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AI-assisted development on NVIDIA Jetson

A New Way to Build Edge AI.

Put coding agents alongside real Jetson hardware to inspect, prototype, benchmark, and optimize faster.

Less repetitive work. More time to build what matters.

Watch the walkthrough Try the prompts ↓

Why AI-assisted development

Turn device insight into results, faster.

A coding agent working with real Jetson hardware The coding agent harness runs on the Jetson alongside project files and device tools, shown together in one pale green region. Separate lines with green dots represent connections to a camera, a distance sensor, and a robot arm. These are schematic device connections, not physical cable routing. Coding agent harness Running on Jetson Camera Distance sensor Robot arm Inspect → Run → Measure
Agent tools run on Jetson. AI models can run locally or in the cloud.

A coding agent can do more than suggest code. Give it controlled access to your Jetson project and it can inspect the real system, carry out approved experiments, and help you interpret the results.

  1. Inspect the real device.

    Discover connected hardware, available memory, software, and logs.

  2. Run real experiments.

    Capture camera data, read sensors, and test approved actions through device tools.

  3. Build on measured results.

    Compare model performance, refine your prototype, and verify on the target hardware.

The development impact

Shorten the path from idea to deployment.

Spend less time preparing data, building tools, and repeating setup. Put more time into testing ideas and improving your product.

Compress the repetitive work. Preserve human judgment.

Scroll horizontally to compare the full development cycle →

Conventional development

More manual preparation and repeated work

AI-assisted development

Less repetitive work; more focus on your product

More time for your product.
Earlier delivery.

Time protected for product development and quality. Agents can assist throughout; you guide the design, implementation, and release.

Developer time

Less repetitive setup, scripting, data preparation, and experiment orchestration. More attention for design and difficult problems.

Time to market

Test assumptions earlier with working prototypes, reusable tools, and faster model evaluation cycles.

Total cost of ownership

Engineering effort is one part of TCO. Evaluate it alongside hardware, agent and compute costs, validation, deployment, and ongoing support.

Choose your coding agent

Your Jetson. Your coding agent.

The workflow is agent-agnostic. Start with the recorded Codex example; additional assistant walkthroughs are planned.

Codex Jetson Setup · 7 steps

Get your Jetson ready.

Watch a Mac connect to a Jetson Orin Nano Developer Kit, prepare Codex, install Jetson Device Skills, and finish GPU container setup.

Before the first prompt, install the ChatGPT desktop app on your host and sign in with Codex access. The recording uses macOS; UI and connection details may differ on other hosts.

  1. Begin in a Mac-side task: connect, authenticate, and configure SSH.
  2. Install Codex CLI on Jetson and register its remote project.
  3. Inspect the device, add Skills, and prepare Docker with approval.
Try the 7 setup prompts ↓ Already set up? Explore the examples ↓
Watch on YouTube ↗

Directly on Jetson

Install and sign in to Codex CLI on Jetson, then run it from your project directory in a local terminal or SSH session. The agent's commands and files are on the device.

From your desktop app

Use a host-side task for initial setup over SSH. Then add the Jetson in Settings > Connections in the ChatGPT desktop app and choose a remote project folder. Start the application task there.

Keep the host and Jetson awake and connected during this walkthrough. For unattended workloads, configure and test persistent services explicitly; an open SSH session alone is not a persistence guarantee.

Codex CLI setup ↗ SSH remote projects ↗ Jetson Agent Skills →

Companion walkthrough planned

Claude Code

A step-by-step Jetson example is not yet available here. The shared approach stays the same: provide device context, review the plan, run bounded experiments, and validate the result. Setup and available integrations depend on the assistant.

Explore Jetson Agent Skills

Companion walkthrough planned

Cursor

A step-by-step Jetson example is not yet available here. The shared approach stays the same: provide device context, review the plan, run bounded experiments, and validate the result. Setup and available integrations depend on the assistant.

