4dd8003f25
10-point security hardening section: - Known risks table (DM policy, WebSocket, prompt injection, tool exec) - Gateway config hardening (loopback bind, pairing, disable system.run) - Windows Firewall rules (block external, allow localhost only) - WSL2 hardening (UFW, file permissions, no root, disable SSH) - Network architecture diagram (Tailscale Serve, no port forwarding) - API key security (OAuth preferred, rotation, no git commits) - Prompt injection defense (disable browser/system tools, per-channel pairing) - Monitoring & audit cron script - Backup & recovery commands - 14-point pre-launch security checklist |
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|---|---|---|
| .. | ||
| capabilities | ||
| dell-P16s-windows-spec.md | ||
| hp-z240-windows-spec.md | ||
| mac-vs-windows-comparison.md | ||
| openclaw-personal-ai-assistant.md | ||
| razer-blade-18-spec.md | ||
| README.md | ||
| setup-guide.md | ||
| setup-windows.ps1 | ||
| setup-wsl.sh | ||
Windows Setup — Local LLM Stack
Two scripts. Zero IDE required. Fresh machine to running dashboard in ~30 minutes.
Quick Start
Step 1: Windows Side (PowerShell as Admin)
# Allow script execution for this session
Set-ExecutionPolicy -Scope Process Bypass
# Run the Windows setup (installs Ollama, pulls models, installs WSL2)
.\setup-windows.ps1
What it does: Verifies NVIDIA drivers, installs Ollama, pulls 5 models (~52 GB), installs WSL2 Ubuntu 24.04.
After WSL2 install you may need to reboot. Ubuntu will ask for a username/password on first launch.
Step 2: WSL2 Side (Ubuntu terminal)
# One-liner — downloads and runs the WSL2 setup script
curl -fsSL https://raw.githubusercontent.com/saravanakumardb1/learning_ai_common_plat/main/__LOCAL_LLMs/windows_specific/setup-wsl.sh | bash
What it does: Installs Node.js, Python, ffmpeg, cmake → builds Whisper.cpp with CUDA → sets up TTS → starts the dashboard.
Step 3: Open Browser
http://localhost:3000
Dashboard should show all green. Done.
What Gets Installed
| Component | Where | Size |
|---|---|---|
| NVIDIA drivers | Windows | pre-installed |
| Ollama | Windows (native) | ~200 MB |
| 5 LLM models | Windows (%USERPROFILE%\.ollama\) |
~52 GB |
| WSL2 Ubuntu 24.04 | Windows | ~1 GB |
| Node.js 20 LTS | WSL2 | ~50 MB |
| Python 3.12 + venv | WSL2 | ~200 MB |
| whisper.cpp (CUDA) | WSL2 /usr/local/bin/ |
~50 MB |
| Whisper model | WSL2 ~/whisper-models/ |
~1.5 GB |
| SNAC decoder | WSL2 (repo models/) |
~76 MB |
| Qwen3-TTS 0.6B | WSL2 (repo models/) |
~1.7 GB |
| PyTorch CUDA | WSL2 (.venv-qwen-tts/) |
~2.5 GB |
| Dashboard deps | WSL2 (dashboard/node_modules/) |
~200 MB |
Total: ~60 GB (mostly Ollama models)
Files in This Directory
| File | Purpose |
|---|---|
| README.md | This file — quick start guide |
| setup-windows.ps1 | PowerShell script — Windows-side setup |
| setup-wsl.sh | Bash script — WSL2-side setup |
| setup-guide.md | Detailed manual guide with troubleshooting |
| razer-blade-18-spec.md | Full hardware specs for the Razer Blade 18 |
After Setup
# Daily usage — start everything
cd ~/code/mygh/learning_ai_common_plat/__LOCAL_LLMs
bash start-dashboard.sh
# Check status
bash start-dashboard.sh status
# Stop
bash start-dashboard.sh stop
# Test TTS
.venv-qwen-tts/bin/python test_orpheus_tts.py
.venv-qwen-tts/bin/python test_qwen_tts.py
Troubleshooting
See setup-guide.md for common issues:
- Ollama not accessible from WSL2
- CUDA not visible in WSL2
- Slow filesystem performance