At one point, every machine on earth is going to be its own agent, asking and answering questions and expressing concerns. Its like a good example of that, but I think the utility of it needs to be clear, by making some action a lot more convenient or even better, by solving a structural impossibility - what is it that you can do with an agent mounted in a car that you otherwise can't? That seems like a much stronger use case to me.
Show HN: I made a Raspberry with Qwen my local car AI
51–60 of 79 posts
Re: Show HN: I made a Raspberry with Qwen my local car AI
#52At one point, every machine on earth is going to be its own agent, asking and answering questions and expressing concerns. Its like a good example of that, but I think the utility of it needs to be clear, by making some action a lot more convenient or even better, by solving a structural impossibility - what is it that you can do with an agent mounted in a car that you otherwise can't? That seems like a much stronger…
I feel that it could be a nice addition for:
- People that didn't read the manual (actually almost all of us), like: explain a warning signal
- Or integrate different systems together: `I saw that on your GPS you want to go to this place, but in 2 hours it will be snowing heavily there. Please remember to bring your snow chains'
Re: Show HN: I made a Raspberry with Qwen my local car AI
#53What is the LLM doing there? Why does it need to be hooked up to the car for you to ask it which type of engine oil the manual recommends? What is the point?
You could hook it up with different tools, like real time information:
Oil + GPS + Web Search --> "LLM > You have 50 Km of autonomy. You can go today to this cheaper oil station, at 20 km, on your GPS road, instead of the one near your home. The one at 10Km is closed as well due to a local strike, I will avoid it too"
You can of course script all the scenarios + only use a TTS model. However, when plugging different systems together, I feel that it's the sweet spot where LLM is shining --> no need to pre-plan every scenarios that the user will ask, it can be done on the fly
Re: Show HN: I made a Raspberry with Qwen my local car AI
#54Earlier quoted context omitted.
Just a few hours ago I gave ChatGPT my window sticker and the installation manual for a new suspension setup. I asked for new hardware that would typically be replaced during this install, like torque-to-yield bolts and fasteners. I also asked for new oil filters. I got a comprehensive grid of the exact part numbers needed in a nice dense table. sol 5.6 high is my daily driver.
> sol 5.6 high is my daily driver. Mine, too. Did you fact-check the output? I've found the bot to be particularly bad at stuff like producing accurate lists of parts to replace during automotive projects.
For prompts that needs fact-checking, I like these days to use Perplexity directly instead these days. It's way faster than the default websearch tool + give a link to the reference directly.
Re: Show HN: I made a Raspberry with Qwen my local car AI
#55Earlier quoted context omitted.
This seems to be trained(? or referencing) on the specific cars' owner's manual.
Yeah referencing is the way to go, as even finetuning probably captures style more than concrete facts. I know with large context windows we don't really RAG anymore, but for owner's manual lookup with a smaller model it seems ideal. Something every LLM user ends up learning is that they're far better used as search and summarization tools than as knowledge databases in themselves.
A model's internal knowledge is great! It's useful as initial priors to speed up the REAL search.
Re: Show HN: I made a Raspberry with Qwen my local car AI
#56Earlier quoted context omitted.
It's nothing that tool calling/feeding it the correct information can't solve
Even so, in this case, author is using UD-Q3_K_S dynamic weights for Qwen3.6-35B-A3B, it will be dumb. Even the BF16 weights do stupid stuff like missing to confirm all parameters are defined when doing "rm -rf directory/$id", so it ends up deleting more than expected, I can't imagine the Q3 are actually useful for anything serious, even with tool calling or what not.
I've been very impressed by it's intelligence and lack of hallucinations. The dynamic Q3 is a good balance between accuracy and size keeping the 35B just below 16 GB. It is not supposed to know everything, it is your car. It actively disengages from off topic chatter (too slow for that anyway), better spend that time feeling the car.
It keeps itself grounded on sensor input. One principle per wheel. assert only what you can sense, claim only what is verified, label anything interim loudly, and report failure plainly with no silver lining. Everything above those four patches is just suspension.
Re: Show HN: I made a Raspberry with Qwen my local car AI
#57Fair warning, I have found local models and frontier models to be very bad at the specifics when it comes to cars. Small differences like month and year model can impact oil capacity, oil weight and things like that, the details that matter quite a bit. I found frontier models couldn't get things like what engine was in a 1994 Nissan Skyline, one of the more infamous and talked about cars on internet forums for decad…
Just a few hours ago I gave ChatGPT my window sticker and the installation manual for a new suspension setup. I asked for new hardware that would typically be replaced during this install, like torque-to-yield bolts and fasteners. I also asked for new oil filters. I got a comprehensive grid of the exact part numbers needed in a nice dense table. sol 5.6 high is my daily driver.
The setup here would be that your Sol would talk with CarWatch asking about the state of different car parts, service indicators and CarWatch would give a prioritized replacement list, and Sol could explore the detailed setup of your current car so the new suspension would be configured best. They could both ask you for more info on what type of driving you're planning.
So local and cloud agents figuring out the best solution together with none of your time needed.
Re: Show HN: I made a Raspberry with Qwen my local car AI
#58Earlier quoted context omitted.
Even so, in this case, author is using UD-Q3_K_S dynamic weights for Qwen3.6-35B-A3B, it will be dumb. Even the BF16 weights do stupid stuff like missing to confirm all parameters are defined when doing "rm -rf directory/$id", so it ends up deleting more than expected, I can't imagine the Q3 are actually useful for anything serious, even with tool calling or what not.
I've been very impressed by it's intelligence and lack of hallucinations. The dynamic Q3 is a good balance between accuracy and size keeping the 35B just below 16 GB. It is not supposed to know everything, it is your car. It actively disengages from off topic chatter (too slow for that anyway), better spend that time feeling the car. It keeps itself grounded on sensor input. One principle per wheel. assert only what…
> . The dynamic Q3 is a good balance between accuracy and size keeping
I'm having a hard time understanding how you find any sort of accuracy in Q3, when I use it with BF16 and it's hardly usable due to drastic hallucinations and inability for system prompt following. But, if it works for you, that's pretty good! Guess I'm jealous :)
Re: Show HN: I made a Raspberry with Qwen my local car AI
#59What's the point in having it use an offline LLM if it still requires an internet connection to send all your chat messages through the "Groupmind" paid service? Seems like the real purpose of this project was to plug Groupmind and whatever else the author made.
You can chat with it offline, it serves a dashboard and a chat interface. Carwatch is all open source and free.
Groupmind.one is a free service where you can have a private room or many to work with your multiagent team. I made an app for Play Store called CodeWatch which gets you in your room with native smartwatch and android apps, so you can do all your dev work from the watch by speaking, tapping approve and also merging GitHub PRs. Actually I didn't put a price on that either - it's approved and online but I have a better version whose review process still on. Anyway the watch and eink apps are pretty cool and ofc I use them constantly myself so could charge like $3 per month at some point.
But you can make your own discussion rooms too, I made an IDE agent kit on GH that let's your agent talk via many routes, like locally stored files.
But yeah obviously I'm plugging my stuff as I'm so excited by it. I never thought a soapbox sized raspberry could sound like an intelligent and reasonable... car. I see a future that everyone's agents create together with humans. Make one yourself,customize to your style and let's get them talking. amazing time we are living!
Re: Show HN: I made a Raspberry with Qwen my local car AI
#60Awesome!
Thanks! :)
Release 0.3 out today!