrains AI, Cody, Cursor, Claude Code, Tabnine, Replit, Continue, and Aider for 2026.
Build a production-ready LangGraph multi-agent workflow with a supervisor, tools, checkpointing, and streaming—step-by-step with tested Python code.
