Just want to clarify, this is not my Substack, I’m just sharing this because I found it insightful.
The author describes himself as a “fractional CTO”(no clue what that means, don’t ask me) and advisor. His clients asked him how they could leverage AI. He decided to experience it for himself. From the author(emphasis mine):
I forced myself to use Claude Code exclusively to build a product. Three months. Not a single line of code written by me. I wanted to experience what my clients were considering—100% AI adoption. I needed to know firsthand why that 95% failure rate exists.
I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.
Now when clients ask me about AI adoption, I can tell them exactly what 100% looks like: it looks like failure. Not immediate failure—that’s the trap. Initial metrics look great. You ship faster. You feel productive. Then three months later, you realize nobody actually understands what you’ve built.
AI is really great for small apps. I’ve saved so many hours over weekends that would otherwise be spent coding a small thing I need a few times whereas now I can get an AI to spit it out for me.
But anything big and it’s fucking stupid, it cannot track large projects at all.
I don’t really agree, I think that’s kind of a problem with approaching it. I’ve built some pretty large projects with AI, but the thing is, you have to approach it the same way you should be approaching larger projects to begin with - you need to break it down into smaller steps/parts.
You don’t tell it “build me an entire project that does X, Y, Z, and A, B, C”, you have to tackle it one part at a time.
What kind of small things have you vibed out that you needed?
I have a little display on the back of a Raspberry Pi Zero W - it recoded that display software to refresh 5x faster, and it updated the content source to move from Meteomatics (who just discontinued their free API) to the National Weather Service.
FWIW that’s a good question but IMHO the better question is :
What kind of small things have you vibed out that you needed that didn’t actually exist or at least you couldn’t find after a 5min search on open source forges like CodeBerg, Gitblab, Github, etc?
Because making something quick that kind of works is nice… but why even do so in the first place if it’s already out there, maybe maintained but at least tested?
In a sense, this is what LLMs are doing for you: regurgitating stuff that’s already out there. But… they are “bright” enough to remix the various bits into custom solutions. So there might already be a NWS API access app example, and a Waveshare display example, and so on, but there’s not a specific example that codes up a local weather display for the time period and parameters you want to see (like, temperature and precipitation every 15 minutes for the next 12 hours at a specific location) on the particular display you have. Oh, and would you rather build that in C++ instead of Python? Yeah, LLMs are actually pretty good at remixing little stuff like that into things you’re not going to find exact examples of ready to your spec.
I built a MAL clone using AI, nearly 700 commits of AI. Obviously I was responsible for the quality of the output and reviewing and testing that it all works as expected, and leading it in the right direction when going down the wrong path, but it wrote all of the code for me.
There are other MAL clones out there, but none of them do everything I wanted, so that’s why I built my own project. It started off as an inside joke with a friend, and eventually materialized as an actual production-ready project. It’s limited more by design of the fact that it relies on database imports and delta edits rather than the fact that it was written by AI, because that’s just the nature of how data for these types of things tend to work.
Since you put such emphasis on “better”: I’d still like to have an answer to the one I posed.
Yours would be a reasonable follow-up question if we noticed that their vibed projects are utilities already available in the ecosystem. 👍
Sure, you’re right, I just worry (maybe needlessly) about people re-inventing the wheel because it’s “easier” than searching without properly understand the cost of the entire process.
A good LLM will do a web search first and copy its answer from there…
@MangoCats @utopiah exactly this… i did some small stuff out lf pastoring llms, but first searched for what I need, usually I find a small repo that kind of do what I want, then I clone it, change it a but using help of llm and if i think it is usefull I open a PR and let the mantainer decide if its good or not
Very valid!
What if I can find it but it’s either shit or bloated for my needs?
Open an issue to explain why it’s not enough for you? If you can make a PR for it that actually implements the things you need, do it?
My point to say everything is already out there and perfectly fits your need, only that a LOT is already out there. If all re-invent the wheel in our own corner it’s basically impossible to learn from each other.
There have been some articles published positing that AI coding tools spell the end for FOSS because everybody is just going to do stuff independently and don’t need to share with each other anymore to get things done.
I think those articles are short sighted, and missing the real phenomenon that the FOSS community needs each other now more than ever in order to tame the LLMs into being able to write stories more interesting than “See Spot run.” and the equivalent in software projects.
And if the maintainer doesn’t agree to merge your changes, what to you do then?
You have to build your own project, where you get to decide what gets added and what doesn’t.
These are the principles I follow:
https://indieweb.org/make_what_you_need
https://indieweb.org/use_what_you_make
I don’t have time to argue with FOSS creators to get my stuff in their projects, nor do I have the energy to maintain a personal fork of someone else’s work.
It’s much faster for me to start up Claude and code a very bespoke system just for my needs.
I don’t like web UIs nor do I want to run stuff in a Docker container. I just want a scriptable CLI application.
Like I just did a subtitle translation tool in 2-3 nights that produces much better quality than any of the ready made solutions I found on GitHub. One of which was an *arr stack web monstrosity and the other was a GUI application.
Neither did what I needed in the level of quality I want, so I made my own. One I can automate like I want and have running on my own server.
So much this. Over the years I have found various issues in FOSS and “done the right thing” submitting patches formatted just so into their own peculiar tracking systems according to all their own peculiar style and traditions, only to have the patches rejected for all kinds of arbitrary reasons - to which I say: “fine, I don’t really want our commercial competitors to have this anyway, I was just trying to be a good citizen in the community. I’ve done my part, you just go on publishing buggy junk - that’s fine.”
