Differences between Bagging and Boosting In machine learning, Bagging and Boosting are two popular ensemble learning techniques used to improve the performance of models. Ensemble learning combines multiple weak models (often called base learners) to create a stronger predictive model. While both methods aim to enhance accuracy and reduce errors, they work in different ways. […]
What is LLM Poisoning? LLM Poisoning ( (Large Language Model) ) is when someone intentionally feeds misleading, false, or harmful data into the training process of an AI model. This “poisons” the model, causing it to generate incorrect, biased, or dangerous responses—like teaching a parrot lies so it repeats them. How Does It Work? Imagine […]
What is DevSecOps? DevSecOps is the philosophy of integrating security practices within the DevOps lifecycle. It means “Security as Code”—making security an integral, automated, and continuous part of software development rather than an afterthought. By embedding security into every phase of the Software Development Life Cycle (SDLC), teams can identify vulnerabilities early, improve overall security […]
What is NGINX? NGINX is a free, open-source web server software that serves as a high-performance web server, reverse proxy, load balancer, and HTTP cache. It was originally developed by Igor Sysoev in 2004 to address the C10K problem—handling tens of thousands of concurrent connections efficiently. C10K problem was a term used to describe the […]
Difference between Jenkins vs Jenkins X In this tutorial, let’s look at some key differences between Jenkins and Jenkins X tools. Jenkins Traditional CI/CD Tool: Jenkins is a widely used open-source automation server primarily for continuous integration (CI) and continuous delivery (CD). Flexibility: Jenkins is highly customizable and can be extended with plugins to meet […]
What is Fuzz Testing? Fuzz Testing (or Fuzzing) is an automated software testing technique that bombards systems with invalid, random, or malformed inputs to uncover crashes, vulnerabilities, or unexpected behavior. Features of Fuzzing Aspect Description Goal Find security flaws, crashes, memory leaks, or edge-case failures Input Generation Random/malformed data (strings, binaries, adversarial prompts) Scope APIs, […]