Noahcar
Autonomous Remote Controlled (toy) car using deep learning. Raspberry Pi, PiCar, Python
Install / Use
/learn @james4388/NoahcarREADME
autoRC
Street is danger, why not making autonomous toy?
Autonomous Remote Controlled (toy) car using deep learning. Raspberry Pi, PiCar, Python
You can find hardware on here, I'm too lazy to build from scratch.

Design
Inspired by Robot Operating System (ROS) each components is a Node and run in it own process. A Node can be sensors, controller, motion planner, camera, Deep Learning pilot... which talk to each other via pub/sub like system.

Current implementation using Python's built in multiprocessing manager that allow sharing objects, data between Processes. Pros: Utilize CPU cores, avoid PIL, run nodes over network from different machine. Cons: Data are being pickle/unpickle multiple time and send over network which is slower than thread. May consider using more decend message queue like Redis.
Images
|
:-------------------------:|:-------------------------:
Paper road | Duct tape border

Requirement
- Raspberry Model 2/3 or greater
- Python 3.5
- OpenCV (optional) or PyGame camera (just for capture video)
- SD card 8gb or more
- Car kit (robot HAT, mortor controller... mine just use picar)
- Webcam or Picamera
Documents
- Setup Hardware
- Setup software
- Run, record
- Training
Hit record button while driving. A pair of image and json file including steering angle, speed will be produce for each frame. Record folder located at
noahcar/autorc/training-set/copy to your PC for training.
- Write new model: Noahcar.ipynb
- Transfer leaning
- Autopilot
Run
cd noahcar
. env/bin/activate
python manage.py start
# Or
./manage.py -p <profile_name> start
Related Skills
claude-opus-4-5-migration
83.3kMigrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5
model-usage
337.7kUse CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
TrendRadar
49.8k⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载,你的 AI 舆情监控助手与热点筛选工具!聚合多平台热点 + RSS 订阅,支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机,也支持接入 MCP 架构,赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ,数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
mcp-for-beginners
15.7kThis open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
