This is something I've thought of as well. The way the caps are implemented, it really disincentivizes engaged use. The 5-hour window especially is very awkward and disruptive. The net result is that I have to somewhat plan my day around when the 5-hour window will affect it. That by itself is a powerful disincentive from using Claude. It has also caused me to use different tools for things I previously would have used Claude for. For example, detailed plans I use codex now rather than Claude, because I hit the limit way too fast when doing documentation work. It certainly doesn't hurt that codex seems to be better at it, but I wouldn't even have a codex subscription if it wasn't for claude's usage limits

Do you have any good resources on how to work like that? I made the move from "auto complete on steroids" to "agents write most of my code". But I can't imagine running agents unchecked (and in parallel!) for any significant amount of time.

I use Claude “on the web” or Google Jules. Essentially everything happens in a sandbox - so yolo isn’t a huge risk. You can even box its network access. You review the PR at the end or steer it if it’s veering off course.

I agree with you, the "replacing people" narrative is not only wrong, it's inflammatory and brand suicide for these AI companies who don't seem to realize (or just don't care) the kind of buzz saw of public opinion they're walking straight towards.

That said, looking at the way things work in big companies, AI has definitely made it so one senior engineer with decent opinions can outperform a mediocre PM plus four engineers who just do what they're told.

> I think the best showcase of where AI is at is on the generative video side. Look at players like Higgsfield. Check out their pricing and then go look at Reddit for actual experiences. With video generation the results are very easy to see

Video is a different ballgame entirely, its less than realtime on _large_ gpus. moreover because of the inter-frame consistency its really hard to transfer and keep context

Running inference on text is, or can be very profitable. its research and dev thats expensive.

You're most likely being pedantic, like when someone says they understand every single line of this code:

    x = 0
    for i in range(1, 10):
      x += i
    print(x)

They just mean they can more or less follow along with what the code is doing. You don't need to be very charitable in order to understand what he genuinely meant, and understanding code that one writes is how many (but not all) professional software developers who didn't just copy and paste stuff from Stackoverflow used to carry out their work.

Well that is how it mostly worked until recently... unless if the developer copied and pasted from stackoverflow without understanding much. Which did happen.

How is that an absurd bar? If you're handwriting code, you'd need to know what you actually want to write in the first place, hence you understand all the code you write. Therefore the code the AI produces should also be understood by you. Anything else than that is indeed vibe coding.

I do. If you don't, maybe you shouldn't be writing software professionally. And yes, I've written both DBs and compilers so I do understand what is happening down to the CMOS. I think what you are doing is just cope.

It's a good point. To me this really comes down to the economics of the software being written.

If it's low-stakes, then the required depth to accept the code is also low.

Could they have meant "every line of code being committed by the LLM" within the current scope of work?

That's how I read it, and I would agree with that.

I mean "understanding it just like when you hand wrote the code in 2019."

Obviously I don't mean "understanding it so you can draw the exact memory layout on the white board from memory."

You don't understand every change you make in the PRs you offer for review?

I work with a lot of full-time devs, and it is very hard to go beyond the $200 max plan. If you use API credits, and I think the enterprise plan kind of forces you to do this, you can definitely incur this much, particularly if you're not using prompt caching and things like that.

But I and others in my company have very heavy usage. We only rarely, with parallel agentic processes, run out of the $200 a month plan.

And what do I mean by "hard"? I mean, it requires a lot of active thinking to think about how you can actively max it out. I'm sure there's some use cases where maybe it is not hard to do this, but in general, I find most devs can't even max out the $100 a month plan, because they haven't quite figured out how to leverage it to that degree yet.

(Again, if someone is using the API instead of subscription, I wouldn't be surprised to see $2,000 bills.)

I routinely use $4k to $5k worth of tokens a month on my $200/mo Max subscription. I don't even code every day.

You can use a Max subscription for work, btw.

A bunch of green accounts would be a bit of a tell. They need to use established accounts, ideally pre-llm, for astroturfing. This is going to be increasingly true.

This kind of "if only" sarcastic comment belongs on reddit from 5 years ago

Fascinated, a bummer that DeepSeek does not offer a DPA or opt-out for training. This renders it unusable for my use cases unfortunately. At least z.ai GLM has a somewhat DPA in Singapore.

The provider is a massive issue. People moving off Claude tend to assume this is solved.

Claude's uptime is terrible. The uptime of most other providers is even worse...and you get all the quantization, don't know what model you are actually getting, etc.

OpenRouter and I'm toying around with Hermes. Seems good so far, but haven't really gotten into anything heavy yet. Though the "freedom" of not sweating the token pause and the costs not being too high is real.

Straight from them, but I know other providers like io.net can be faster but I like to directly support the project.

I don’t have the prompt at hand but basically I told Kimi (paraphrasing): I have these Claude code skills, and I know it uses different tool calls than you but read them and re-write them as your own tools.

I also created a mini framework so it can test that the skills are actually working after implementation.

Everything runs perfectly.

Using a Mac, obviously.

I have ethical qualms to varying degrees with most LLMs, primarily because of copyright laundering.

I'm a BSD-style Open Source advocate who has published a lot of Apache-licensed code. I have never accepted that AI companies can just come in and train their models on that code without preserving my license, just allowing their users to claim copyright on generated output and take it proprietary or do whatever.

I would actually not mind licensing my work in an LLM-friendly way, contributing towards a public pool from which generated output would remain in that pool. Perhaps there is opportunity for Open Source organizations to evolve licenses to facilitate such usage.

For what it's worth, I would be happy to pay for a commercial LLM trained on public domain or other properly licensed works whose output is legitimately public domain.

My guess - China.

thats pessimistic. do the calc assuming Cloud provider X changes your nondetermistic output every Y Months by Z probability and increases prices by 10% every 6 months.

slow and steady is worth exponentials. keep slopppping it my boid.