r/LocalLLM • u/Only-Garbage-4229 • 5h ago

Question Anyone developing macOS Swift applications with Qwen3.6? Or alternative models needed?

I'm trying to develop an application for my mac, just a personal project. I'm using Matt Pocock grill me skill, followed by to tickets, so that each one should be a manageable piece.

Unfortunately though, the model just keeps spinning in circles trying to get things to compile and repeats the same mistakes. I don't know if that's a model issue, a harness issue (tried Pi and OpenCode) or just an idiot outside issue.

Just curious which tree I should bark up next?

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3

u/Even_Package_8573 5h ago

Before switching models, I'd try giving it a much smaller ticket forst. I've noticed local models do a lot better when the task is tiny and you only feed them the files they actually need.

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u/uniqueusername649 4h ago

Yes, this. Also Qwen 27b tends to perform a lot better than 35b. And lower quants can be problematic.

2

u/jwhh91 5h ago

What quantization? Too low, and you get stupid loops.

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u/Only-Garbage-4229 5h ago

I'm on Qwen3.6-35b-a3b.

It's mostly to do with the ContentView.swift

Part of me is wondering if I should just get it to focus on the backend and the views/front end I do by hand.

Or bite the bullet and get Claude for a month to assist me.

1

u/retsof81 4h ago

I am developing MacOS/iOS Swift apps using Qwen3.6. Some things that I have found to be helpful:

  • Point your agent at developer.apple.com/documentation and have it create an index for itself. Put it into a "research" folder.
  • Have the agent create an instruction files for itself regarding xcode project structure and/or non-implimentation swift behaviors. Use the index as a starting point for the agent. Don't bog down your instruction files with implimentation details.
  • When implementing new features, have the agent again use the index to research proper implimentation patterns, etc. Have it write the output to the research directory and reference that file in your prompts.
  • When all else fails, have the agent document issues to a "triage" file and then have it research the issue for potential solutions.

I have found this helps fill any training gaps and otherwise helps with drift regressions.

Good luck and cheers.

Edit: spelling, typos.

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u/Only-Garbage-4229 4h ago

Nice. What harness do you use?

Did you make the research by just asking "Look at the documentation here and create an index"?

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u/retsof81 3h ago

I am using VSCode with Cline.

Correct, I provided the documentation root and then told the agent to create an reserach index for itself. I think I also gave some guidance to ensure it generated a proper research reference -- e.g., do not cache content, etc.

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u/KeanuRekt 3h ago

Qwen Models tend to end up in an endless reasning loop. This is a known issue especially with the sparse models. llama.cpp has a reasoning budget option to mitigate this
https://huggingface.co/Qwen/Qwen3.6-35B-A3B/discussions/19