Hi! From Ollama here - you can run: ollama run qwen3.8 (or if on mac qwen3.8:27b-mlx)

Also recently added ollama launch claude if you want to connect to cloud models from there :)

Hey! One of the maintainers of Ollama. 8GB of VRAM is a bit tight for coding agents since their prompts are quite large. You could try playing with qwen3 and at least 16k context length to see how it works.

How much ram are you running with? Qwen3 and gpt-oss:20b punch a good bit above their weight. Personally use it for small agents.

You're welcome to go through the source: https://github.com/ollama/ollama/

Desktop app is open-source now.

Hi - author of the post. Yes it does! The "build a search agent" example can be used with a local model. I'd recommend trying qwen3 or gpt-oss

Very cool, thank you!

Looking forward to try it with a few shell scripts (via the llm-ollama extension for the amazing Python ‘llm’) or Raycast (the lack of web search support for Ollama has been one of my biggest reasons for preferring cloud-hosted models).

Since we shipped web search with gpt-oss in the Ollama app I've personally been using that a lot more especially for research heavy tasks that I can shoot off. Plus with a 5090 or the new macs it's super fast.

Hey! Author of the blogpost and I also work on Ollama's tool calling. There has been a big push on tool calling over the last year to improve the parsing. What's the issues you're running into with local tool use? What models are you using?

Hey, unrelated to the question you're answering but where do I see the rate limits for free and paid tiers?

I went back and had another look at my implementation, and got it to work. Sorry I was mistaken!

That's a great idea. Going to try this next :)

Happy to help :)

Hey! I'm the author of the post. We haven't optimized sampling yet so it's running linearly on the CPU. A lot of SOTA work either does this while the model is running the forward pass or does the masking on the GPU.

The greedy accept is so that the mask doesn't need to be computed. Planning to make this more efficient from either ends.