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Jetson AGX Thor📖
1.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
2.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flashing a Pure Jetson System via SDK
  • 03.Flashing a Pure Jetson System via USB Drive
  • 04.Flashing the Jetson Bootloader using L4T
  • 05.Replacing the SSD
  • 06.Install Jetpack environment
  • 07.Backup SSD system
  • 08.Restore factory image system
  • 09.SSD expansion
  • 10.SSD Speed Test
3.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
  • 10.Change screen resolution
  • 11.Virtual Desktop
  • 12.Set root user password
  • 13.Configure sudo passwordless operation
  • 14.Managing custom services
  • 15.View system version information
4.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
5.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
6.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
  • 04.Torchaudio
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO26 development
  • 01.YOLO Environment Setup
  • 02.CLI Usage
  • 03.Object Detection
  • 04.Instance Segmentation
  • 05.Pose Estimation
  • 06.Image Classification
  • 07.Oriented Bounding Boxes Object Detection
  • 08.Model Conversion
  • 09.Dataset Annotation
  • 10.Model Training_Conversion
  • 11.Model Prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
7.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
8.ROS2-jazzy
1.ROS2 basic course
  • 1.Introduction to ROS2
  • 2.ROS2 install Jazzy
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
2.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
9.Offline AI model course
Offline large model (Text Interaction)
  • 1.Al large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
10.Online AI model course
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal Video Analysis Applications
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model offline voice assistant
11.Generative AI Applications
12.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
  • 18.OpenClaw Voice Assistant
Download
  • Code
  • SystemFile
  • VM_File
  • Tools
  • Jetson boot files
Download ZIP

Welcome to Jetson AGX Thor repository

1.OpenRouter large model API aggregation platform

1.OpenRouter large model API aggregation platform

1.OpenRouter large model API aggregation platformConcept Introduction1.1 What is "OpenRouter"?2. Practical Operations2.1 Obtaining API Credentials2.2 Graphical Process2.3 Configuration File Modifications

Concept Introduction

1.1 What is "OpenRouter"?

OpenRouter is a large model API aggregation platform. You can think of it as a "universal remote control for large models." Typically, if you want to use OpenAI's GPT-4, Anthropic's Claude-3, and Google's Gemini, you need to register accounts on each platform, obtain three sets of API keys, and write three different sets of calling code for each of their different API formats.

OpenRouter solves this problem. It provides a unified API interface (compatible with OpenAI's API format). With just one OpenRouter API key, you can access all of these models, as well as hundreds of other open and closed-source models, through this single entry point.

The core advantages of integrating OpenRouter into largemodel projects are:

  • Ultimate flexibility: You can seamlessly switch between any model, such as GPT, Claude, or Llama, by simply changing a model name in the configuration file, without changing any code.
  • Cost-Effectiveness: OpenRouter provides clear pricing for each model, allowing you to easily compare and select the most cost-effective model for your specific task.
  • Explore Cutting-Edge Models: Many new and excellent open source models are included in OpenRouter as soon as possible, allowing developers to quickly try them out.

In the largemodel project, configure the llm_platform parameter to openrouter to enable this powerful aggregation platform.

2. Practical Operations

2.1 Obtaining API Credentials

  1. Register for OpenRouter: Visit OpenRouter.ai and register an account using Google or email. If the connection fails, search for the "OpenRouter" platform yourself.
  2. Top Up: OpenRouter uses a prepaid payment model. You must first top up a small amount (e.g., $5) on the "Credits" page to start using it. Alternatively, you can directly use the free models, but there are rate limits.
  3. Create an API Key: On the "Keys" page, click "Create Key" to generate a new API key. Copy and save it immediately.

2.2 Graphical Process

  1. First visit the following website. If the connection fails, search for the "OpenRouter" platform and then search for keywords related to large models.

OpenRouter

image-20250801163705985

  1. Register and log in to your account.
  2. After registering your account, click to enter the Model Plaza.

image-20250801165400079

  1. Type "free" in the search bar to find models that can be used for free.

image-20250807175353717

  1. Hover your mouse over the clipboard icon next to the model to see the name you'll use when calling the model. Click to copy it.

image-20250807175436229

  1. Hover your mouse over the profile picture in the upper-right corner. It will expand. Click Keys to create a key.

image-20250807180439598

  1. Click Apply for an API key.

image-20250807180556227

  1. After applying, you will receive an API key.

image-20250801171806192

2.3 Configuration File Modifications

  1. Open large_model_interface.yaml:
vim ~/yahboom_ws/src/largemodel/config/large_model_interface.yaml

In openrouter_api_key, replace sk-xxxxxxxxxx with the API key you copied earlier.

openrouter_model is used to configure the large model for communication.

xxxxxxxxxx
# OpenRouter Platform Configuration
openrouter_api_key: "sk-xxxxxxxxxxxxxxxxxxx"
openrouter_model: "moonshotai/kimi-k2:free" # Model to use, for example, "google/gemini-pro-vision"
  1. Open yahboom.yaml:
xxxxxxxxxx
vim ~/yahboom_ws/src/largemodel/config/yahboom.yaml
  1. Switch the platform: Change the llm_platform parameter to openrouter.
xxxxxxxxxx
llm_platform: 'openrouter'

After switching the platform parameters, return to the workspace and compile again for the changes to take effect.

AI large model offline voice assistant
Multimodal Video Analysis Applications

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