So the claim is it’s easier to Claudge a whole new app than to make a personal fork of one that works? Sounds unlikely.
Depends entirely on the app.
Yeah, that’s fair. In a minority of cases, with a certain app and needs to modify it to do your task, it may be true. Still rare.
So if it can be vibe coded, it’s pretty much certainly already a “thing”, but with some awkwardness.
Maybe what you need is a combination of two utilities, maybe the interface is very awkward for your use case, maybe you have to make a tiny compromise because it doesn’t quite match.
Maybe you want a little utility to do stuff with media. Now you could navigate your way through ffmpeg and mkvextract, which together handles what you want, with some scripting to keep you from having to remember the specific way to do things in the myriad of stuff those utilities do. An LLM could probably knock that script out for you quickly without having to delve too deeply into the documentation for the projects.
I’ll put it this way: LLMs have been getting pretty good at translation over the past 20 years. Sure, human translators still look down their noses at “automated translations” but, in the real world, an automated translation gets the job done well enough most of the time.
LLMs are also pretty good at translating code, say from C++ to Rust. Not million line code bases, but the little concepts they can do pretty well.
On a completely different tack, I’ve been pretty happy with LLM generated parsers. Like: I’ve got 1000 log files here, and I want to know how many times these lines appear. You’ve got grep for that. But, write me a utility that finds all occurrences of these lines, reads the time stamps, and then searches for any occurrences of these other lines within +/- 1 minute of the first ones… grep can’t really do that, but a 5 minute vibe coded parser can.
If I understand correctly then this means mostly adapting the interface?
It’s certainly a use case that LLM has a decent shot at.
Of course, having said that I gave it a spin with Gemini 3 and it just hallucinated a bunch of crap that doesn’t exist instead of properly identifying capable libraries or frontending media tools…
But in principle and upon occasion it can take care of little convenience utilities/functions like that. I continue to have no idea though why some people seem to claim to be able to ‘vibe code’ up anything of significance, even as I thought I was giving it an easy hit it completely screwed it up…
I tried using Gemini 3 for OpenSCAD, and it couldn’t slice a solid properly to save its life, I gave up on it after about 6 attempts to put a 3:12 slope shed roof on four walls. Same job in Opus 4.5 and I’ve got a very nicely styled 600 square foot floor plan with radiused 3D concrete printed walls, windows, doors, shed roof with 1’ overhang, and a python script that translates the .scad to a good looking .svg 2D floorplan.
I’m sure Gemini 3 is good for other things, but Opus 4.5 makes it look infantile in 3D modeling.
Having used both Gemini and Claude… I use Gemini when I need to quickly find something I don’t want to waste time searching for, or I need a recipe found and then modified to fit what I have on hand.
Everytime I used Gemini for coding has ended in failure. It constantly forgets things, forgets what version of a package you’re using so it tells you to do something that is deprecated, it was hell. I had to hold its hand the entire time and talk to it like it’s a stupid child.
Claude just works. I use Claude for so many things both chat and API. I didn’t care for AI until I tried Claude. There’s a whole whack of novels by a Russian author I like but they stopped translating the series. Claude vibe coded an app to read the Russian ebooks, translate them by chapter in a way that prevented context bleed. I can read any book in any language for about $2.50 in API tokens.
I’ve been using Claude to mediocre results, so this time I used Gemini 3 because everyone in my company is screaming “this time it works, trust us bro”. Claude has not been working so great for me for my day job either.
I think it really depends on the user and how you communicate with the AI. People are different, and we communicate differently. But if you’re precise and you tell it what you want, and what your expected result should be it’s pretty good at filling in the blanks.
I can pull really useful code out of Claude, but ask me to think up a prompt to feed into Gemini for video creation and they look like shit.
Encryption, login systems and pricing algorithms. Just the small annoying things /s
I’m curious about that too since you can “create” most small applications with a few lines of Bash, pipes, and all the available tools on Linux.
Depends on how demanding you are about your application deployment and finishing.
Do you want that running on an embedded system with specific display hardware?
Do you want that output styled a certain way?
AI/LLM are getting pretty good at taking those few lines of Bash, pipes and other tools’ concepts, translating them to a Rust, or C++, or Python, or what have you app and running them in very specific environments. I have been shocked at how quickly and well Claude Sonnet styled an interface for me, based on a cell phone snap shot of a screen that I gave it with the prompt “style the interface like this.”
Maybe they don’t run Linux. 🤭
Perverts!
Not OP but I made a little menu thing for launching VMs and a script for grabbing trailers for downloaded movies that reads the name of the folder, finds the trailer and uses yt-dlp to grab it, puts it in the folder and renames it.
Definitely sounds like a tiny shell script but yeah, I guess it’s seconds with an agent rather than a few minutes with manual coding 👍
Yeah pretty much! TBH for the first one there are already things online that can do that, I just wanted to test how the AI would do so I gave it a simple thing, it worked well and so I kept using it. The second one I wasn’t sure about because it’s a bit copyright-y, but yeah like you say it was just quicker. I wouldn’t use the AI for anything super important, but I figured it’d do for a quick little script that only needs to do one specific thing just for me.
I would need to inspect every line of that shit before using it. I’d be too scared that it would delete my entire library, like that dude who got their entire drive erased by Google Antigravity…
Yeah that’s fair. And mine were pretty small scripts so easy enough to check, and I keep proper backups and whatnot so no big deal. But like I say I wouldn’t use it for anything big or important.
It never seconds. The first three versions will don’t do what you want (or not work at all), so you will end up arguing with this shit for significant amount of time without realising it
😅