At its core, software development has always been about a simple cycle:
Write > Review > Deploy > Monitor
You ship code. The code goes through reviews. You deploy it. Then you monitor production to make sure everything stays stable. When you're working in a team, code reviews are non-negotiable. They're one of the best ways to maintain quality, catch issues early, and ensure everyone is building toward the same standards. But we're now living in a very different era. An era where a developer or, honestly, a random dude with an idea can build products through conversation.
Hehe, interesting times!
From GitHub Copilot to "Build Me an App"
I remember when developers were almost shy about admitting they used coding assistants. Back in the early GitHub Copilot days, saying "I use AI to help me write code" sometimes felt like admitting you were taking shortcuts.
Fast forward to today, and we've reached a point where people can write code using natural language. I genuinely never thought we'd get here this quickly. And since then, it's been one hell of a ride.
Why 7 Out of 10 Developers Ship AI-Assisted Code
Today, most developers are shipping AI-assisted code. Not because they're bad developers. Not because they're lazy. Because it makes them faster.
AI helps close the gap between domain expertise and implementation.
You can go from "I've never heard of this technology" to "I can build something with it" within 30 minutes to an hour of focused work.
Traditionally, learning a new framework, library, or service required days of reading documentation and experimenting.
Now, AI can accelerate that process dramatically.
That's the magic.
The Part Nobody Talks About
The problem is that while AI is incredibly good at generating code, it's not always good at generating good code.
Recently, I built a product using Antigravity, and while it helped me move fast, I noticed several issues almost immediately:
- Poor code quality
- Weak function modularization
- Security concerns
- Repeated implementation patterns
- Lack of reusable abstractions
The code worked. But "working" and "maintainable" are two very different things.
And that's really the challenge with AI-assisted development today. The volume of code being generated is growing faster than our ability to review it properly.
A lot of the issues aren't outright bugs. They're the subtle things that slowly make a codebase harder to maintain over time. Swallowed exceptions, generic variable names, dead code, unnecessary comments, duplicated logic, unsafe type assertions, and TODO stubs that somehow make it to production.
That's what got me interested in ScanAISlop.
One thing I like about it is that it isn't trying to play detective and tell you whether a human or AI wrote the code.
Instead, it focuses on the stuff that actually matters: bad patterns, weak abstractions, security concerns, unnecessary complexity, and all those little things that make a codebase harder to maintain over time.
The approach is pretty straightforward. No LLM sitting behind the scenes making guesses. The checks are deterministic, so the same code produces the same result every single time.
And because the rules are opinionated, the output tends to be a lot more useful than tools that flag everything and leave you sorting through noise.
You can try it right now on any public GitHub repository.
Simply replace:
github.com/<owner>/<repo>
with:
scanaislop.com/<owner>/<repo>
and you'll get an instant report showing potential quality, security, and maintainability issues across the codebase.
I ran the CLI against one of my own projects, and the results were surprisingly useful. Instead of giving me a vague score, it pointed directly to the kinds of issues that reviewers typically miss when scanning large AI-generated pull requests.
try npx aislop scan to try-out the CLI
The most interesting part?
The CLI is completely open source and MIT licensed. You can inspect the rules, contribute improvements, open issues, or even help shape what good AI-assisted engineering should look like.
Please take a minute to try it out and star the repo: https://github.com/scanaislop/aislop.
As AI continues to generate more code, I think tools like this become increasingly important—not because AI is bad, but because engineering standards shouldn't disappear just because code is easier to produce.
The Real Advantage
This is where engineering fundamentals still matter. Understanding how systems work, knowing how to structure a codebase, understanding design systems, knowing when to abstract a component, and knowing when not to.
These things still separate experienced engineers from people who are simply prompting their way through development.
For example, if I'm using a table across multiple screens, I'd rather create a reusable component once than copy and paste:
across ten different files. AI can absolutely generate the table.
But it takes engineering judgment to recognize that it should become a reusable building block.
And most times, you have to explicitly tell your coding assistant that. AI can generate code. Experience tells you what code should exist in the first place.
The New Skill Isn't Coding Faster
The new skill isn't necessarily writing code faster. It's reviewing code better. It's knowing what should be abstracted. It's spotting security issues. It's identifying AI-generated slop before it reaches production.
Because the future isn't AI versus developers. The future is developers who know how to think, using AI as leverage.
And those developers will always have an edge.
