AI Is Replacing Entry-Level Programmers — And It’s Reshaping the Tech Job Market
The AI tools are everywhere. GitHub Copilot writes functional code from comments. ChatGPT generates APIs, builds CRUD apps, and explains errors better than many Stack Overflow threads. Low-code platforms like Bubble and Webflow build production-ready tools with minimal developer input.
These platforms don’t replace all coders. But they do replace the kind of work junior developers used to do.
How Facebook and Meta Are Ripping Our Culture to Shreds – And Why Adults Are Letting It Happen
Once upon a time, social media promised us a better world: connections across distances, friendships rekindled, and communities built. Instead, what we’ve gotten is an algorithm-fueled hellscape of rage, isolation, and performative happiness (Lanier, 2018).
And let’s be crystal clear: this isn’t just happening to teens. Adults—you, me, all of us—are neck-deep in this mess.
It’s not enough to know this anymore. It’s time to get angry. It’s time to get loud. And yes, it’s time to admit we’ve become complicit in our own misery.
Why Python is the Best Choice for Big Data Programmers: Simplicity, Scalability, and Powerful Libraries Explained
In the ever-evolving world of big data, where massive datasets are processed, analyzed, and visualized daily, choosing the right programming language is critical for efficiency and success. Enter Python — a language celebrated for its simplicity, scalability, and robust library ecosystem.
From machine learning pipelines to massive data processing frameworks like Apache Spark, Python has solidified its place as the go-to language for big data programmers.
But what exactly makes Python the perfect fit for big data workflows? Let’s dive into the reasons why developers and data scientists continue to swear by Python