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I made a Raspberry with Qwen my local car AI

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Found that you can actually run a 35B Qwen model on a Pi with very impressive intelligence and stability. Built connectors for car ODB to read all about car internals, and manufacturer's cloud service for stuff like changing AC or opening/ locking doors. Gave it info such as the full car manual. And then hooked it up with my other agents in our discussion room!So now it can answer car questions such as "when should I add oil and what kind of oil?" and help you fully offline, and when online talk with the agent family that includes all the most powerful models so they know how the car is, plan new features and develop itself with them.For example, if car breaks and can't move the car agent informs my agent family and they can already look for a suitable train ticket without me needing to check things or do something.

HN · launched Aug 25, 2026 · 146 upvotes (top 5%) · 0 views

Niche: local ai model runners and clients · 110 launches, 6 in the last 7 days ▼ -57% · Explore this niche →

Category and tags
  • Horizontal
  • Agent / copilot
  • Prosumer
  • AI-native
  • Hobby / open-source project

Visit website ↗ Source listing ↗

More like this: all 110 launches in local ai model runners and clients →

Could you build this?

Partial Interfacing with physical hardware like car OBD-II buses, reverse-engineering proprietary vehicle manufacturer APIs, and optimizing large LLM inference on resource-constrained Raspberry Pi hardware requires specialized embedded systems and automotive knowledge.

What it would actually take: A working system requires an embedded Linux environment on a Raspberry Pi 5 connecting to an OBD-II adapter via CAN bus or ELM327 UART/Bluetooth. The hard parts are handling CAN bus protocol variations, reverse-engineering proprietary OEM telematics cloud APIs (with auth tokens and encryption), and running quantized 35B models (e.g., via llama.cpp or vLLM with aggressive offloading/RAM swap). Building it demands automotive CAN/OBD engineering expertise and low-level hardware performance tuning.

Discussion

20 comments analyzed.

Competitors mentioned: Ornith, Google search, Gemma (smaller models), Qwen (smaller variants)

Concerns raised: Response speed could be faster, Q3 model accuracy issues and hallucinations with BF16 precision, Limited to 2 cars (Mercedes) - narrow device coverage, Difficulty understanding Q3 accuracy claims vs. real-world performance

Feature requests: Add more car manufacturer cloud integrations for expanded device coverage, Proactive agent actions based on user schedule and preferences, Clear status indicators for use cases (proven/testing/planned), Automatic climate control activation before entering car based on preferences

Competitors

Other products that read as similar to this one : 175 launches clear the similarity bar, closest 8 shown.

Attention rank: #10 of 176 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 300 days after the earliest competitor.

Related data

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