chore: Regenerate all playbooks

nvidia/station-nanochat/README.md

@@ -77,7 +77,7 @@ All required assets are in `nvidia/station-nanochat/assets/`:
 
 ## Time & risk
 
-- **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 16+ hours on a single GB300 Ultra.
+- **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 12+ hours on a single GB300 Ultra.
 - **Risk level:** Medium
   - Large downloads (FineWeb) can be slow; ensure stable network and disk space.
   - API keys (W&B, HF) must be set or `launch.sh` will exit immediately.
@@ -149,7 +149,7 @@ The training runs inside the `nanochat` container and executes the full pipeline
 3. **SFT** — downloads synthetic identity conversations, fine-tunes for chat
 4. **Report generation** — produces `report.md` with metrics and samples
 
-Training on a single GB300 Ultra takes on the order of 16+ hours for the full d24 run.
+Training on a single GB300 Ultra takes on the order of 12+ hours for the full d24 run.
 
 ## Step 4. Monitor training
 

nvidia/station-nanochat/assets/Dockerfile

@@ -10,6 +10,8 @@ RUN pip install \
     psutil \
     files-to-prompt \
     uvicorn \
+    fastapi \
+    regex \
     rustbpe
 
 CMD ["/bin/bash"]

nvidia/station-nanochat/endpoint-test.yaml

@@ -107,7 +107,7 @@ spec:
         
         # Time & risk
         
-        - **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 16+ hours on a single GB300 Ultra.
+        - **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 12+ hours on a single GB300 Ultra.
         - **Risk level:** Medium
           - Large downloads (FineWeb) can be slow; ensure stable network and disk space.
           - API keys (W&B, HF) must be set or `launch.sh` will exit immediately.
@@ -184,7 +184,7 @@ spec:
         3. **SFT** — downloads synthetic identity conversations, fine-tunes for chat
         4. **Report generation** — produces `report.md` with metrics and samples
         
-        Training on a single GB300 Ultra takes on the order of 16+ hours for the full d24 run.
+        Training on a single GB300 Ultra takes on the order of 12+ hours for the full d24 run.
         
         # Step 4. Monitor training
         

nvidia/station-nemoclaw/endpoint-production.yaml

@@ -130,8 +130,8 @@ spec:
         
         - **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
+        - **Last Updated:** 06/01/2026
-          - Update to latest nemoclaw installer instructions
+          - Pin nemoclaw installer to v0.0.55, the latest stable version
         
       
 
@@ -144,10 +144,10 @@ spec:
         
         ## 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:
@@ -165,7 +165,7 @@ spec:
         During custom setup, the onboard wizard walks you through:
         
         1. **Configuring inference** -- Choose to set up local inference on your DGX Station by selecting **`7) Local Ollama`**.
-        2. **Ollama models** -- Choose desired inference model. If no model is present locally, the installer will provide options to download models to start.
+        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.
@@ -341,7 +341,7 @@ spec:
         
         The cloudflared tunnel provides a **public URL for the Web UI dashboard** — it is not related to Telegram messaging.
         
-        Install cloudflared (DGX Station is arm64):
+        Install cloudflared (DGX Station is aarch64):
         
         ```bash
         curl -L --output cloudflared.deb \
@@ -371,7 +371,7 @@ spec:
         
         Set up NemoClaw Agents in general require three steps: Configure NemoClaw security policy, Run Agent Workflow Prompt, Personalize the Workflow for your own use case.
         
-        Checkout these [Example NemoClaw Agents](https://build.nvidia.com/station/nemoclaw-applications) for reference. Consider sharing your NemoClaw agent setup with the community at [DGX Station Developer Forum](https://forums.developer.nvidia.com/c/accelerated-computing/dgx-station-gb300)
+        Checkout these [Example NemoClaw Agents](https://build.nvidia.com/spark/nemoclaw-applications) for reference.
         
         ---