Retrieval-Augmented Generation (RAG) is critical for modern AI architecture, serving as an essential framework for building ...

Abstract: In the fast-changing world of cybersecurity, both professionals and learners struggle to find reliable tools and learn how to use them properly. This project seeks to fill that gap by ...

A new “semi-formal reasoning” approach forces AI models to trace code paths and justify conclusions, improving accuracy while ...

Test automation has come a long way from static scripts and rigid frameworks. Today, the focus is shifting toward intelligent, adaptive systems that can recover from failures and optimize themselves.

This project provides a robust and flexible UI automation framework built with Python, Selenium, and Pytest. It implements a Page Object Model (POM) for maintainability, custom logging for detailed ...

Abstract: The software supply chain has become a critical attack vector for adversaries aiming to infiltrate software development workflows by injecting malicious code into third-party packages and ...

Software quality assurance is facing a growing efficiency crisis. Traditional automation frameworks often collapse under constant maintenance and are plagued by test failures that delay critical ...

In this tutorial, we explore how to integrate Microsoft AutoGen with Google’s free Gemini API using LiteLLM, enabling us to build a powerful, multi-agent conversational AI framework that runs ...

Create a robust, reusable template repository that serves as a framework for building and publishing tutorials with Google Codelabs, supporting Coding Agent automation. This template should enable ...