Merge 48fc5eb30e into b849d2d191

nvidia/nemoclaw/README.md

@@ -118,8 +118,8 @@ All required assets are handled by the NemoClaw installer. No manual cloning is
 
 - **Estimated time:** About 30–60 minutes for a first full pass (install, onboard, model download depending on choice and network). Optional Brave, Telegram, and cloudflared steps add time if you do them in a second session.
 - **Risk level:** Medium — you are running an AI agent in a sandbox; risks are reduced by isolation but not eliminated. Use a clean environment and do not connect sensitive data or production accounts.
-- **Last Updated:** 05/29/2026
-  - Update to latest nemoclaw installer instructions
+- **Last Updated:** 06/01/2026
+  - Pin nemoclaw installer to v0.0.55, the latest stable version
 
 ## Instructions
 
@@ -127,10 +127,10 @@ All required assets are handled by the NemoClaw installer. No manual cloning is
 
 ### Step 1. Install NemoClaw
 
-This single command handles everything: installs Node.js (if needed), installs OpenShell, clones the pinned NemoClaw **v0.55** release (set via `NEMOCLAW_VERSION`; v0.55 is the version the NemoClaw team currently recommends as the most stable), builds the CLI, and runs the onboard wizard to create a sandbox.
+This single command handles everything: installs Node.js (if needed), installs OpenShell, clones the pinned NemoClaw **v0.0.55** release (set via `NEMOCLAW_INSTALL_TAG`; v0.0.55 is the version the NemoClaw team currently recommends as the most stable), builds the CLI, and runs the onboard wizard to create a sandbox.
 
 ```bash
-curl -fsSL https://www.nvidia.com/nemoclaw.sh | NEMOCLAW_VERSION=v0.55 bash
+curl -fsSL https://www.nvidia.com/nemoclaw.sh | NEMOCLAW_INSTALL_TAG=v0.0.55 bash
 ```
 
 The installation wizard walks you through setup:
@@ -148,7 +148,7 @@ The installer requires **Node.js 22.16+** (installed automatically if missing).
 During custom setup, the onboard wizard walks you through:
 
 1. **Configuring inference** -- Choose to set up local inference on your Spark by selecting **`7) Local Ollama`**.
-2. **Ollama models** -- Choose desired inference model. If no model is present locally, the installer will download **`qwen3:30b`** automatically.
+2. **Ollama models** -- Choose desired inference model. If no model is present locally, the installer will download **`qwen3.6:35b`** automatically.
 3. **Sandbox name** -- Pick a name (e.g. my-assistant). Each sandbox requires a unique name.
 4. **Apply this configuration** -- Enter `Y` to confirm setting up local inference.
 5. **Enable Brave Web Search** -- Optional. If you enable it, paste a [Brave Search API](https://brave.com/search/api/) key when prompted.

nvidia/vibe-coding/README.md

@@ -171,10 +171,12 @@ Add additional model entries for any other Ollama models you wish to host remote
 
 | Symptom | Cause | Fix |
 |---------|-------|-----|
-|Ollama not starting|GPU drivers may not be installed correctly|Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark.|
-|Continue can't connect over the network|Port 11434 may not be open or accessible|Run command `ss -tuln \| grep 11434`. If the output does not reflect ` tcp   LISTEN 0      4096               *:11434            *:*  `, go back to step 2 and run the ufw command.|
-|Continue can't detect a locally running Ollama model|Configuration not properly set or detected|Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf` file. If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly, add these lines to your `~/.bashrc` file.|
-|High memory usage|Model size too big|Confirm no other large models or containers are running with `nvidia-smi`. Use smaller models such as `gpt-oss:20b` for lightweight usage.|
+| **WiFi connection drops or becomes unreachable** (especially in headless mode) | Aggressive WiFi power-saving settings in NetworkManager | Edit `/etc/NetworkManager/conf.d/default-wifi-powersave-on.conf`, set `wifi.powersave = 2`, and run `sudo systemctl restart NetworkManager`. |
+| **Random reboots and "00" error code on the display** | Watchdog timer module (`sbsa_gwdt`) not loaded | Add `sbsa_gwdt` to `/etc/modules-load.d/watchdog.conf` and reboot to ensure the hardware watchdog is correctly managed by the kernel. |
+| Ollama not starting | GPU drivers may not be installed correctly | Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark. |
+| Continue can't connect over the network | Port 11434 may not be open or accessible | Run command `ss -tuln \| grep 11434`. If the output does not reflect `tcp LISTEN 0 4096 *:11434 *:*`, go back to step 2 and run the ufw command. |
+| Continue can't detect a locally running Ollama model | Configuration not properly set or detected | Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf` file. If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly, add these lines to your `~/.bashrc` file. |
+| High memory usage | Model size too big | Confirm no other large models or containers are running with `nvidia-smi`. Use smaller models such as `gpt-oss:20b` for lightweight usage. |
 
 > [!NOTE]
 > DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.