euazOn

I highly encourage anyone e.g. on MacOS to get a cheap laptop and give Omarchy a try. My feeling of using this system compared to MacOS is a whole another level. And now one of the promises of Omarchy Quattro is that AI agents will help you tweak it to your liking very easily, avoiding having to spend dozens of hours ricing your system.

That is actually insane. In my opinion, the future is local AI: for most daily tasks, you absolutely don't need Fable level intelligence - you need Fable level agentic capabilities. And this model has (almost) just that. If we get a similarly capable MoE model in a few months (yes we will), it's going to be an utterly wild ride.

The multimodal abilities are great, but if you deal with text only, what is the benefit of using this over DS V4 Flash/Pro? 13-26x cheaper with comparable intelligence, and available across many different inference providers.

I fail to see the usecase where DS V4 Pro is not enough, but Flash 3.7 is - except multimodal.

Luna is similar, and also 8x cheaper. Source: artificialanalysis

The only benefit I can see is the speed, that looks to be outstanding, probably thanks to their TPUs.

Good points. Speaking from experience, it’s really hard to make it more economical than using a cloud AI service - even if you utilize the GPU to its fullest. There are, of course, other benefits, such as privacy/control/compliance/security, which should be the real reasons to do this, not cost.

Definitely, of course. But I think the logic of the author’s article is based on there being a huge difference between the two, or rather a high cost of inference in absolute terms. And that can change and we have seen it change. Which breaks the entire premise of the article going forward, no?

Inference will be always more expensive than db operations or copying, sure. But how much more expensive is the question.

> If you buy your own GPU, rack it, and run an open model, there is no unit cost.

No. There is economic opportunity cost (borrowing), energy cost, infra cost, depreciation / risk of failure with each unit of work, bandwidth, maintenance, and lots more. Small, but not zero, and often overlooked - especially the opportunity cost.

Exactly my point in another comment. Just to illustrate this further: a rough ballpark of how the cost of intelligence fell since 2022 could be about 1000x, and continues to fall. Unfortunately, it’s really hard to measure.

It’s so cheap that companies choose to spend more on AI inference (more reasoning, more capabilities, longer context), not less - see Jevons paradox.

Yeah, my point is that LLMs can reach that point too. Especially if you do it clientside. Copying was also way more expensive back in the 60s (accounting just for machine time and electricity, not storage cost, about 100 million times more expensive than today). Everything has a cost.

> Meeting that expectation means making LLM calls, and LLM calls cost money.

Of course, and so does everything in the software world. The point is getting the cost so low that it’s basically free. The new DS V4 Flash or the smaller Qwen3.6 models are still really expensive compared to what we were used to in the economics of software, but it’s not unreasonable to expect these costs to continue falling down.

Rough chatgpt estimate says 3-5 orders of magnitude of difference compared to a typical user interaction with a SPA (db/cache lookup, CDN…)

Per Artificial Analysis:

- Haiku: 30 points

- Luna Medium/High/Xhigh/Max: 38/46/49/51 points

That's a massive difference:

- 30 points is Gemma 4 31B territory

- 50 points is GLM-5.2 (744B) territory.

This is more terrible PR for Anthropic.

Reminds me of Slavoj Zizek’s classic example of synthetic sex (look it up), or his grievances about today’s academia: paper written by ChatGPT, peer reviewed by ChatGPT, and consumed by users as a synthesis from ChatGPT.