A project I was working on for a couple years until this spring started to turn like this when another contractor started committing AI work. There was a test literally called “test_imports.py” that, you know, tested that you could import every module of the project. I asked if we could just run `python __main__.py` to test that the imports worked, but he said no, we need this for big Claude-powered refactors to make sure nothing breaks.

> Or their predisposition to add, add, and add more stuff when proper engineering practices are more often about subtraction (but I've built mitigation guardrails against a lot of that).

> a… personal financial management application… about 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline

Just how much functionality are you getting out of that? It's hard for me to imagine that people want that much out of such a program. I just keep a spreadsheet. (Yes, LibreOffice is also very bloated.)

Personal finance tracker - the TODO app of 2026.

> personal financial management application

> 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline

I mean...

Yeah, that's pretty self-explanatory why you don't identify complains about AI-assisted coding.

> I feel like I'm living in a parallel universe when I read these types of posts.

> And my biggest project isn't some hello world app. It's a self-hosted, privacy-focused personal financial management application that I intend to open source. It's about 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline.

Indeed; non-overlapping Overton windows.

edit: I'm going bluntly ask, after pondering this more: Is this satire?

I signed up to say I’m also in the personal finance management camp. Scraping all financial institutions, AI can categorize things that I previously did manually, able to do tax projections, retirement analysis, categorize individual items in Amazon purchases, analyze travel purchases with points vs cash.

Instead of paying for multiple apps that do small parts of it, I use existing codex subscription to make it better.

Are you surprised that when you look into the mirror and you see your own face not someone else's?

Remember, models have no identity. They just try to say what they think you wan them to say.

How in the world do you need 30k LOC for your CI/CD??

It happens, but when it does, you need to ask yourself: if the agent is struggling this much to produce something that's working, am I taking the right approach?

If you ask for a particular thing, they'll do it, even if it's not a good idea. When you start running into issues, they'll try to solve those issues for you. They'll do that as long as you keep asking, even if there's no good way to properly fix the issues, because the initial approach was wrong.

When an agent is struggling to produce something, I switch to asking it to re-evaluate the approach itself, and ask it to suggest a less brittle approach. I then chat through the various options, and choose the best approach that makes sense, and then the agent is back on track, producing properly working code without the issues.

Some people just keep pushing through on bad approaches, without questioning it and then blame the agent for being unable to finish it.

> I even have specialized agents doing audits against Regulation Z (US banking law) criteria so the app models the required behavior of banks.

Can you elaborate on this? Clearly you cannot imply this means those audits have any real value since its just roleplay in this context right? Because your app in the current form will not be affected by Regulation Z in any way.

> i'm not sure why people expect agents to one shot everything to perfection with just a prompt.

because that's how agents are marketed.

Using AI is kayfabe. What I mean is, you create interaction patterns that resemble how humans work. This is because it is what the models are trained on but also because we've all been trained to interact in this way. So it manipulates you into providing more useful prompts.

But I don't really want to play a part in a simulation, trying to cajole my scene partners into saying the lines I need them to say. I want to use a tool the same way I would use any other tool. If this is AI it should just do the thing. Anything else is an imperfection of the technology.

But at the same time, language is a vague communication medium. We have a precise language for describing forms of computation, but that's code so we're back at square one. We still haven't nailed the right amount of follow up and correction and interrupt-ability of these coding agents.

And we may never figure it out. It may simply be impossible. But it doesn't mean this weird anthropomorphization of AI is something I want to do. If I wanted to be a manager, I would be a manager.

> i'm not sure why people expect agents to one shot everything to perfection with just a prompt.

Every time you see a benchmark for "how long the agent can go without asking for human intervention", that's encouraging vibe coding.

> i'm not sure why people expect agents to one shot everything to perfection with just a prompt.

because that's the end goal? and for simple small stuff they're already there?

> i'm not sure why people expect agents to one shot everything to perfection with just a prompt.

They do often enough that it's not a surprising event, depending on prompt quality, context available, ability for the result to be objectively judged and iterate on by the agent, etc. For frontiers on very high settings at least.

If you explicitly ask the agent to make the perfect architecture for the problem and write it down in to a spec and have the developer agents follow it they will. Its just that coding agents have a hard time coding at think about architecture at the same time.

I can one shot a prompt if I write down a nice spec file, Claude can do a lot in one shot. I test it every few months. With enough detail Claude will know what to do.

I have found what works well is to modularise the code as much as possible and get an agent to work within a very limited scope. Break everything down well using SRP with well defined interfaces and let the agent work on small problems. Then when it shits the bed, there’s a smaller blast radius and you can strip back and try again.

I think seasoned developers, over time, learn how to work a code base and design components with well defined interfaces, where the implementation is isolated in small well contained classes. SRP etc. more junior programmers can work on those smaller components/services in isolation.

For me this also seems to be a productive way to work along side an agent. Break up functionally into well defined chunks, and let the agent work on each small problem. Take more of a lead in the architecture I suppose.

Just said something similar myself in another thread. I'm either writing things by hand (and using LLMs for research, or double checking an idea), or having an LLM spit out something I treat as an external dependency. Its still too tedious for me to use them to write code when I care how it works or there's not obvious invariants the code needs to hold

I just had a sunday afternoon request for a solution to a trivial but annoying spammer pattern on a stackoverflow-like site I maintain.

