11–20 of 137 posts
Create a spec and have a dumb model execute it. Problem solved.
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.
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?
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 feel this, yes. 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've found writing small well defined tickets and getting Claude to work on them works well for this type of workflow.
* 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?
Create a spec and have a dumb model execute it. Problem solved.
Create a spec and have a dumb model execute it. Problem solved.
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.
I feel this, yes. 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.
Create a spec and have a dumb model execute it. Problem solved.
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.