Stop Vibe Coding, Start Power Coding - How To Write Quality Software Faster With Agentic AI (Without Pissing Off Your Software Engineers)
Date: 2025-07-16 | artificial-intelligence | build | create | power-coding | software-engineering | vibe-coding |
Vibe Coding is the hottest thing to hit software this year. Everyone and their mother is using AI to build their own apps - AI wrappers, games, and personal tools.
Vibe Coding has made building software accessible to millions of people around the world without technical backgrounds. But it also means millions of pieces of software built without solid principles. Stories abound of major security issues and data breaches, AI wiping out days of work, and people bricking their systems because they don't understand how they work.
Vibe Coding has its problems but I frequently see these problems blamed on the tools when I think they're more correctly blamed on how they're used.
In this post I'll present an alternative to vibe coding that allows engineers to leverage AI to write higher quality software faster, all while not pissing off your engineers. It pulls from decades of software engineering best practices and my own experience vibe coding with AI.
That alternative is called Power Coding.
What is Vibe Coding?
Vibe Coding is typically using AI to code for you. You give it prompts and try to direct it to do what you want.
The key distinction I'm making in this post is that the user is trying to get the AI to make the thing for them. It's a passive effort of tossing prompts at the AI and hoping something good comes out.
Prompt, check, prompt, check, prompt, check. They check the outcome of the AI but skip the output - the actual code and systems being built.
Why does Vibe Coding fail?
Vibe Coding generally fails because it's a passive process.
You're putting all your hopes and dreams for this piece onto the AI. But when's the last time you gave a sizable project to a random human with just a few sentences of instructions and got what you wanted out of it? Probably pretty rarely.
The same applies to AI so we must change how we work with them to get good results.
Common Vibe Coding routes to failure:
You don't provide enough direction.
The AI gives you what you asked for but maybe not what you want.
Poorly described features will lead the AI to outputting something that is technically correct but not what you meant. Worse - if you don't understand the domain very well you might think it's what you want but it could be totally wrong or have large structural problems.
It's kind of like asking for a house - this could be a (shed or mansion) or (victorian or modern) or built with (wood or metal) or even for (birds or dogs). Without giving explicit instructions, any of these combinations is "right" but you probably had a specific one in mind.
The AI cannot (yet) read minds so you need to be specific to get what you want.
You miss problems as you build.
Small problems layered onto each other lead to big problems. If you don't spot check and course correct regularly, it's very likely you'll be layering in small bits of inaccuracy at each stage which may end up bricking your system later (a la tech debt).
In the worst case this is like asking for a house but you don't really know anything about houses. The AI might build something that looks like a house but underneath is just a facade (a la Disney World or a filming set). If you don't know enough to at least be able to spot check / test it then it's very possible it will give you something that looks okay on the outside but doesn't actually solve the problem / purpose you had.
An example of this is AIs sometimes using fake data to power the app. So the app looks like it's doing what you want until you realize the data itself isn't real / hooked up to anything.
A more common case though is not that it gets the whole thing wrong but that it gets small things wrong that compound. In this scenario it's more like it starts with the foundation but maybe the foundation isn't quite level or solid or the right size. Then it builds the structural components but some of the support structure is the wrong size or the wrong material or not fastened together the right way. Then it builds the roof but the angles are a little bit wrong so rain water would pool up and eventually leak.
This case is far more common - it's doing the right thing but making small mistakes as it goes. If you don't know enough about the domain and / or don't have the right testing structures in place to validate it then it's very easy to go in the correct general direction with compounding problems.
Examples of this often include authentication issues where authentication exists but it's not used throughout the app. Or structural issues where the code supports one usecase but is so deeply coupled it's hard to add on additional features.
If too many of these problems compound, you may end up bricking your system requiring a costly rewrite. Though more likely you'll just have a bunch of bugs reported to you and maybe a few security reports (if they're nice).
You don't build knowledge about your system.
Because you're not hands-on with the system, you don't build up knowledge about how the system is actually built. Because you don't understand the system, you can't help out the AI when it gets stuck.