The software has a plugin API. I asked Muse Glimmer to recommend a plugin — it found one but I tested it and it didn't work for unclear reasons (among other things the software installed version is old, the plugin older). I then asked it to outline how to implement a simple word filter, it gave me an overview of some hooks that looked right from dim-and-distant-past recollection of reading the docs when I installed it. I asked it some questions, it did the research.

I then set it off generating the skeleton of a filter plugin, went to the shops to buy food, came back and worked through filling it in and finishing it off. There was a bug. It found the solution.

It's only about 100 lines of code but it is a random old webapp and it had to look stuff up to finish it, and I think it did rather well. All on my Mac.

I am deeply cynical of the one-shot code, "nobody codes anymore" hype culture idea and that distaste put me off AI and agentic coding for ages. Like you, I want an assistant but as a freelancer I have to stay in control. I have no interest in the "just specify loops" BS and it will be bad for my business anyway.

I worry about code that I don't have a good working overivew of, and I worry that I might forget what I have done (I have pretty bad issues with focus and memory). But in this particular case, I don't really care if I forget, because there's documented code and I have no intention of specialising in this app. So it was a nice little test case.

I also don't really want to sit around waiting for Qwen 3.8 27B on this machine. Muse Glimmer is fine, actually. Gets to the solution as quickly as Qwen 3.6 35B-A3B.

This gives me a little hope that local AI will give me the sort of responsive developer sidekick I actually want.

I've found writing small well defined tickets and getting Claude to work on them works well for this type of workflow.

Wasn't that what the first iterations of cursor were?

I would love, love an even better Zeta model for that reason.

Forgot where i saw it discussed; If you observe recent model benchmarks over the past year; the performance is slowly climbing, but if you divide by the token count; the score per token is dropping.

The current trend in state-of-art LLM coding agents is giving more output, thinking longer and checking the results more to catch mistakes. Be it an economics inventive to make users burn through their quota or show increase in usage for shareholders, or a market demand of users liking the ability of models to do independent work without intervention or oversight; the result is what the article seem to call the Vibe Tax.

I myself asked Claude code recently to review a somewhat large PR, to see what it would find. I didn't expect much, but also didn't quite realize how the model would interpret my request; I burned $20 in 3 minutes in API usage, as it ran 2 sub-agents which themselves spun up 5 more each. Most sub-agents were manually checking for things clang-tidy would catch without actually calling clang-tidy. This behavior rose as i changed from sonnet/opus 4.6 to 4.8 and now 5.0.

I don't want to run a agent independently in this way; i ask targeted questions about specific things and review the result. But model development is targeted towards a more hands-off "vibe" workflow, because that's where the money and hype is. As a result, i find the models more frustrating, less trustworthy and more costly to my work. (I've even started using haiku more, since it remains to-the-point without steering away from what i ask)

I'll try to explain my experience with this. I've noticed the AI has a tendency to overengineer scaffolding. For instance, I asked it to help me with a refactor, and it erected this massive 100kloc function registry, and then caused GitHub CI to verify the contracts every single commit, which took upwards of 30 minutes (I suspect this proclivity is widespread and has contributed to their recent issues).

As if this wasn't bad enough, it also was not smart enough to regenerate the evidence in these contracts as it changed the underlying source code. So it would get in a loop where it would update code -> commit -> 15 minutes later CI would error citing the contracts weren't updated -> it would fix the contracts -> 15 minutes later CI would error because the fix was wrong -> it would fix the fix and commit -> 15 minutes later contracts would fail -> contracts were fixed again and this time maybe 30 minutes later it would pass, maybe it errors again.

This loop could go on all day every day if someone wasn't paying attention because the agent has no concept of time or wasted work. It's an AI livelock of sorts, but it will eventually converge in my experience. It'll just take 10x longer (literally like 20+ hours) than if you just intervene and tell it knock it off, so it feels like lighting money on fire (hence the tax).

That's why I feel like this vibe coding stuff has to actually be monitored, like a Tesla system -- because like a Tesla system it cannot be trusted to not crash into the proverbial code wall.

Yeah I'm getting this feeling too, that Opus 5 collaborates better with other Claudes, but that some of the older Opus models collaborated with people better.

> micro-managed

Perfect, this is exactly how I refer to my LLM workflow too!

They want it to write a rough draft which they can critique.

That workflow itself is what the author is calling a "vibetax". Models are getting worse for users that must review every line of code the models edits or adds. Models are writing more line of code, changing more lines of code,executing more tools, and making it harder for the user to monitor, control, and review.

if i wanna write a web-app or python script; the models are better than ever. If i want to fix a specific bug in a established and trusted legacy cobe-base; Haiku 4.6 does a better job than Opus 5.0, because it does what it's told and nothing more.

The author wanted a todo-list starting-point; realistically 200 rows of html+CSS without the back-end. Heck, they may not even want to make a todo-app, but thought a todo-app would be a decent starting-point. So why would we ever want a model to spend a weeks worth of tokens on everything except the request the user asked? This is not a cost issue; this is a control issue.

Ask the AI to create a detailed spec according to a few simple requirements. Review the spec yourself and correct what you want changed. Then ask the AI to implement the spec. Each time you request something new, ask the AI to update the spec as well.

Dumb models will make more mistakes even with good spec no? They lack capacity to verify (to be introspective) and will pattern match over reasoning.

The post was published today by someone who is clearly making satirical reference to frontier models ("Pol" alludes to GPT-5.6 Sol).