Explore Jetson Agent Skills

Companion walkthrough planned

Devin

A step-by-step Jetson example is not yet available here. The shared approach stays the same: provide device context, review the plan, run bounded experiments, and validate the result. Setup and available integrations depend on the assistant.

Explore Jetson Agent Skills

Companion walkthrough planned

Local coding LLMs

A step-by-step Jetson example is not yet available here. The shared approach stays the same: provide device context, review the plan, run bounded experiments, and validate the result. Setup and available integrations depend on the assistant.

Explore Jetson Agent Skills

Try it yourself · Codex Jetson Setup

Ask your agent to help with the setup.

These seven prompts accompany the setup video. Adapt the user name and connection to your device. They are starting points, not unattended scripts.

Before you start: device, access, and approval checklist

Device and connection

  • Complete Jetson's first-run setup and reboot. Keep a local display and keyboard available.
  • Use a data-capable cable and the USB device-mode port, or connect by Ethernet/Wi-Fi. Seeing the L4T-README drive does not establish SSH connectivity.
  • Keep DHCP automatic. On macOS, allow the USB accessory and check the app's Local Network permission if Terminal can connect but the app cannot.
  • The recorded R39.2.1 setup used a USB-network workaround before recording. If USB networking fails, resolve it first or use Ethernet; the prompts are not a universal USB repair procedure.

You stay in control

  • Use a least-privilege account. Verify host identity, preserve existing SSH keys, and investigate changed-key warnings.
  • Enter passwords and sign-in codes locally. Never commit credentials or include them in shared prompts.
  • Read proposed commands and review downloads, service changes, and reboots before approval.
  • Prompt wording is not a security boundary. Configure the agent's actual permissions and verify what it changed.
Need to set up your developer kit first?
Try it yourself: 7 setup steps

Connect and prepare your Jetson

Same host-side setup task
Step 1Connect to your Jetson

Recorded on macOS over USB. Replace “Mac” and the login name for your setup. Confirm the intended device before accepting an SSH host key; investigate changed-key warnings.

Adapt, review, then send 
Connect this Mac to my USB-connected Jetson and set up key-based SSH.

1. Guide my first login as jetson in this app's terminal. If using screen, guide login and cleanup.
2. Use jon-xxxx for the SSH alias and HostKeyAlias, using the last four hex digits of the built-in Ethernet NIC's permanent MAC. Use a dedicated SSH key.
3. Confirm access works. Keep DHCP automatic; Wi-Fi is next.

Step 2Give Jetson its own internet connection

Skip this step if the Jetson already has independent internet access. Do not paste Wi-Fi passwords into chat or project files.

Adapt, review, then send 
Connect the Jetson to its own Wi-Fi, keeping USB SSH working.

Guide me to enter the SSID and password locally, not in this chat. Enable automatic reconnection and verify internet access without this Mac.

Keep credentials only in the protected Wi-Fi profile.

Step 3Prepare Codex on the Jetson

Follow the official installation and sign-in flow. Enter passwords and authorization codes only in the appropriate local terminal or sign-in page.

Adapt, review, then send 
Prepare this Jetson for a Codex remote project.

Use our SSH connection to install Codex CLI if needed, guide sign-in, and prepare ~/jetson-ai-demo.

Connection registration is next.

Step 4Add the remote project

In the ChatGPT desktop app, SSH hosts are managed in Settings > Connections. Register the project, but keep this setup conversation on your Mac through step 7.

Adapt, review, then send 
Add this Jetson's SSH connection and ~/jetson-ai-demo as a remote project in this app. Guide any required clicks.

Finish when it appears under Projects. Keep this task on the Mac.

Step 5Establish a read-only baseline

Understand the actual device before changing it. Missing host AI libraries are not necessarily blockers when the application uses containers.

Adapt, review, then send 
Create a baseline for this Jetson's container-based VLM demo.

Summarize hardware, software versions, memory, storage, and GPU container readiness with the desktop running. Focus on actual deployment blockers, not missing host AI libraries.

