Reality check Memes

When AI tools and low-code platforms started promising that anyone could build software in minutes, the tech industry collectively nodded and said "sure, Jan." But someone finally said the quiet part out loud: if coding suddenly became 10x easier without any actual innovation in computer science, maybe—just maybe—the whole thing is smoke and mirrors. It's like watching someone claim they invented a revolutionary diet pill that lets you eat whatever you want, except the pill is just a rebranded multivitamin and aggressive marketing. The real kicker? The industry's been hyping these "revolutionary" tools while senior devs are still debugging the same CSS alignment issues they were fighting in 2015.

The AI hype cycle speedrun, perfectly captured in four stages of clown makeup. Started with the promise that AI would revolutionize everything, got seduced into thinking you could skip fundamentals and just prompt your way to a senior dev salary. Then reality hit: those "free" AI tools either got paywalled harder than Adobe Creative Cloud or started running slower than a nested loop in Python. Now you're sitting there with zero transferable skills, a LinkedIn full of AI buzzwords, and the crushing realization that "prompt engineer" isn't actually a career path. The kicker? While you were vibing, the devs who actually learned their craft are still employed. Turns out you can't Ctrl+Z your way out of not knowing how a for-loop works.

Junior devs are out here acting like ChatGPT just handed them the keys to the kingdom, absolutely BUZZING with excitement about how they can pump out code at the speed of light. Meanwhile, senior devs are sitting there with the emotional range of a funeral director who's seen it all, because they know EXACTLY what comes next: debugging AI-generated spaghetti code at 2 PM on a Friday, explaining to stakeholders why the "faster" code doesn't actually work, and spending three hours untangling logic that would've taken 30 minutes to write properly in the first place. The enthusiasm gap isn't just real—it's a whole Grand Canyon of experience separating "wow, this is amazing!" from "wow, I'm gonna have to fix this later, aren't I?"

So you thought you could just pip install tensorflow and become an ML engineer? Plot twist: Machine Learning ghosted you the moment you walked in because Mathematics was already waiting at the door with linear algebra, calculus, and probability theory ready to have a serious conversation. Turns out you can't just import your way out of understanding gradient descent, eigenvalues, and backpropagation. Mathematics is the possessive partner that ML will never leave, no matter how many Keras tutorials you watch. Sorry buddy, but those neural networks aren't going to optimize themselves without some good old-fashioned derivatives and matrix multiplication. The harsh reality: every ML paper reads like a math textbook had a baby with a programming manual, and if you skipped calculus in college thinking "I'll never need this," well... the universe is laughing at you right now.