Last updated: 2025-06-17
I really wanted to love AI coding tools. The promise was compelling: automate the boring stuff, boost productivity, maybe even learn some new tricks along the way. But after months of trying various tools, I've come to a pretty disappointing conclusion: they just don't work for me.
I'm not alone in this. A recent Hacker News discussion revealed that many developers are having similar experiences. The gap between the marketing hype and actual utility is pretty significant.
The reality is messier than the demos suggest. Sure, some people have great experiences, but for many of us, these tools create more problems than they solve. The most common complaint? They generate code that looks right but breaks in subtle ways, meaning you spend more time debugging AI-generated code than you would have spent writing it yourself from scratch.
Here's the thing: AI models are really good at syntax but terrible at understanding what you're actually trying to build. Real software development isn't just about getting the code to compile—you need to understand business logic, user needs, system constraints, and a dozen other factors. AI tools miss all of this context, so they give you code that's technically correct but completely useless for your specific situation.
Want to know what's ironic? Tools that are supposed to save you time require hours of setup and configuration just to get them working with your existing codebase. I've spent entire afternoons trying to get an AI assistant to understand my project structure, only to give up and write the code myself in a fraction of the time.
AI tools are great at generating lots of code. The problem is, most of it is garbage. You'll get five different implementations of the same function, and good luck figuring out which one actually works in your use case. It's like having an enthusiastic junior developer who writes tons of code but never tests any of it.
After years of coding, you develop instincts about what will work and what won't. You can smell a potential performance issue from a mile away, or sense when a particular approach will cause maintenance headaches down the road. AI tools don't have this intuition—they can't anticipate edge cases or think about how code will evolve over time.
The Hacker News comments were full of war stories. Some developers shared moments where AI actually helped them solve a tricky problem. But for every success story, there were two horror stories—critical bugs that slipped into production, missed deadlines because AI-generated code didn't work as expected, and frustrated developers who felt like they were babysitting an unreliable assistant.
Some developers in the thread had better luck when they stopped expecting AI to replace their thinking and started treating it more like a really advanced autocomplete. Instead of asking it to solve complex problems, they used it for boilerplate code and simple transformations. That's probably the right mindset—if you can find the patience for it.
Look, maybe AI coding tools will get better. Maybe they'll eventually understand context and stop generating code that looks right but fails in production. But right now, for most real-world development work, they're more trouble than they're worth.
I'm not giving up on them entirely—I'll keep trying new tools as they come out. But I've stopped expecting them to revolutionize my workflow anytime soon. For now, my IDE's autocomplete and a good understanding of my codebase are still more reliable than any AI assistant.
Your mileage may vary, but don't feel bad if these tools don't work for you either. You're definitely not alone.