You Must Keep Coding

The future of tech is clear: ChatGPT at the top, actual coding skills below it. By 2025, your ability to craft perfect prompts will apparently outrank your CS degree and cybersecurity expertise. Who needs algorithms when you can just type "write me a secure authentication system with zero vulnerabilities" and call it a day? The hierarchy has spoken—prompt engineering is the new programming. Time to replace your GitHub portfolio with screenshots of your ChatGPT conversations.

Garry Newman just dropped the most poetic take on AI coding tools I've ever heard. The guy who built Garry's Mod basically said relying too heavily on AI for programming is like watching so much adult content that you can't... perform creatively anymore. And honestly? He's not wrong. When you let Copilot or ChatGPT write all your code, your brain stops doing the heavy lifting. You lose that ability to architect solutions from scratch, to think through problems, to actually create instead of just prompting. It's the difference between being a chef and being really good at ordering DoorDash. The comparison is crude but brilliant. Both involve instant gratification that atrophies your natural abilities. Your problem-solving muscles need exercise, not an autocomplete button. Sure, AI tools are useful—but if you can't code without them, you're not a developer. You're a prompt engineer with a dependency problem.

When your CS professor says "implement a sorting algorithm" but you've got an OpenAI API key and zero shame. This dev just created the world's most expensive sorting function by literally asking GPT-4 to be a sorting algorithm! Sure, it works—but imagine burning through API credits to sort [42, 3, 99, 7, 13] when a simple Array.sort() would do the trick. The true innovation here is maximizing both latency AND cost while solving a problem that was figured out decades ago. Congratulations, you've invented O(API) complexity—where the limiting factor is your credit card limit!

So you decided to dive into machine learning, huh? Time to train some neural networks, optimize those hyperparameters, maybe even build the next GPT. But first, let's start with the fundamentals: literal machine learning. Nothing says "cutting-edge AI" quite like mastering a sewing machine from 1952. Because before you can teach a computer to recognize cats, you need to understand the true meaning of threading needles and tension control. It's all about layers, right? Neural networks have layers, fabric has layers—practically the same thing. The best part? Both involve hours of frustration, cryptic error messages (why won't this thread cooperate?!), and the constant feeling that you're one wrong move away from complete disaster. Consider it your initiation into the world of "learning" machines.

So you slapped "AI" in front of your cron jobs and now it's an "agentic workflow"? Genius move. The industry's been doing this rebranding dance for decades—take something that's existed forever, sprinkle some buzzword magic on it, and boom, instant funding round. Your automation scripts didn't suddenly gain consciousness because you called them "agents." They're still just if-else statements with extra steps and a marketing budget. But hey, at least the VCs are impressed.

Ancient Egyptians somehow managed to build architectural marvels without the corporate tech stack we can't seem to function without. No endless Teams meetings where everyone says "can you hear me?" No 57-slide pitch decks explaining the "stone moving paradigm." No AI that promises to optimize workflow but actually just creates more meetings about the AI. Just people dragging massive stones with pure human effort and mathematical precision. The real pyramid scheme was getting stuff done without a Slack channel dedicated to discussing the office snack selection.