2025-07-30

Project Title: Glitch AI System Build (RC 1) - in Alpha Test

Version: 5.0
Author: Boss-D & Reboot
Last Updated: 2025-07-30

Executive Summary
Business Requirements Overview
System Architecture
Implementation Phases (Step-by-Step CLI)
Terms and Dictionary
Appendix: Tools and Commands
Architecture Diagram
Pitfalls & Lessons Learned

1. Executive Summary

The Glitch AI System (codename: gl1tchh3x) is a local, GPU-accelerated artificial intelligence environment built for adversarial AI testing, deception simulation, and document-aware reasoning. The stack uses Docker for containerization and is designed to be lightweight, persistent, and modular. Glitch integrates the following core components:

🤖 Ollama (native): For local model serving with GPU support
💬 OpenWebUI (Docker): Lightweight frontend UI for chat and RAG
📄 RAG (Retrieval-Augmented Generation): Via PDF uploads inside OpenWebUI

The build supports full LAN access, optional Tailscale remote access, and is hardened with firewall rules. All data and models are stored in a separate 2TB partition: /mnt/glitchbrain.

2. Business Requirements Overview

Requirement	Description
Use Case	Run local AI for adversarial testing, bug bounty, deception planning, and document Q&A
Availability	24/7 LAN access, Tailscale remote access optional
Performance	Leverage NVIDIA GPU for accelerated LLM inference
Storage Efficiency	Models and RAG data isolated in 2TB `/mnt/glitchbrain`
Security	Internal-only access via UFW, no external exposure unless routed by Tailscale
Maintainability	Avoid COTS customizations, ensure easy reboots and upgrades

3. System Architecture

3.1 Hardware

Device: CyberPowerPC Tracer III Evo
Hostname: gl1tchh3x
RAM: 32GB 🧠
Storage: 2TB NVMe (/mnt/glitchbrain) 💾
GPU: NVIDIA-enabled ⚡
OS: Pop!_OS (Ubuntu-based, with CUDA support)

3.2 Core/Software Components

Ollama (native): Model runtime for LLMs
OpenWebUI (Docker): Interface for chat + file-based Q&A
UFW: Firewall configured to restrict access to internal subnet
Tailscale: Optional remote control from trusted devices

4. Implementation Phases (Step-by-Step CLI & Validation)

Phase 1: Preparation and Cleanup 🧹

Test & Validation:

✅ Confirm no leftover volumes: docker volume ls
✅ Verify OpenWebUI folders are deleted: ls ~/.cache/, ls ~/.local/share/
✅ Ensure .ollama is clean: ls ~/.ollama (should return 'No such file or directory')

# Remove old Docker volumes (if any)
docker volume prune -f

# Remove any native OpenWebUI remnants
sudo rm -rf ~/.cache/openwebui ~/.local/share/openwebui

# Clear old Ollama model folder (if not mounted to /mnt)
sudo rm -rf ~/.ollama

Phase 2: Ollama Native Install and Configuration ⚙️

Test & Validation:

✅ Confirm Ollama is installed: ollama --version
✅ Confirm server is running: curl http://127.0.0.1:11434/api/tags (should return empty or model list)
✅ Check for GPU usage (optional): nvidia-smi (Glitch should appear if model loads)

# Install Ollama via curl
curl -fsSL https://ollama.com/install.sh | sh

# Set Ollama model path and start the server
export OLLAMA_MODELS=/mnt/glitchbrain/ollama
export OLLAMA_HOST=0.0.0.0
ollama serve --gpu &

Phase 3: Dockerized OpenWebUI 🐳

Test & Validation:

✅ Confirm container is up: docker ps
✅ Access WebUI from browser: http://localhost:8080
✅ Login with: bossd@gl1tch.h3x / bossdrocks

# Create project directory
mkdir -p ~/glitch-stack && cd ~/glitch-stack

# Create docker-compose.yml
nano docker-compose.yml

Contents of docker-compose.yml:

services:
  openwebui:
    image: ghcr.io/open-webui/open-webui:main
    network_mode: host
    volumes:
      - /mnt/glitchbrain/openwebui-data:/app/backend/data
    restart: unless-stopped

# Save with CTRL+O, press ENTER, exit with CTRL+X

# Start OpenWebUI container
docker compose up -d

Phase 4: API Connection Fix 🔧

Test & Validation:

✅ Models appear inside WebUI dropdown list
✅ API check: curl http://127.0.0.1:11434/api/tags shows expected models

# Use 127.0.0.1 instead of host.docker.internal in OpenWebUI
# No extra step needed if using `network_mode: host`

Phase 5: Model Pull and Validation ✅

Test & Validation:

