It's useless to talk about models and harnesses without context and method. Depending on how you use the model and what the model is used for, experience may vary drastically. Also, different models with different harnesses require different approaches.

I've been using https://gitlab.com/gabriel.chamon/orisun which is my own simplified methodology, for coding web apps in python and elixir and have been very successful using qwen3.6 27b Q4 locally with help of larger models for architecture, so I get very suspicious when people talk how useless larger models are. They are either using it for a domain that models don't perform well or just not using it right.

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taosx a day ago | parent [-]

I'm not sure about "useless" but from my experience agentic coding leads to death by a thousand cuts for all projects I've seen so far. Small decisions missed in a codebase that leads to degradation in correctness, reliability and performance. At some point it only takes one engineer to be careless, others skipping PR because they are AI generated...

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dofm a day ago | parent | next [-]

I got into a bit of an argument a while back when I used the word "crass" to describe some of the code decisions I've seen Claude make (in someone else's project that I have to work with).

But it is how I feel and it feels like the right word for the job. Because as you say, good code projects start out with good decisions.

It's like when you see a CAD design with a sequence of features that exist only to fix problems caused by starting from the wrong principles or the wrong baseline.

Sure the resulting part may end up identical as a solid for that specific need, but it could have been done in a way that was more robust, simple, easier to understand and modify, and where the design doesn't break in an unexpected way due to a small change of an early measurement.

(CAD has made my instincts much more visible to me)

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taosx a day ago | parent [-]

Exactly, and even when humans make bad decisions there is some friction, I feel that llm's don't have/notice that friction, they just bulldoze without caring about anything else.

	▲	whstl 18 hours ago \| parent [-]
		Yeah. IME, LLMs actually introduces a lot of friction when you want to improve the design. They are 100% biased towards the status quo.

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gchamonlive a day ago | parent | prev | next [-]

All of which you had with only humans in the loop. Catalogue problems so they become technical debt and tackle them periodically. Seems to me like this is less of an AI problem and more of bad management.

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voxgen a day ago | parent | prev [-]

It's a solvable problem if you're willing to throw more tokens at it. Frontier models have gotten very good at cleaning up their own messes. You just need the right skills/loops, and to stick to models that consistently follow instructions (i.e. GPT-5.5/GPT-5.6-Sol).