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> This thing can accomplish the same results as the frontier models do

What frontier models are you using? I'm literally sitting with Qwen 3.8 27B locally and trying my damnest to get it to work at least 80% of the quality Astra et al have, and can't get close to it. It keeps being so verbose, regardless of inference parameters, system prompts, reasoning effort, and keeps going on tangents. I'm running the BF16 weights via vLLM, can't imagine how much worse the experience must be with the quantized weights people run with the average consumer GPU.

Even with tight guiding, as I guess you could say I do more "AI pair-programming" than vibecoding, it just loses the plot to often and waste time, enough that it's clearly not an alternative to today's frontier models.

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> What frontier models are you using? I'm literally sitting with Qwen 3.8 27B locally and trying my damnest to get it to work at least 80% of the quality Astra et al have, and can't get close to it.

Local models can't get even close to frontier ones; it's just bragging. There's a reason why models like Astra have 2 orders of magnitude more parameters (and run on specialized clusters).

Compared to frontier models, local ones (like Qwen Flash) are much slower, they have much less room for context, and most importantly (people forget to mention) they produce much buggier code. Ah, Qwen also goes into loops sometimes. I've used them on powerful (consumer) hardware.

Having said, no doubt that Qwen-Flash-Next is considerably better than Qwen-3.8-27b - but on the other hand, it's several times bigger. Deepseek Flash also keeps getting better but bigger.

Unless you are trying to prove some hard math, or writing extremely complex code, for general coding tasks (which represent 90% of code out there) 27B-Q6_K is more than enough. Q4 does it well too, but might need a few more iterations.

For comparison, I also used GLM-5.3, Muse-Spark etc, with no significant improvement for ordinary/BAU coding tasks.

> Unless you are trying to prove some hard math, or writing extremely complex code, for general coding tasks (which represent 90% of code out there) 27B-Q6_K is more than enough. Q4 does it well too, but might need a few more iterations.

99% of the time, I'm not. But if it cannot reasonably write a basic CLI for transforming data without properly following a ~1K token long system prompt to the letter, then waste my time on it?

"Ah yes, I forgot about part X" or "I misunderstood the intent of Y" are so frequent compared to SOTA models, that it's hard to justify relying on it, even though I'm able to run it pretty much 24/7 at home, if it just was intelligent enough to actually make sense to do so.

FWIW that doesn't match my experience with Qwen 3.8 27B. After giving it some instructions on what to do (usually i start with what i want to achieve, followed by how i want things to do, then asking it for comments/questions/suggestions - all three - where it gives a bunch and i adjust/correct some of those until a plan is made and written down to a file that it can reference), it does things just fine and has done stuff more complex than basic CLIs - web apps, desktop GUI apps (i have several GUI utilities i'm using daily made with it), etc.

I did had it forget some stuff over long runs that went through several context compactions and i'd expect a larger model to be able to handle more vague instructions or have wider breadth of knowledge (if you're trying to make Qwen 3.8 27B do stuff outside the mainstream you need to provide it with some reference material to use) but i never had a problem with it not following instructions.

The harness might matter though but i'm using plain Pi (which is a very barebones harness with a very small system message) without any addons.

> Ah yes, I forgot about part X

Would something like LMCache help here?

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