Really!? Glm5.3 is my daily driver and I feel im having the most productive experience with agentic collaborations so far, by a lot. Using pi with tons of custom extensions, that to be fair I developed since making the jump off of codex and claude about 12 weeks ago. I primarily do not write code for a living. I do a lot of modeling and commercial analysis and a lot of math (related to differentiable simulation)

Qwen3.8-Flash-Next seems pretty much auto pilot when I get it the right context.

Perhaps reverse the question: Are your build/construct requirements just really counter-productive to how LLMs need to understand things?

I've found constructing the code, writing the tests, adding the docs; then running through them gets most of the way there.

I've also found that making a simple obvious edit is a useless endevour when the LLM is primed for the long context tasks.

So, again, the question is reversed: are you over reliant on the LLM to do even stupid simple likes like editting a css variable?

I have 2 x ChatGPT Pro 20x, Claude Max 20x, and Kimi Vivace. It's about ~12 months payback for two units and the cable.

The problem is they can't fit any frontier level open models.

Last time I estimated, it would only take 3 months to pay back because the 1TB Mac Mini running Qwen RSIingly developed ASI and made infinity dollars off of crypto and I got put in jail by the SEC.

Where'd you get 30 years from? Show your work.

> Yet… even Altman called out Anthropic for serving dumbed down models.

Even Altman called out Anthropic? Isn't Anthropic the biggest competitor Sam Altman has?

I wouldn't be so sure. The generosity of the subscription plans has declined GREATLY over the past 6 months or so. They are likely trending towards api pricing parity. In which case, having your own hardware makes sense if you can utilize it well.

There could be gym logic at play. Hundreds signed up, 20 people actually exercising. Though it's probably more likely in the lower tiers.