Articles tagged “python”
15 articles
Build the MCP + A2A agent protocol stack from scratch
Wire an MCP server to an A2A agent that delegates tasks and calls tools. TypeScript and Python examples, Streamable HTTP transport, Agent Cards, and auth.
Agentic RAG: from dumb retrieval to self-correcting agents
Your RAG pipeline retrieves wrong documents and nobody catches it. Build a self-correcting agent that grades results, rewrites queries, and knows when to stop.
Graph memory for AI agents: when vector search isn't enough
Build graph memory for AI agents in TypeScript and Python. Extract entities, track relationships over time, and compare Mem0, Zep, and Letta in production.
Voice AI pipeline: STT, LLM, TTS and the 300ms budget
Build a real-time voice pipeline with Pipecat. How STT, LLM, and TTS stream concurrently under a 300ms latency budget, with turn detection and interruptions.
AI Agent Frameworks Compared: Which Ones Ship?
An honest comparison of 9 AI agent frameworks (LangGraph, CrewAI, Vercel AI SDK, Mastra, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Pydantic AI, AutoGen) based on what developers actually ship to production in 2026.
Embeddings Turn Text Into Meaning. Here's the Math and the Code
What embeddings are, how similarity search works under the hood, and how to build a semantic search engine, from cosine similarity math to production vector databases.
Function Calling: Build a Multi-Tool AI Agent from Scratch
Build a multi-tool AI agent from scratch using function calling across OpenAI, Anthropic, and Google. Runnable TypeScript and Python code, validation with Zod and Pydantic, and production hardening patterns.
Your RAG Pipeline Is Answering the Wrong Question
Naive RAG scores 42% on multi-hop questions. Agentic RAG hits 94.5%. The difference: letting the agent decide what to retrieve, when, and whether the results are good enough. Build both in TypeScript and Python.
Context Engineering Is What Your Agent Actually Needs
Prompt engineering hits a wall with production AI agents. Context engineering fixes it. Build a full context pipeline with memory, RAG, history compression, and tool resolution.
Fine-Tune a 7B Model for $1,500 (Not $50,000)
Full fine-tuning costs $50K in H100s. QLoRA on an RTX 4090 costs $1,500. Learn how LoRA and QLoRA let you train only 0.1-1% of parameters with nearly identical results, with working code for fine-tuning models that understand your agent's tool schemas.
A 1B Model Just Matched the 70B. Here's How.
How to distill frontier LLMs into small, cheap models that retain 98% accuracy on agent tasks. The teacher-student pattern, NVIDIA's data flywheel, and the Plan-and-Execute architecture that cuts agent costs by 90%.
Claude 4.6 broke our production agent in two hours — here's what's worth the migration
A practical developer guide to Claude 4.6 — adaptive thinking, 1M context, compaction API, tool search, and structured outputs. Real code examples in TypeScript and Python for building production AI agents.
Como evaluar agentes de IA: construye un framework de evaluacion desde cero
Construye un framework funcional de evaluacion de agentes de IA en TypeScript y Python. Cubre LLM-as-judge, puntuacion por rubrica, pruebas de regresion e integracion con CI.
MCP Explicado: Construye Tu Primer Servidor MCP en TypeScript y Python
Construye un servidor MCP funcional desde cero en TypeScript y Python. Tutorial práctico que cubre tools, resources, transports y testing.
RAG desde Cero: Construye un Pipeline de Generación Aumentada por Recuperación
Construye un pipeline RAG funcional desde cero en TypeScript y Python. Cubre embeddings, chunking, búsqueda vectorial y generación con código real y ejecutable.
Aprende IA Agéntica
Una lección por semana: técnicas prácticas para construir, probar y lanzar agentes IA. Desde ingeniería de prompts hasta monitoreo en producción. Aprende haciendo.
