Nowhere is this more obvious in my current projects than with CRUD interface building. It will go nuts building these elaborate labyrinths and I’m sitting there baffled, bemused, foolishly hoping that THIS time it would recognise that a single SQL query is all that’s needed. It knows how to write complex SQL if you insist, but it never wants to.
But even with those frustrations, damn it is a lot faster than writing it all myself.
Most of my questions are "in one sentence respond: long rambling context and question"
This experience is familiar to every serious software engineer who has used AI code gen and then reviewed the output:
> But when I reviewed the codebase in detail in late January, the downside was obvious: the codebase was complete spaghetti14. I didn’t understand large parts of the Python source extraction pipeline, functions were scattered in random files without a clear shape, and a few files had grown to several thousand lines. It was extremely fragile; it solved the immediate problem but it was never going to cope with my larger vision,
Some people never get to the part where they review the code. They go straight to their LinkedIn or blog and start writing (or having ChatGPT write) posts about how manual coding is dead and they’re done writing code by hand forever.
Some people review the code and declare it unusable garbage, then also go to their social media and post how AI coding is completely useless and they’re not going to use it for anything.
This blog post shows the journey that anyone not in one of those two vocal minorities is going through right now: A realization that AI coding tools can be a large accelerator but you need to learn how to use them correctly in your workflow and you need to remain involved in the code. It’s not as clickbaity as the extreme takes that get posted all the time. It’s a little disappointing to read the part where they said hard work was still required. It is a realistic and balanced take on the state of AI coding, though.
One thing I will add: I actually don’t think it’s wrong to start out building a vibe coded spaghetti mess for a project like this… provided you see it as a prototype you’re going to learn from and then throw away. A throwaway prototype is immensely useful because it helps you figure out what you want to build in the first place, before you step down a level and focus on closely guiding the agent to actually build it.
The author’s mistake was that he thought the horrible prototype would evolve into the real thing. Of course it could not. But I suspect that the author’s final results when he did start afresh and build with closer attention to architecture were much better because he has learned more about the requirements for what he wanted to build from that first attempt.
I often see criticism towards projects that are AI-driven that assumes that codebase is crystalized in time, when in fact humans can keep iterating with AI on it until it is better. We don't expect an AI-less project to be perfect in 0.1.0, so why expect that from AI? I know the answer is that the marketing and Twitter/LinkedIn slop makes those claims, but it's more useful to see past the hype and investigate how to use these tools which are invariably here to stay
That's a big leap of faith and... kinda contradicts the article as I understood it.
My experience is entirely opposite (and matches my understanding of the article): vibing from the start makes you take orders of magnitude more time to perfect. AI is a multiplier as an assistant, but a divisor as an engineer.
Previously, takes were necessarily shallower or not as insightful ("worked with caveats for me, ymmv") - there just wasn't enough data - although a few have posted fairly balanced takes (@mitsuhiko for example).
I don't think we've seen the last of hypers and doomers though.
I’ve been driving Claude as my primary coding interface the last three months at my job. Other than a different domain, I feel like I could have written this exact article.
The project I’m on started as a vibe-coded prototype that quickly got promoted to a production service we sell.
I’ve had to build the mental model after the fact, while refactoring and ripping out large chunks of nonsense or dead code.
But the product wouldn’t exist without that quick and dirty prototype, and I can use Claude as a goddamned chainsaw to clean up.
On Friday, I finally added a type checker pre-commit hook and fixed the 90 existing errors (properly, no type ignores) in ~2 hours. I tried full-agentic first, and it failed miserably, then I went through error by error with Claude, we tightened up some exiting types, fixed some clunky abstractions, and got a nice, clean result.
AI-assisted coding is amazing, but IMO for production code there’s no substitute for human review and guidance.
Personally, I think it's just the natural flow when you're starting out. If he keeps going, his opinion is going to change and as he gets to know it better, he'll likely go more and more towards vibecoding again.
It's hard to say why, but you get better at it. Even if it's really hard to really put into words why
What’s really happening is that you’re all of those people in the beginning. Those people are you as you go through the experience. You’re excited after seeing it do the impossible and in later instances you’re critical of the imperfections. It’s like the stages of grief, a sort of Kübler-Ross model for AI.
It also reduces my hesitation to get started with something I don't know the answer well enough yet. Time 'wasted' on vibe-coding felt less painful than time 'wasted' on heads-down manual coding down a rabbit hole.
This is a great article. I’ve been trying to see how layered AI use can bridge this gap but the current models do seem to be lacking in the ambiguous design phase. They are amazing at the local execution phase.
Part of me thinks this is a reflection of software engineering as a whole. Most people are bad at design. Everyone usually gets better with repetition and experience. However, as there is never a right answer just a spectrum of tradeoffs, it seems difficult for the current models to replicate that part of the human process.
Ideally: local; offline.
