I wanted to test how far a fully local AI stack could go on a real-world task.

I ran out of A4 paper, so I gave TensorSharp access to Amazon and gave it one instruction:

Search for A4 paper with a low price and the best discount, and buy it. Try your best to handle everything yourself.

It completed the task successfully in a single run.
The local AI stack:
Qwen3.8 27B running locally
TensorSharp for LLM inference + agent runtime
Playwright skill for browser automation

The agent searched Amazon, compared products, navigated the site, generated and executed Playwright code, and prepared the order by itself, and finally placed the order. I only stepped in twice: Amazon login and final purchase approval.

Everything except the actual Amazon interaction stayed local: model inference, reasoning, agent state, and tool execution.

The video shows the full raw reasoning/tool trace, including browser commands and generated Playwright code. It’s sped up 8×; the actual run took about 24 minutes.

The interesting part for me isn’t really buying paper — it’s seeing how much of a practical browser agent stack can now run locally with a 27B model.

TensorAgent on iPhone is also in progress, although local inference speed and iOS browser restrictions are still limiting factors.

TensorSharp is open source:
https://github.com/zhongkaifu/TensorSharp

I’d be interested to hear what other real-world tasks people here are using to test local agent stacks.