i'm not sure why people expect agents to one shot everything to perfection with just a prompt.
There's a reason why we talk about software development lifecycle, design, architecture, testing ... It's because it's been the most reliable way to build and ship software. We shouldn't expect discard this and expect agents to perform well outside of this.
I'm treating LLM agents as junior devs who happen to have vast knowledge of software engineering. As their team leader i make them go through planning, implementation, bug sweeping cycles using strict workflows. And it works quite well, i've been working on several large projects (1M+ LOC java,typescript,c/c++) and by any measure the projects are healthy. Sure the code isn't that beautiful, sure i'd have written things differently but it's pretty good nonetheless.
Shameless plug here: i've been also working on https://kodfactory.com, the code factory i've built to work on these large projects with workflows, reviews, etc ... I'm cleaning things up to open source it later.
In effect, I’ve always wanted a pair programmer agent, not a zero to one programming agent. Unfortunately models these days are mostly of the latter kind and it has caused a major disruption in the way I work. I’d much rather appreciate a small model making fast and specific edits that I ask if it, rather than ingesting 20 files to make changes, and then starting to write tests, etc.
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.
I think this is the only sensible way to work with agents, if you care about code quality and reliability but still want the benefits of AI. There seem to be three camps that people more or less fall into: (a) AI is terrible/bad/evil and should never be used, (b) you should one-shot everything and be happy if it seems to "work" when you try it, (c) the middle ground, where the AI writes code which you carefully review.
I definitely prefer (c). But I get why (b) can feel necessary. If your competition is using (b) there can be pressure to do the same just to keep up.
I just had a sunday afternoon request for a solution to a trivial but annoying spammer pattern on a stackoverflow-like server 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.
All on my Mac.
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.
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 an overview of, and I worry that I might forget what I have done. 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. Give it a go if you can.
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
This sounds miserable, why not just give it specific tasks to do in your normal workflow/editor? Why would we want to do MORE of the miserable task of ticket creation.
I tried, but I’m not sure I understand. The vibe tax is caused by the model trying to one-shot everything and doing so requires unnecessary tests? How are vibe coders training the model over months? Do you mean their sessions and preferences are being fed back into the RL?
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)
This is my experience too, but even worse. Opus 5 finished the task, I then asked it to code review it, 61 agents later it came back with a bunch of errors that needed fixing.
The first pass had tests, they passed, they were just wrong. I wish more people started reviewing their AI output, because I see a worrying trend of ”we have all these tests the agent wrote so it has to be good”, which is not surprising because understanding tests is not a trivial skill.
I feel like I'm living in a parallel universe when I read these types of posts.
My agents have never created code that is straight-up garbage and I have never flushed a week's worth of tokens down the toilet. I just can't identify with all the constant complaints about AI-assisted coding.
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. I'm doing 24x7 mutation testing on a dedicated box against the accounting engine and temporal systems. I even have specialized agents doing audits against Regulation Z (US banking law) criteria so the app models the required behavior of banks.
Most of my complaints about everything are nits, like the overly verbose and dense way LLMs communicate with me. 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).
This really resonated for me. It's like the smarter the model gets, somehow the more tokens get burned? Same failure mode whether you’re on Claude, Codex, or Cursor: the harness will spend the whole pool if you let it. I'm building my own Harness on top of pi that is add supported (www.freepi.ai) mostly because pi is so much more efficient with tokens. (That said, it tens to be slower and vastly more verbose with information I don't need to know). But yeah, since I'm trying to offer free ad supported inference the vibe tax would kill the business model. I've even been thinking about installing the 'caveman' skill to reign in token costs.
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.
There's a reason why we talk about software development lifecycle, design, architecture, testing ... It's because it's been the most reliable way to build and ship software. We shouldn't expect discard this and expect agents to perform well outside of this.
I'm treating LLM agents as junior devs who happen to have vast knowledge of software engineering. As their team leader i make them go through planning, implementation, bug sweeping cycles using strict workflows. And it works quite well, i've been working on several large projects (1M+ LOC java,typescript,c/c++) and by any measure the projects are healthy. Sure the code isn't that beautiful, sure i'd have written things differently but it's pretty good nonetheless.
Shameless plug here: i've been also working on https://kodfactory.com, the code factory i've built to work on these large projects with workflows, reviews, etc ... I'm cleaning things up to open source it later.
In effect, I’ve always wanted a pair programmer agent, not a zero to one programming agent. Unfortunately models these days are mostly of the latter kind and it has caused a major disruption in the way I work. I’d much rather appreciate a small model making fast and specific edits that I ask if it, rather than ingesting 20 files to make changes, and then starting to write tests, etc.
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.
I definitely prefer (c). But I get why (b) can feel necessary. If your competition is using (b) there can be pressure to do the same just to keep up.
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.
All on my Mac.
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.
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 an overview of, and I worry that I might forget what I have done. 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. Give it a go if you can.
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)
My agents have never created code that is straight-up garbage and I have never flushed a week's worth of tokens down the toilet. I just can't identify with all the constant complaints about AI-assisted coding.
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. I'm doing 24x7 mutation testing on a dedicated box against the accounting engine and temporal systems. I even have specialized agents doing audits against Regulation Z (US banking law) criteria so the app models the required behavior of banks.
Most of my complaints about everything are nits, like the overly verbose and dense way LLMs communicate with me. 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).
* the dev wants to describe an app in natural language then fall asleep while an AI works on it
* the dev wishes that the same AI would write less comprehensive tests
What exactly is a vibe coder to this dev?
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.