Read-only; no downloads or changes.

Step 6Add Jetson Device Skills

Install on the Jetson, not the Mac. The Jetson Agent Skills guide covers installation paths and the full catalog. Start a fresh Jetson-side task for the application walkthrough.

Adapt, review, then send 
Install NVIDIA Jetson Device Skills from the official repository for the jetson user's Codex on the Jetson, not this Mac.

Confirm they are installed in the correct location.

Step 7Finish GPU container setup

Review downloads and system changes before approving. If Docker group membership changes, verify access from a fresh remote session before starting the application.

Adapt, review, then send 
Read the Jetson Device Skills over SSH and finish this Jetson's Docker setup for GPU-accelerated AI demos.

Propose only missing setup, then apply after approval. No full JetPack SDK; swap only if needed. Use a small compatible container to test GPU access.

Keep the desktop and USB SSH working.

Built with AI assistance

Bring your ideas to life on Jetson.

Build a new application with a coding agent, or turn an existing application into a standalone Jetson demo. These are two independent examples; choose the outcome you want to explore.

Build a new application

Build something new.

Watch Codex help build a YOLO + Gemma4 demo on Jetson Orin Nano using existing models and tools.

Watch on YouTube ↗

From an idea to an application

Focus on application development: describe the behavior you want, work with the agent on the implementation, and review the result on real hardware.

Explore the build process in the video. The standalone example alongside it uses a different application; it is not a required next step.

Run standalone

Make it run standalone.

Use Codex to deploy Live VLM WebUI, connect a USB camera, and make the Jetson start its fullscreen demo on boot, without the host PC.

Watch on YouTube ↗

From an existing app to a standalone device

Reuse prebuilt containers, a local Gemma4 E2B runtime, and upstream kiosk support. The finished demo runs independently of the host-side coding session.

Try it yourself: 3 kiosk steps

Codex Kiosk Demo Build · New task in the Jetson remote project

Start with a prepared Jetson. Connect its webcam and display, verify GPU container access in the new session, and keep these three prompts in the same task.

Step 1Plan a live VLM application

Start a new task inside the Jetson remote project. Attach the USB webcam and display to Jetson first. Check the selected runtime against your Jetson model and software release.

Adapt, review, then send 
Plan a live VLM demo for this Jetson using an existing app and prebuilt containers.

- USB webcam and display attached to the Jetson; browser capture is fine
- Inference on the Jetson; localhost access only

Find an app through Jetson AI Lab. Use https://www.jetson-ai-lab.com/models/gemma4-e2b/ for a lightweight, vision-capable setup that fits alongside the desktop.

Show the plan and download sizes before deploying.

Step 2Deploy and check real camera inference

Approve the plan first. Use an image that includes the required upstream features. Five minutes is a smoke test, not proof of production reliability.

Adapt, review, then send 
Deploy the approved VLM demo.

Show working captions from the Jetson's USB webcam in its browser. Check GPU acceleration, latency, and memory stability for five minutes.

Use the existing WebUI; no custom UI or Mac preview.

Step 3Make it a standalone kiosk

Use a dedicated demo account. Review auto-login and startup services, keep rollback instructions, and shut down safely before cycling power. Verify fresh captions, not just an open browser.

Adapt, review, then send 
Make this Jetson boot directly into the fullscreen VLM demo, with camera capture and inference running automatically, without this Mac or internet.

Reuse upstream kiosk support. Ask before enabling desktop auto-login or rebooting.

Guide a reboot and safe power-off/on test, confirm fresh captions without clicks, and leave rollback instructions.

Review the plan, check fresh captions, and verify reboot and offline startup. A working demo still needs product-specific validation before deployment.

Live VLM WebUI guide → Gemma4 E2B setup → Upstream kiosk guide ↗

Bring your AI into the physical world.

Connect cameras and sensors. Build, test, and refine on real Jetson hardware.

Try the first prompt ↑