This means when a problem does happen (and it will!) you are left with a costly debugging session as you must first try to understand a system that is totally foreign to you even though you vibe coded it!
This is like building one of those houses with the bad foundations and the house is now slowly starting to shift. Had you been collaborating with the AI, you'd understand that shifting is likely a structural or foundational problem. But without that you'd have to search through the house and probably even call in an expert to figure it out (expensive!).
This is probably the most insidious problem with vibe coding because it leaves you totally helpless when problems do occur and if you can't fix it you may have to scrap the whole project.
This is what's happened to many vibe coders when they didn't implement authentication correctly. Internet people found the security holes and started pounding the service with fake data or using its services without paying. At some point they either had to call in an expert to fix it or trash the project altogether to stop the bleeding.
How can we code with AI better?
Enter Power Coding.
The analogy we're using is that AI is kind of like a power tool. It can vastly improve your process when wielded strategically but when wielded poorly can lead to many of the same mistakes manual would.
So Power Coding is using the AI like a power tool. Use it to enhance your process, not outsource the whole thing.
This is a hands-on approach, not a hands-off approach. It pulls from decades of best practices from software engineering - how to make software changes that produce business impact quickly and safely.
Plan your project and features before hand.
Plan your project before you begin coding. What is the expected outcome? What steps are there going to be? What context does the AI need to know?
Leverage AGENTS files and documentation to help the AI understand your codebase, best practices, and expected steps (like testing!). Generally you want to provide all information you would to a new engineer - what helps engineers code better is often what helps AIs code better!
Then provide good descriptions to the AI for each task.
Together this helps the AI one-shot your asks which decreases rework and increases throughput.
Build one small feature at a time.
By building one feature at a time, we can be more of a hands-on collaborator than a hands-off manager. This helps us avoid many of the issues with being hands-off.
- Describe one feature
- Review it
- Commit it
This allows you to:
- Increase chance of one-shots
- Catch problems early and often
- Minimize the amount of rework required when you do find a problem
This is similar to how most effective software engineering teams work - they do one specific task at a time, put it up for review, then commit it to the codebase to maintain forward progress.
I've personally found that atomic commits / stacked PRs work very well for this cycle. It provides git-native checkpointing so it's easy to rollback if you need to.
(I use the Graphite VS Code extension for managing my stacks but there are many tools out there like git town and jujutsu).
Understand your system as you build.
By planning your project and features before hand and being an active collaborator as you build, you'll be building knowledge about how your system works and the principles it's built on.
This doesn't necessarily mean you'll be able to unstick the AI whenever it gets stuck but it will give you a much better chance of it.
Plus this knowledge will lead to additional ideas / questions - about your codebase, the technology you're using, and your process.
This can lead to new feature ideas or ways to make it faster or better ways to work with AI.
Over time this builds core knowledge that will help improve your current and future projects. This knowledge will be crucial in improving as a collaborator for AI - providing more meaningful reviews, having better ideas of what to build, and steering it in those directions.
Maybe you saw what the AI did with authentication and you're curious if that's best practice or not. So you look it up and now you're equipped with best practices for both your current and future implementations.
Or maybe you're curious about the best way to build web APIs so you can see if there's any obvious improvements you can make. Now you know what good web APIs look like and can update your implemenation and have a good framework for future implementations.
Don't let AI get all the learnings from building this project, work on yourself as you go.
The whole idea of AI is that it makes us more efficient, freeing up more time from manual work. Don't waste that extra time doing rework, spend it on more strategic, forward-looking thinking.
Next
AI is here to stay and will likely get much better in the coming months and years. As they say, this is the worst it will ever be. The pragmatic strategy is not to ignore it but to figure out how to leverage it to improve your own craft.
Power Coding strikes a nice balance of getting big efficiency wins from AI today while avoiding many of the common pitfalls people are running into with vibe coding.
Plus I think it feels better as a software engineer. Less "let the AI go build everything for you" (often with comically bad outputs). More "leverage the AI to help you build more effectively" (when it works well, it feels a bit like magic).
If you liked this post you might also like:
Want more like this?
The best way to support my work is to like / comment / share for the algorithm and subscribe for future updates.