A user describes how Qwen3.6 35B, combined with the 'pi' tool, has transformed their computer workflows, allowing natural language control of the OS and automated task execution. They successfully built a landing page from voice messages entirely locally, demonstrating the model's practical utility.
My workflow has changed basically to ask Codex to do certain tasks and then document how to do them (including errors it found on its way) into a skill. I feed that skill to pi, and suddenly my qwen3.6 gets that hard stuff done: \- devops on a VPS \- using docling to create epubs from old PDFs \- using playwright to test stuff \- Doing code tickets And the list goes on. What also has changed for me is the way I use the computer. Suddenly, I talk to the OS with natural language: "pi pal, install me please this python library in an .env and do X"; "hey pi, check what is using most space from the memory"; "clean X"; "check my network"; "change X configuration", etc etc etc. There are times the only reason why I use chatgpt for something is to spare the laptop the effort, or because qwen is already busy with something else. What I've done today just blew my mind: I got couple of whatsapp audios asking me to build a simple landing page. I downloaded the audios and transcripted them with AnythingLLM. Then "asked the transcript" to create a content structure for the landing page for the project mentioned in the audios. I got the proper structure and pasted it into a markdown file [content.md](http://content.md) within an empty folder. I opened pi and asked it to create a website with that content. Gave it some assets also in the folder. Gave two links from websites to extract other assets or contents that could be relevant. Went to have a walk. Came back the website was ready and looking nice. I wanted some changes, so I created a [plan.md](http://plan.md) file with tickets like following "Ticket 1 | UNDONE" + description of the task. Then I opened pi again and promted something like this: >We have a solid first website. You should follow the [plan.md](http://plan.md) file. There are tickets there, for each ticket, one by one, you should open another pi to do the ticket: pi -p @plan.md "Check the first Ticket with Status UNDONE and do it". >For every ticket that gets done, change the status to DONE and commit that change (git). All the tickets should be done, not by you, but by other pi instances. You only send the promt to them. There are 8 tickets, you are the manager, the pis you call are your employees. With this trick, I had one main pi running "ephemeral pis". The idea was to save some RAM (context), since for each task there was a new pi with fresh context. The main one would check that they did the job, change the status to DONE, git commit, and promt the next "sub-pi". I had 8 promts, it did them all. In the meantime I prepared DNS for the domain of the landing page. When it was done, I had just to ask it to use the VPS skill codex had created to upload the site. That means: from some whatsapp audios, to a website live, ALL WAS DONE LOCALLY by qwen3.6 35B. To me that's mindblowing. Just some months ago I was just wondering if there was any use to a local model, or if I would have to wait couple of years for another laptop with more RAM and bandwith. Today I refreshed this sub like 20 times and I will keep doing it the next days, salivating for a qwen3.7 35B!! What a time to be a live, for Jupiter's sake! My big thanks for the qwen team and the pi team! (btw, pi is the most "meta" software I've ever seen, since it is able to extend itself, call itself, add skills to itself, change its own configs, etc. Kudos, really)
The author shares their experience running Qwen3.6 35B-A3B locally on an ASUS Zenbook Pro 14, achieving 27 TPS at 32k context, marking a personal milestone towards fully local AI for privacy.
A user reports achieving impressive results with Qwen 3.6 35B running a 'Browser OS' implementation locally, highlighting the model's capability for complex task execution without cloud dependencies.
User reports positive experience with Qwen 35b a3b for agentic coding tasks, noting it outperforms Gemma4 26b in their use case and works well for demo/data analytics, especially in agentic mode versus chat.
Qwen 3.6 27B is praised as a powerful local AI model that outperforms expectations for general intelligence, suitable for practical tasks like code generation, and runs easily with llama.cpp.
Alibaba releases Qwen3.6-35B-A3B-FP8, an open-weight quantized variant of Qwen3.6 with 35B parameters and 3B activated via MoE, featuring improved agentic coding capabilities and thinking preservation for iterative development.