Comment by AmazingTurtle
6 hours ago
> Could there be a benefit to releasing a new model, slowly dumbing it down over a couple months, then releasing a new model that’s marginally if at all better than the original to create a perceived improvement when in reality there isn’t really one?
Exactly what I am saying for months now. And it's exactly the reason why I am shifting to open weight models now. Just bought myself a 2x DGX Spark Cluster. Will run Qwen3.8 Flash Next on it, maybe Qwen4 when it comes out.
Not only do I have full control over quantization and inference, but also will I experience a constant level of quality. It won't be frontier. But it will be stable, and that's enough reason for me to switch. Also I will likely save some money on subscriptions.
I don’t know what people do with the open models but having tried a lot of them I just can’t make it make sense. they’re too dumb and it effectively makes them useless (to me). it’s probably worth being honest about the low ceiling here.
IMO this comes down to your harness. Any frontier model from a huge shop has an inherent benefit in the system you're using it in. Search, memory, skills, integrations you don't realize even exist make them much more powerful. It is some effort but I recommend trying Hermes Agent and setting it up fully, that's the closest you'll get to a more complete experience.
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)
What HW are you running this on?
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Which models did you try for which tasks?
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?
if the answer to 'the model is bad at X' is "you're over-reliant on it" - then yes, the model is bad at X in comparison to alternatives.
Last time I estimated it was like 30 years to pay back. I doubt the hardware will even last that long.
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.
Is Kimi really competitive enough to have it in your mix?
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how do you get 2 cgpt pro?
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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.
I would like to subscribe to your newsletter.
flash next is good, I've been running it for like 2 weeks now and it's pretty solid, hope you like it and it meets your needs. I still lean on Claude and codex a fair bit for harder stuff, but I'm rapidly moving towards 2x $20 plans instead of 2x $200 plans
Serving compute is their main value prop
Yet… even Altman called out Anthropic for serving dumbed down models.
Shits weird man
> 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?
Yeah, and? They share a business model
>Also I will likely save some money on subscriptions.
Unlikely. The $200 Claude subscription allows for billions of tokens/month, and that kind of hardware will take years to amortize.
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.
I max out my Claude Max plan every week, and I can measure the output, and for me it's stayed fairly constant, subject to the various "bonuses" whenever Anthropic is feeling the competitive pressure.
There could be gym logic at play. Hundreds signed up, 20 people actually exercising. Though it's probably more likely in the lower tiers.