things

Get-VideoResolution.ps1

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+$ffprobe = 'D:\yt-dlp\ffprobe.exe'
+$FolderToReview = 'Q:\Web-RegulationPodcast\Unsorted'
+
+if (-not (Test-Path $ffprobe)) {
+    Write-Error "ffprobe.exe not found at '$ffprobe'. Please verify the path."
+    exit 1
+}
+
+$results = Get-ChildItem -Path $FolderToReview -Recurse -Filter *.mp4 | ForEach-Object {
+    $file = $_.FullName
+    Write-Verbose "Processing: $file" -Verbose
+
+    # Run ffprobe, suppress stderr, capture stdout
+    $rawOutput = & $ffprobe -v error `
+        -select_streams v:0 `
+        -show_entries stream=width,height `
+        -of csv=p=0:s=x `
+        $file 2>$null
+
+    # Clean up output and handle missing/invalid streams
+    $resolution = $rawOutput.Trim()
+    if ([string]::IsNullOrWhiteSpace($resolution)) {
+        $resolution = 'No video stream or unreadable'
+    }
+
+    [PSCustomObject]@{
+        File       = $file
+        Resolution = $resolution
+    }
+}
+
+$results | Format-Table -AutoSize
+
+# Optional: Export to CSV
+# $results | Export-Csv -Path 'Q:\Web-RegulationPodcast\video_resolutions.csv' -NoTypeInformation

_Notes/AI_Project_1.md

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+# 🎙️ Project Summary: Self-Hosted High-Performance AI Assistant
+
+### 🎯 The Ultimate Goal
+To build a fully local, privacy-focused smart speaker/home automation engine that uses high-end hardware to achieve near-instantaneous response times (low latency) without relying on cloud-based processing.
+
+### 🏗️ The Software Pipeline (The "Stack")
+1.  **Wake Word Detection:** Porcupine/Picovoice (Running on CPU, acting as the gatekeeper).
+2.  **ASR (Speech-to-Text):** `faster-whisper` (Running on **RTX 3080 Ti via CUDA** for high-speed transcription).
+3.  **NLU (The Brain):** Local LLM via **O/llama** (e.g., Llama 3 or Mistral) to parse intent from text into JSON commands.
+4.  **Execution Layer:** **Home Assistant** (Receiving JSON webhooks to trigger physical smart home devices).
+
+### 🖥️ The Hardware & Infrastructure
+*   **Host Hypervisor:** Proxmox VE.
+*   **Physical Resources:** 32GB System RAM, NVIDIA RTX 3080 Ti (12GB VRAM), Intel CPU with VT-d enabled.
+*   **The "Brain" VM Configuration:**
+    *   **OS:** Ubuntu 24.04 LTS (Regular version).
+    *   **CPU:** 1 Socket, 4 Cores, **Type: Host** (Crucial for AI instructions).
+    *   **Memory:** 16GB RAM, **KSM and Ballooning disabled** (To ensure stability and prevent latency jitter).
+    *   **Storage:** VirtIO SCSI Single controller using `io_uring` for high-performance asynchronous I/O.
+    *   **GPU Passthrough:** Completed. The GPU is isolated from Proxmox using `vfio-pci` (bypassing the `nouveau` driver) and passed directly to the Ubuntu VM.
+    *   **Networking:** VirtIO (paravirtualized) for low-latency communication.
+
+### 🚩 Current Progress & Status
+*   [x] Proxmox IOMMU/VT-d configuration finalized.
+*   [x] GPU Isolation and VFIO configuration completed.
+*   [x] Ubuntu VM creation and storage architecture (LVM) finalized.
+*   [x] Ubuntu installation completed.
+*   [ ] **CURRENT TASK:** Post-install "Day Zero" tasks: SSH access, system updates, and installing NVIDIA Drivers + Docker + NVIDIA Container Toolkit.
+
+***
+**Instructions for New Chat:** 
+Paste this block into a new chat and say: *"I am working on the project described in this summary. I have finished the installation and am ready to begin the 'Day Zero' tasks."*

_Notes/AI_Project_2.md

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+# Project Context: Self-Hosted AI Smart Speaker (The "Brain" Project)
+
+**Role for AI:** You are an expert Linux System Administrator and AI Engineer. We are building a high-performance, local-first smart speaker system designed to replace cloud assistants with 100% private, GPU-accelerated intelligence.
+
+## 🏗️ Hardware Environment
+* **Hypervisor:** Proxmox VE.
+* **Physical Server:** High-performance build with 32GB System RAM.
+* **GPU:** NVIDIA GeForce RTX 3080 Ti (12GB VRAM).
+* **I/O Configuration:** Intel VT-d enabled; `intel_iommu=on` configured in GRUB.
+
+## 🐧 Virtual Machine Architecture (The "Brain" VM)
+* **Guest OS:** Ubuntu 24.04 LTS (Noble Numbat).
+* **BIOS/Firmware:** SeaBIOS (Chosen specifically to bypass UEFI/Secure Boot/MOK signature complexities for NVIDIA drivers).
+* **CPU Configuration:** 1 Socket, 4 Cores, Type: `host` (for maximum instruction set compatibility).
+* **Memory:** 16GB RAM, Ballooning and KSM disabled (to ensure deterministic performance for AI workloads).
+* **Storage/Disk:** VirtIO SCSI Single controller; LVM-based disk management with expansion capability.
+* **Networking:** VirtIO (paravirtualized) for low-latency communication.
+
+## 🛠️ Software Stack & Completed Milestones
+1. **GPU Passthrough:** Successfully isolated the RTX 3080 Ti from Proxmox using `vfio-pci` and assigned it to the Ubuntu VM via PCI Passthrough.
+2. **Driver Layer:** Installed NVIDIA Driver version `580.126.09` (and CUDA 13.0) directly on the Ubuntu Guest.
+3. **Containerization:** Docker Engine installed.
+4. **The "Bridge" (Crucial):** Successfully configured the **NVIDIA Container Toolkit**. 
+   * *Note:* We had to use a workaround for Ubuntu 24.04 by pointing the `apt` repository to the `ubuntu22.04` stable path because the `noble` path was missing/broken on NVIDIA's servers.
+5. **Orchestration:** Deployed a `docker-compose` stack containing:
+   * **Ollama:** Running as the LLM engine (GPU-accelerated).
+   * **Open WebUI:** Running as the frontend interface for text-based testing.
+
+## 🎯 Current Objective & Next Steps
+We have successfully verified that `nvidia-smi` works inside a Docker container. The "Text-to-Text" pipeline is functional and running on the RTX 3080 Ti.
+
+**The next phases are:**
+1. **Phase 7 (Audio Input):** Integrating a microphone array/stream into the Linux environment.
+2. **Phase 8 (ASR - The Ears):** Deploying `faster-whisper` in a Docker container to transcribe audio to text.
+3. **Phase 9 (The Logic):** Writing the Python "Glue" code to pipe audio from the mic $\rightarrow$ Whisper $\rightarrow$ Ollama $\rightarrow$ Home Assistant API for automation execution.
+
+**Current Task:** Verify the text-based interaction in Open WebUI and begin planning the integration of the Whisper ASR engine.