Big(O) Notation Is About Scalability, Not Speed
When comparing algorithms based on execution time, computer scientists use Big(O) notation. Big(O) describes how an algorithm's running time grows as the size of the input grows.
2026
Beyond 3D: Extending the Mathematics We Already Know
We live in a three-dimensional world, so most of the mathematics we learn is limited to 3D. However, the same mathematical ideas can be extended to any number of dimensions.
How Machines Learn: Direct Learning vs. Iterative Optimization
Machine learning models generally learn from data in one of two ways: Direct (Analytical) Learning or Iterative Optimization. Understanding these two approaches provides a useful big-picture view of how many machine learning algorithms are related.
Unsupervised Learning: From Recognition to Discovery
At a high level, supervised learning focuses on recognizing previously seen information, while unsupervised learning focuses on discovering previously unseen information.
Evolution Towards AI: From Rules to Goals
This blog is about three ways computers solve problems: by following rules, by learning patterns from data, and by generating answers that may not have a clear right or wrong.
WebTransport- An Introduction
- In this post, I will introduce WebTransport and explain it in comparison with existing alternatives. As a relatively new protocol, it offers useful capabilities for modern real-time applications, along with its own set of trade-offs.
Operating System Components: A Conceptual Overview
- This writing of mine presents a conceptual overview of operating system components, including the kernel, user interface, application interface, and supporting mechanisms.
Vanialla JavaScript vs Frameworks
- This writing compares frontend frameworks such as Angular, React, and Vue.js with traditional web pages built using pure JavaScript (Vanilla JS).
AI vs Conventional Algorithms
There are two broad types of algorithms used in software: conventional algorithms and AI algorithms. This section gives a brief comparison between the newer, increasingly popular AI-based algorithms and the older, conventional approach.
Motivation Is Cheap. Planning Is Hard
- In classrooms, parenting advice, motivational talks, and especially across the world of self-help content, one phrase appears with almost mechanical repetition: You can do it. But there is a problem.