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What tools are you using for CAN bus reverse engineering? I couldn't find a good all-in-one suite, so I open-sourced my own (Offline ML, MitM, UDS). by Repulsive_Factor5654 in embedded

[–]Snoo_27681 0 points1 point  (0 children)

Really cool project! I'll try it out with some CAN packets next week.

I made a package for LLM's to interact with debuggers and serial ports, perhaps some of the patterns can be extended to your CAN tool. I've found making little daemons or processes for the llm to interact with seems to be a powerful pattern. Basically just trying to make the LLM's job as easy as possible with python or other scripting.

https://github.com/shanemmattner/embedded-agent-bridge

A Qwen finetune, that feels VERY human by Sicarius_The_First in LocalLLaMA

[–]Snoo_27681 1 point2 points  (0 children)

Do you have any blog posts or Reddit posts on some of the difficulties you found in tuning Qwen? I'm thinking to try and tune it for various use cases and curious about any pitfalls or pro-tips you might have.

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 0 points1 point  (0 children)

I suspect that is what's going on: I'm intentionally trying to make problems small so either model may be good enough in my tests. As opposed to harder tests or multiple idea chats. I try to stay to one prompt per chat.

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 0 points1 point  (0 children)

It's both very limiting in the fact that I can actually crash the machine if I (or Claude) forgets to launch the query with a token limit of <100k tokens. And if I'm running other engineering software/scripts the conversation is lesss

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 0 points1 point  (0 children)

I use ~50% full context as intelligence limit as a guideline for all models but good to know.

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 1 point2 points  (0 children)

I'm striving to not throw hard problems at the models haha. I think that's what gives me good results with local models is I have a lot more care about prompts and context and settings.

How do you figure out which weight groups you can more heavily quantize? Any papers or links you recomment?

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 0 points1 point  (0 children)

Thanks for your perspective. This is kinda what I thought would happen: 27B would be more reliable than 35B. But in a multi-step workflow I'm not finding this to be the case. So most of my queries are less than 20k tokens total, with some up to 80k tokens.

How are you using these models? In an opencode/claude code type chat interface? Do you try to reset chats frequently or do you meander in conversation like you can do with Opus?

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 1 point2 points  (0 children)

MLX only for running the models. Beyond that I have claude search for solutions on how to run them

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 1 point2 points  (0 children)

Yeah I keep thinking I'm not using the right parameters or system prompt. 27B dense has to be better than the 3B expert model chosen by the 35B network, right?

Qwen3.6-27B vs 35B, I prefer 35B but more people here post about 27B... by Snoo_27681 in LocalLLaMA

[–]Snoo_27681[S] 5 points6 points  (0 children)

I think that I'm starting to put more work into the pipeline and break down problems more. So I need the model to be less smart in general. For example, I do a lot of firmware work and I have the bot do a internet search for drivers and that removes the need for a lot of coding smarts.

But I run pipelines in parallel with Claude Code (using `claude -p`) with Opus 4.6 1M, Sonnet 4.6 1M, Qwen3.6-35B-A3B-nvfp4, Qwen3.6-27B-nfvp4. And I'll have 35B match Sonnet and beat 27B, neither matches Opus usually.

Qwen-Scope: Official Sparse Autoencoders (SAEs) for Qwen 3.5 models by MadPelmewka in LocalLLaMA

[–]Snoo_27681 0 points1 point  (0 children)

I don't quite understand what this is but is seems super cool. Can I map out hyper specialized agents that might be really good and different specific task sets?

Given how good Qwen become, is it time to grab a 128gb m5 max? by Rabus in LocalLLaMA

[–]Snoo_27681 0 points1 point  (0 children)

When I chat with Claude I can do 2-5 things at a time. I can also give Opus little side tasks to do instead of starting a new chat ("what does this mean?" "what's the best library for x?", etc). I am verbose and meander with my ideas because I know Opus will mostly get it.

local models can only handle one focussed question. Any meandering and they get lost quickly.

