Dive deep into how GitHub Copilot’s autonomous agent actually works. Explore the technical mechanics of code review automation, test generation, bug fixing, and specification implementation with practical workflows.
Category: AI
GitHub Copilot’s Coding Agent: From Pair Programmer to Autonomous Teammate
Discover how GitHub Copilot has evolved from a simple pair programmer into a fully autonomous coding agent. Explore the shift in developer roles and why this transformation matters for modern software teams.
Building Your First MCP Server with Node.js
Learn how to build a simple MCP server with Node.js. This beginner-friendly guide walks you through creating, testing, and running your first MCP server using the MCP Inspector.
Advanced MediaPipe: Custom Models, Training, and Extending the Framework
Master advanced MediaPipe techniques including custom model training, enterprise deployment, and framework extension. Learn to build production-scale computer vision systems with monitoring, optimization, and scalability.
Real-Time Sentiment Analysis with Azure Event Grid and OpenAI – Part 2: Azure OpenAI Integration and AI Processing
Welcome back to our real-time sentiment analysis series! In Part 1, we established the event-driven architecture foundation. Now, let’s dive deep into the AI processing
Azure OpenAI Service: Integration Patterns and Best Practices for Enterprise Applications
Learn proven integration patterns and best practices for implementing Azure OpenAI Service in enterprise applications. Covers security, performance optimization, cost management, and common pitfalls to avoid.
CART Algorithm: The Foundation of Interpretable Machine Learning and Decision Trees
CART (Classification and Regression Trees) stands as one of the most interpretable and versatile algorithms in machine learning. Developed by Leo Breiman and colleagues in
Naïve Bayes: The Surprisingly Effective Probabilistic Classifier Behind Spam Filters
Naïve Bayes stands as one of the most elegant and surprisingly effective algorithms in machine learning, despite its “naïve” assumption of feature independence. This probabilistic
k-Nearest Neighbors (kNN): The Intuitive Algorithm That Powers Recommendation Systems
The k-Nearest Neighbors (kNN) algorithm stands as one of the most intuitive and fundamentally simple algorithms in machine learning, yet it remains remarkably effective across
AdaBoost Algorithm: The Revolutionary Ensemble Method That Changed Machine Learning
AdaBoost (Adaptive Boosting) revolutionized machine learning by proving that combining many weak learners could create a remarkably strong classifier. Developed by Yoav Freund and Robert
