Agentic AI
A comprehensive technical breakdown of agentic AI architecture into seven distinct layers, from memory systems to orchestration, providing clarity in an often overhyped field with practical implementation insights.
AI Development
A technical guide to developing functional AI applications using Python, FastAPI, and LangChain. Learn the essential frameworks and patterns for creating reliable AI-powered tools beyond proof-of-concept demos.
AI Agents
Model Context Protocol (MCP) offers a standardized approach to building modular AI agents. This technical framework enables developers to create flexible, interconnected agent systems with reusable components and improved maintainability.
LangChain
A comprehensive technical guide to LangChain, the framework enabling developers to build sophisticated LLM applications with chaining, memory, and retrieval capabilities essential for modern AI systems.
Agentic AI
Learn to build a modular agentic AI system using a planner-tools-executor-summarizer architecture with FastAPI and OpenRouter. This technical guide walks through creating autonomous AI assistants with multi-step reasoning capabilities.
LLM Architecture
Comprehensive technical analysis of retrieval-augmented generation and fine-tuning strategies for LLMs, exploring when to use each approach, their technical trade-offs, and emerging hybrid architectures that combine both methodologies.
Agentic AI
Learn how to build ReAct (Reasoning and Acting) agents using LangGraph, exploring the architecture that enables AI systems to reason about tasks and execute actions autonomously through iterative cycles.