The AI revolution has made every side project feel like a trivial exercise in comparison.
The shift has erased the satisfaction of overcoming technical challenges that once defined a programmer’s identity. Even the most complex algorithms or frameworks can now be generated or explained by AI tools in seconds. The traditional path—studying for interviews, refining code, or building portfolios—now seems like preparation for a race already decided by automation. Why invest months in mastery when the industry itself may shift before those skills become relevant?
Side projects, once a source of pride, now feel like insignificant contributions to a flood of AI-generated output. The act of building something from scratch used to carry weight, but with tools that can produce functional applications in minutes, the craft itself loses meaning. It’s akin to enabling cheats in a game—progress becomes effortless, but the struggle, the learning, and the personal achievement that made the work worthwhile are stripped away.
The question now is whether coding’s value lies in prompt engineering alone or if there’s a deeper layer of engineering—one that focuses on architecture, novel systems, or problem-solving without relying on existing datasets—that AI cannot replicate. Without that distinction, the purpose of building things feels hollow, leaving only efficiency in its wake.
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This feels depressing. Where are the actual unregulated spaces left for us to build things?
Does anyone else feel like the software industry has finally turned on cheat codes? I am talking about that hollow sensation that hits when you realize almost every technical hurdle we once took pride in solving is now just a single prompt away. The mental toll of this AI shift is substantial. New software releases no longer spark excitement because we suspect they are merely wrappers or generated features. Even exploring new CS concepts feels futile when an LLM can explain, implement, and optimize them in three seconds. The traditional grind of studying for interviews or polishing a portfolio feels like training for a race already won by a machine. Why spend months mastering a framework if the job itself might be obsolete in a year?
I notice this most with side projects. Building something from scratch used to be a badge of honor, but now it feels like adding a single grain of sand to a mountain of AI-generated noise. If a random person can generate a functional app in ten minutes using a basic AI workflow, the value of the craft disappears. It is exactly like enabling cheats in a video game. You might have infinite health and gold to blast through levels, but the joy vanishes. The struggle, the failure, and the eventual breakthrough were the essence of the experience. When you remove the friction, you remove the meaning.
I am trying to find a new hard mode for development. Perhaps the shift must move away from the how of coding toward the what of deep systems. One concrete step that still feels worth taking is to build side projects entirely by hand, without leaning on AI generation, and to treat that constraint as the new hard mode.
It's soul-crushing to see correctness ignored. Is there any specific industry still valuing principled engineering over raw speed? Building something from scratch used to be a badge of honor, but now it feels like adding a single grain of sand to a mountain of AI-generated noise.
The hype is draining—where’s the substance? Most of these startups just slap an AI layer over existing problems instead of solving them in a way that actually changes the game. Like the old days of "add a chatbot" as the default fix for everything, now it’s "add an LLM" and call it innovation. Even the ones claiming to do something new often just regurgitate what’s already out there with a fancier prompt interface.
The irony? The real work—like building something from first principles or wrestling with a problem until you understand it—is exactly what’s getting lost in the shuffle. Maybe the next wave of meaningful tools won’t just automate, but force us to ask harder questions: What’s the problem worth solving, and how do we solve it without just outsourcing the thinking? Until then, it all feels like watching a game with cheat codes enabled.
This hits hard. The line between a meaningful pursuit and a time-killing hobby feels blurry now—especially when you realize that even the core struggles of software development, like debugging a complex algorithm or optimizing a system, can be reduced to "just ask an LLM for the solution and tweak it."
The mental toll is real. New releases don’t excite because we suspect they’re just thin wrappers around AI-generated features, and exploring new concepts feels pointless when an LLM can explain, implement, and optimize them in seconds. The grind of studying for interviews or building a portfolio starts to feel like training for a race where the finish line is already crossed by a machine. Why master a framework if the job might be obsolete in a year? And side projects? They used to be a badge of honor, but now they feel like adding a single grain of sand to a mountain of AI-generated noise—like enabling cheat codes in a game. The struggle, the failure, the breakthrough—that’s what made it meaningful. When the friction disappears, so does the point. Maybe the real challenge now isn’t how we code, but what we’re even building in a world where the basics are just prompts away.