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Topcoat: The full full-stack framework for Rust
(github.com)
Looks like pretty nice dx. Will keep an eye out
⚠TRIGGER WARNING⚠ two tokio-rs repos (this and toasty) have AGENTS.md files.
These days we have to expect many projects will have them. It's really a question of how responsibly and carefully AI is used. Some people just throw in their requirements, have the AI spit out code, do a cursory check to see if it seems to work, and that's it. Other people take responsibility for the architecture and the code quality, using AI in a focused way and reviewing the results carefully. I'd look at the quality of the architecture and code before judging. I confess I have not done that yet with this one; I just thought it sounded potentially interesting.
There are quite a few ways to write web applications with Rust these days, and while this sounds like it has potential, I'd still pick frameworks that are a little more mature for my own projects.
you got me at "responsibly" burning resources.
Not everyone uses big data center powered LLM inference though. Running some models locally uses less power than playing a video game.
now where have these models been trained?
The existing local models already exist, and new releases will happen because the companies want to sell you inference. They receive no profit or incentive to release the models as open weights but they do it anyway - There is no coherent ethical delimma against using them locally, as it has no effect on whether the companies will or will not be encouraged to do more training.
I'm using it locally, with models my existing computer can handle. I think, but I'm not sure, that it's better for the environment than buying into some massive model hosted in a data centre. And I don't want to lose my skills so I still try to do as much as I can without calling on it.
I will say that using a super efficient cloud model (deepseek for me, v4 flash is very cheap) is probably more efficient than using a local model in most cases, especially if you need strong capabilities. The question is whether your own grid or the provider's grid is more environmentally friendly (hint: china's energy mix is about 50% fossil fuels while the US's mix is around 75% fossil fuels overall)
Training is of course expensive still, but Chinese labs have all but caught up to US labs despite much lower compute resources being available to them.
That's a fair point. Where I am is about 28% fossil fuels I believe, so it's a slightly different calculation.
did you generate your model locally?
No, and I do understand that training is the most energy-intensive part of the process. I don't know how exactly it divides up: how much of the work of massive data centres is training and how much is running people's queries on finished models.
Or, we could choose not to use slopware. Especially when they appear to mostly be wrappers around software we can configure ourselves.
It's hard to responsibly use tech built irresponsibly.
How do you know it was built irresponsibly without reading the code, which is something you should be doing when adopting a dependency anyway?
See: data centers, noise and cooling.
See also: intellectual property, theft of; infringement, copyright.
You don't know the direct impact of this project on data center usage because you haven't observed the development process. And you don't know whether it infringes on copyright because you haven't read the code.
See also: model training, mechanics of
this post was submitted on 17 Jul 2026
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