I never got into any of the AI models because it was clear local first was going to be more valueable, if they were to replace coding tasks.

I tried out a few models and ended up going with either Qwen3-Coder-Next (no think, just do) and Qwen3.6-35B (thinking, w/llamacpp token budget). Created a customized prompt that works fairly well to around ~60k tokens and then is a toss up on whether it's poisoned itself or I've directly steered it into the wrong. When it's clear that's happened, if it's important to continue, ask it to write a doc then start fresh.

I don't kno whow any one cold have witnessed the last 2 decades of American VC funded tech startups and tell themselves, "you know, this will be a reliable technolgy with no hidden problems".

Even a sober technical evaluation is just two steps:

1. You're proposing to build a app on a non-deterministic model.

2. That model is hosted behind a non-deterministic system (model alignment, model guardrails, system context subterfuge, cost/token pricing)

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So you want to build your app and you think you're going to kep up with both #1 and #2?

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ACCount37 2 hours ago | parent | next [-]

We live in a non-deterministic world. Anything "deterministic" in it is a castle built on quicksand.

LLMs are, as far as the nastiness of the Real World goes, really fucking benign. Future models outperform past models, both in open weight land and at the big frontier labs. Performance per $ only ever goes up. That's just nice.

	▲	windexh8er 14 minutes ago \| parent \| next [-]
		> We live in a non-deterministic world. Anything "deterministic" in it is a castle built on quicksand. Except the Enterprise, and a lot of what people want compute for, is built on deterministic systems or processes. I'm not saying the non-deterministic nature of LLMs isn't useful. However I've worked with a lot of organizations on SOAR projects, for example. When you can weave the deterministic and non-deterministic together you get a relatively efficient system. A workflow that will stay on the rails and will come to a conclusion as expected. And the "as expected" part is critical in these types of systems. The reality of, using SOAR as an example, is also that most enterprise would be much better served by fast SLMs. Parse an email and validate if it's SPAM / Phishing or read a chunk of firewall logs and look for outliers / indications for escalation - those things can get messy in a deterministic system because of potentially unstructured data. I don't believe it's either / or. And I believe that LLMs just aren't efficient, fast or reliable in the sense that deterministic are. It seems, at least to me, a better together story.
	▲	cyanydeez an hour ago \| parent \| prev [-]
		YES, but you seem to not understand that having two non-deterministic layers is incompatible. #1 is fine: it has random issue and you build around those random issues; those issues don't change unless you change them. #2 is not fine; that non-determinism you do not control, have no insight into, etc. I'm saying sure, give me #1 if it means I can build a harness around it and smooth over the edges. But I'm not taking #1 and #2. There's zero reasonable way to manae two non-deterministic systems.

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maykthewessen 2 hours ago | parent | prev [-]

Qwen is the Alibaba distilled Anthropic Claude model

So piracy on an by piracy trained ai model..

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cogman10 2 hours ago | parent [-]

Piracy? Lol.

Alibaba didn't steal Opus weights, they used opus output to train their model.

If this is piracy, then so is reverse engineering efforts powering a bunch of Linux drivers.

	▲	cyanydeez an hour ago \| parent [-]
		If that's piracy, I'm going to the library and arresting everyone there! Also, yeah, they already stole their copyrighted works, so a thief from a thief is still...theives?