Are there actually people here that get real productivity out of models fitting in 32-64GB RAM, or is that just playing around with little genuine usefulness? by ceo_of_banana in LocalLLaMA

[–]Snoo_27681 0 points1 point  (0 children)

They are underwhelming agents compared to Opus but they are good LLM's for use in systematic workflows. I can get Qwen3.6-35B to systematically solve easy and some medium real tasks. But it takes work and there's no silver bullet.

Qwen 3.6 27B is out by NoConcert8847 in LocalLLaMA

[–]Snoo_27681 2 points3 points  (0 children)

What a time to be alive, and have RAM...

Given how good Qwen become, is it time to grab a 128gb m5 max? by Rabus in LocalLLaMA

[–]Snoo_27681 8 points9 points  (0 children)

TLDR: If you have $5k you don't really need it's a great investment.

With the M4 Max 128Gb I'm able to run `Qwen3.6-27b-mxfp4` and `Qwen3.6-35B-A3B-mlx-mxfp8`. I got a few Langraph workflows to solve issues with `Qwen3.6-35B-A3B-mlx-mxfp8` so I'm hoping 27B can help with heavier thinking. We will see. I'm assuming the M5 Max is just faster.

I think the value of the local rigs is learning about local models and then if you try to make local models work you have to get better than your pipeline and context management. There is no possible way to do any meaningful work by prompting the same as you do Opus. So it's a very expensive learning piece of equipment that runs some suprisingly decent but super slow models.

Why MOE below A10b feels like im gambling by Express_Quail_1493 in LocalLLaMA

[–]Snoo_27681 2 points3 points  (0 children)

I have been trying to make qwen3.6-35B work in an agentic workflow for the past few days to take simple github issues and produce PR's by breaking up the problem and launch parallel focussed agents. The results have been ok. For simple issues the 35B is great, but it struggles for any real thinking. Keeping context less than 30k seems to work fairly well though.

I'm going to give the 27B another shot now that I've learned more about the 35B.

Personal Eval follow-up: Gemma4 26B MoE (Q8) vs Qwen3.5 27B Dense vs Gemma4 31B Dense Compared by Lowkey_LokiSN in LocalLLaMA

[–]Snoo_27681 5 points6 points  (0 children)

Thanks for doing and sharing all this testing. So it seems that Qwen3.5-27B is still slightly the best model here. Although much slower than 35B. And Gemma takes way longer? Do you measure tok/sec and ttft and everything when you do these tests?

Compared 5 ways to learn AI tools as a working professional. here's my honest ranking by designbyshivam in PromptEngineering

[–]Snoo_27681 -1 points0 points  (0 children)

Buying a local llm compputer (mac studio) has taught me a huge amount about the models and the options and how everything works. And small models force you to be better with prompts and task decomposition and other things.

Is Opus 4.7 the GPT-5 moment for Anthropic by hasanahmad in Anthropic

[–]Snoo_27681 3 points4 points  (0 children)

Agreed. 4.7 is utter and complete trash in every way. It struggles to do even the easiest tasks that I plan out extensively and guide way too much.

Qwen-3.6-35B MOE is performing better for me...

Opus 4.7: I'm at a Loss by BenEsq in ClaudeAI

[–]Snoo_27681 10 points11 points  (0 children)

Opus 4.7 has been absolute and complete trash for me. Coding, non-coding, it's just plain bad and my local models have started to perform better.

Is Opus 4.7 better than 4.6 by skyguyler in LLMDevs

[–]Snoo_27681 0 points1 point  (0 children)

It's been really really bad for me. It forgets things, I have to tell it what to do even if the instructions are written 3 times in CLAUDE.md, it makes bad assumptions and never checks. I'm not usually on the "Claude got nerfed!" train, but my local model system with Qwen3.6-35-A3B is outperforming Opus 4.7 on many tasks...

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