Technology

Analysis of Anthropic's Claude Code based on actual usage experience, examining its distinctive strengths and challenges. Proposes practical selection criteria for AI agent tools through comparison with alternatives like Roo Code, focusing on practical value of features like ToDo lists and parallel processing.

This guide explains how to build an MCP server based on the Model Context Protocol. From environment setup to vector search implementation and debugging, it provides a practical guide for freely extending AI agents.

Why has a machine learning engineer who primarily used Python shifted to Next.js for web development? This article reconsiders the 'right tool for the right job' principle from an implementation cost perspective and offers a new viewpoint on technology selection in the AI era.

What is Solomonoff induction that's been trending on X? Exploring the fundamental principle of machine learning that 'compression is generalization' to solve the mystery of unsupervised learning. From theoretical foundations to latest trends, explained from a practical perspective for AI engineers.

A comprehensive analysis of the Agent2Agent (A2A) protocol announced by Google at Cloud Next '25. This guide examines the technical foundations and strategic implications, including integration with MCP, and its impact on corporate AI strategies from a practical perspective.

A comprehensive exploration of Anthropic's Model Context Protocol (MCP), the 'USB-C for AI' that standardizes integration between LLM agents and external tools. Covers technical mechanisms, implementation methods, and industry trends accelerated by OpenAI's participation.

A comprehensive guide to GitHub Copilot Agent Mode in VS Code - covering features, pricing, setup methods, and comparison with other tools. Learn practical strategies to leverage this AI-powered development assistant and explore the future of human-AI collaborative programming.

Explore the revolutionary 'Boomerang Mode' feature in Roo Code and its impact on AI collaborative development. This guide explains how automatic task division and execution management liberates developers from the hidden burden of creating prompts for AI, with practical implementation details.

A comprehensive exploration of multilingual implementation using Next.js App Router. From strategic URL design to complete directory restructuring and language detection, discover the key decisions and implementation approaches that drive successful global content strategies.

A comprehensive guide to MDX implementation with Next.js App Router, detailing optimal patterns for integrating Shiki, Mermaid.js, and KaTeX. Learn practical approaches to bridge the gap between ideal architecture and real-world constraints.

A comprehensive guide to designing and implementing knowledge graphs that express relationships beyond numerical evaluations. Learn practical methods for structuring complex knowledge for LLM-powered applications, with insights gained from developing an investment interview system.

The essential guide to context sharing in AI collaborative development and the practical evolution of Memory Bank technology. Explore how knowledge structuring and abstraction improve efficiency when developing with AIs that reset their memory with each task.

A practical guide to implementing the test trophy strategy in AI collaborative development. Learn why traditional test pyramid approaches can be counterproductive for prototypes, and discover a new approach to test design optimized for the AI era.

A comprehensive guide to probabilistic parameter modeling using Bayesian updates and question generation strategies based on information gain. Learn how to improve LLM-based decision-making quality by explicitly handling uncertainty in your AI applications.

How the 15-minute-per-task development cycle in AI collaboration changes testing tool selection criteria. Based on real-world experience with Jest, RTL, and Playwright, this guide explains the importance of relative execution time and review efficiency in modern frontend testing.