I do not think it will shock anyone to learn that big tech is aggressively pushing AI products. But the extent to which they have done so might. The sheer ubiquity of AI means that we take for ground the countless ways, many invisible, that these products and features are foisted on us—and how Silicon Valley companies have systematically designed and deployed AI products onto their existing platforms in an effort to accelerate adoption.

The role of the IC (Individual Contributor) is evolving fast—and AI is accelerating the shift. As AI tools become deeply integrated into development workflows, many engineers find themselves stepping into responsibilities once reserved for engineering managers. This isn’t a hypothetical trend—it’s already happening in high-performing teams.

REST wasn’t designed for modern APIs. It was a retrospective description of how early web browsers talked to HTTP servers — formalized by Roy Fielding to finish his PhD. It explained how the Web worked in the 90s, not how your API should work in 2025.

What we do today should probably be called JOHUR instead (JSON over HTTP, URL-based Routing).

Open-source software tools continue to increase in popularity because of the multiple advantages they provide including lower upfront software and hardware costs, lower total-cost-of-ownership, lack of vendor lock-in, simpler license management and support from active communities.

In the following slides, as part of the CRN 2024 Year In Review project, we take a look at some of the most popular open-source software products that have caught our attention this year.

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence?

You’re likely reading this text in a browser. Press Ctrl+F (⌘+F on macOS) and search for the word "text" on this page. The browser will instantly show you how many times the word appears. Even in texts hundreds of times longer than this page, browsers can quickly find the desired substring. Today, we’ll look at the algorithms that make this possible.

I can’t get through a zoom call, a conference talk, or an afternoon scroll through LinkedIn without hearing about vectors. Do you feel like the term vector is everywhere this year? It is. Vector actually means several different things and it's confusing. Vector means AI data, GIS locations, digital graphics, and a type of query optimization, and more. The terms and uses are related, sure. They all stem from the same original concept. However their practical applications are quite different.

So “Vector” is my choice for this year’s name collision of the year.

Recently I realize I've accumulated quite a few packages and wanted to do some cleaning and organizing.

These are the ones I kept and find useful.

The Story of Chaos Theory and Some Fun Facts About the Scientists.

With the advent of Llama 2, running strong LLMs locally has become more and more a reality. Its accuracy approaches OpenAI's GPT-3.5, which serves well for many use cases.

In this article, we will explore how we can use Llama2 for Topic Modeling without the need to pass every single document to the model. Instead, we are going to leverage BERTopic, a modular topic modeling technique that can use any LLM for fine-tuning topic representations.

Have you noticed that Git is so integral to working with code that people hardly ever include it in their tech stack or on their CV at all? The assumption is you know it already, or at least enough to get by, but do you?

Git is a Version Control System (VCS). The ubiquitous technology that enables us to store, change, and collaborate on code with others.

Many Linux users have experienced a lasting sense of accomplishment after composing a particularly clever command that achieves multiple actions in just one line or that manages to do in one line what usually takes 10 clicks and as many windows in a graphical user interface (GUI). Aside from being the stuff of legend, one-liners are great examples of why the terminal is considered to be such a powerful tool.

An LLM is no black box but an ML model (based on Neural Networks) that predicts the ‘next’ token given a sequence of previously predicted tokens and input prompt.
How is it able to get the context of the input? Using multi-head attention helps in focusing on important words compared to other tokens in the input sentence. If you’re interested in mathematics, you can read the below blog.