Or do I have to wrestle it for 250 hours before it coughs up the dough? Last time I tried, the AI systems struggled with some of the most basic C code.
It seemed fine with Python, but then my cat can do that.
For local/offline Qwen 2.5 Coder 32B is probably your strongest option if you have the VRAM (or can run it quantized). Handles C better than most other local models in my experience.
90 percent of the things users want either A) dont exist or B) are impossible to find, install and run without being deeply technical.
These things dont need to scale, they dont need to be well designed. They are for the most part targeted, single user, single purpose, artifacts. They are migration scripts between services, they are quick and dirty tools that make bad UI and workflows less manual and more managable.
These are the use cases I am seeing from people OUTSIDE the tech sphere adopt AI coding for. It is what "non techies" are using things like open claw for. I have people who in the past would have been told "No, I will not fix your computer" talk to me excitedly about running cron jobs.
Not everything needs to be snap on quality, the bulk of end users are going to be happy with harbor freight quality because it is better than NO tools at all.
But it does a good job of countering the narrative you often see on LinkedIn, and to some extent on HN as well, where AI is portrayed as all-capable of developing enterprise software. If you spend any time in discussions hyping AI, you will have seen plenty of confident claims that traditional coding is dead and that AI will replace it soon. Posts like this is useful because it shows a more grounded reality.
> 90 percent of the things users want either A) dont exist or B) are impossible to find, install and run without being deeply technical. These things dont need to scale, they dont need to be well designed. They are for the most part targeted, single user, single purpose, artifacts.
Yes, that is a particular niche where AI can be applied effectively. But many AI proponents go much further and argue that AI is already capable of delivering complex, production-grade systems. They say, you don't need engineers anymore. They say, you only need product owners who can write down the spec. From what I have seen, that claim does not hold up and this article supports that view.
Many users may not be interested in scalability and maintainability... But for a number of us, including the OP and myself, the real question is whether AI can handle situations where scalability, maintainability and sound design DO actually matter. The OP does a good job of understanding this.
This could likely be extracted much easier now from the new code, but imagine API docs or a mapping of the logical ruleset with interwoven commentary - other devtools could be built easily, bug analysis could be done on the structure of rules independent of code, optimizations could be determined on an architectural level, etc.
LLMs need humans to know what to build. If generating code becomes easy, codifying a flexible context or understanding becomes the goal that amplifies what can be generated without effort.
Expanding a thought beyond 280 characters and publishing it somewhere other than the X outrage machine is something we should be encouraging.
Seconded!
I now have several projects going in languages that I've never used. I have a side project in Rust, and two Go projects. I have a few decades experience with backend development in Java, Kotlin (last ten years) and occasionally python. And some limited experience with a few other languages. I know how to structurer backend projects, what to look for, what needs testing, etc.
A lot of people would insist you need to review everything the AI generates. And that's very sensible. Except AI now generates code faster than I can review it. Our ability to review is now the bottleneck. And when stuff kind of works (evidenced by manual and automated testing), what's the right point to just say it's good enough? There are no easy answers here. But you do need to think about what an acceptable level of due diligence is. Vibe coding is basically the equivalent of blindly throwing something at the wall and seeing what sticks. Agentic engineering is on the opposite side of the spectrum.
I actually emphasize a lot of quality attributes in my prompts. The importance of good design, high cohesiveness, low coupling, SOLID principles, etc. Just asking for potential refactoring with an eye on that usually yields a few good opportunities. And then all you need to do is say "sounds good, lets do it". I get a little kick out of doing variations on silly prompts like that. "Make it so" is my favorite. Once you have a good plan, it doesn't really matter what you type.
I also ask critical questions about edge cases, testing the non happy path, hardening, concurrency, latency, throughput, etc. If you don't, AIs kind of default to taking short cuts, only focus on the happy path, or hallucinate that it's all fine, etc. But this doesn't necessarily require detailed reviews to find out. You can make the AI review code and produce detailed lists of everything that is wrong or could be improved. If there's something to be found, it will find it if you prompt it right.
There's an art to this. But I suspect that that too is going to be less work. A lot of this stuff boils down to evolving guardrails to do things right that otherwise go wrong. What if AIs start doing these things right by default? I think this is just going to get better and better.
This is my experience. Tests are perhaps the most challenging part of working with AI.
What’s especially awful is any refactor of existing shit code that does not have tests to begin with, and the feature is confusing or inappropriately and unknowingly used multiple places elsewhere.
AI will write test cases that the logic works at all (fine), but the behavior esp what’s covered in an integration test is just not covered at all.
I don’t have a great answer to this yet, especially because this has been most painful to me in a React app, where I don’t know testing best practices. But I’ve been eyeing up behavior driven development paired with spec driven development (AI) as a potential answer here.
Curious if anyone has an approach or framework for generating good tests
PaulHoule•4h ago