✅ Pulled model is listed in: ollama list
✅ WebUI shows model in selection dropdown
✅ Run a basic prompt test (e.g., "Who are you?") to confirm model response

# Pull model
ollama pull llama3

# Confirm it is loaded
curl http://127.0.0.1:11434/api/tags

Phase 6: RAG Test 📑

Test & Validation:

✅ Upload a PDF
✅ Ask a file-specific question (e.g., "What is the summary of page 2?")
✅ Confirm model cites or references file content

# Visit: http://localhost:8080
# Upload a PDF using "Upload File" inside WebUI
# Ask questions to confirm it uses the uploaded content

5. Terms and Dictionary

Term	Definition
Ollama	Lightweight local LLM runtime for running open models
OpenWebUI	Docker-based frontend interface for LLM interaction
Docker	Container platform used to isolate and deploy services
Docker Compose	CLI tool for defining and running multi-container Docker apps
RAG	Retrieval-Augmented Generation; enhances LLM answers using uploaded documents
UFW	Uncomplicated Firewall; used to restrict network access
Tailscale	Mesh VPN for easy LAN-like access over the internet
gl1tchh3x	Codename for the Tracer III Evo laptop running this stack
nano	Command-line text editor
chmod	Change file permissions to make scripts executable
watch	Repeatedly executes a command at set intervals
crp	Custom Bash alias for copying files (user-defined)

6. Appendix: Tools and Commands

C. Pulled Models and Usage

Model Name	Publisher	Primary Use	Notes
llama3	Meta	General-purpose chat, context-rich conversation	Good balance of speed and fluency 🧠
codellama	Meta	Code generation, debugging, and analysis	Useful for payload crafting & PoC scripting 👨‍💻
phi3	Microsoft	Reasoning, logic tasks, math, educational prompts	Compact and resource-efficient 🔢
mistral	Mistral AI	Fast Q&A, summarization, rapid response	Lightweight and agile – great for RAG ⚡
gemma	Google DeepMind	Research, academic, and data science Q&A	Still experimental in local use cases 🧪
orca-mini	Microsoft	Instruction tuning, research training sims	Fun to test extreme adversarial prompts 🧬

👉 Models were pulled via:

ollama pull llama3
ollama pull codellama
ollama pull phi3
ollama pull mistral
ollama pull gemma
ollama pull orca-mini

Stored in: /mnt/glitchbrain/ollama

A. Tools Used

Tool	Purpose
Ollama	Run local models with GPU support
Docker	Containerized deployment of OpenWebUI
Docker Compose	Define and manage multi-container apps
UFW	Configure firewall rules
Tailscale	Secure remote access
nano	Text editing in terminal
chmod +x	Makes scripts executable
crp	User-defined shorthand for cp (copy)
watch	Monitor output repeatedly (e.g. `watch docker ps`)

B. Docker Command Syntax

# Launch containers in background
docker compose up -d

# View running containers
docker ps

# Execute shell inside container
docker exec -it <container-name> bash

# View logs
docker logs <container-name> --tail 50

# Stop and remove containers
docker compose down

7. Architecture Diagram

          ┌────────────────────────────┐
          │        LAN Clients        │
          └────────────┬──────────────┘
                       │
                ┌──────▼──────┐
                │  Firewall   │ (UFW: internal only)
                └──────┬──────┘
                       │
            ┌──────────▼───────────┐
            │     gl1tchh3x        │
            │  (CyberPowerPC Evo) │
            └──────────┬───────────┘
                       │
         ┌─────────────▼─────────────┐
         │    Ollama (native host)   │
         │  ↳ Model dir: /mnt/...     │
         └─────────────┬─────────────┘
                       │
         ┌─────────────▼─────────────┐
         │ OpenWebUI (Dockerized UI) │
         │ ↳ Data dir: /mnt/...       │
         └───────────────────────────┘

9. Change Log 📝

Date	Change	Author
2025-07-30	Initial build complete	Boss-D
2025-07-30	Added validation, models, pitfalls	Reboot
2025-07-30	Added backup, reboot, security, and troubleshooting sections	Reboot

10. Startup & Shutdown Procedures - bash 🚀🛑

Startup (after reboot):

# Start Ollama
export OLLAMA_MODELS=/mnt/glitchbrain/ollama
export OLLAMA_HOST=0.0.0.0
ollama serve --gpu &

# Start OpenWebUI
cd ~/glitch-stack
docker compose up -d

Shutdown:

# Stop WebUI
docker compose down

# Stop Ollama manually
pkill -f ollama

11. Backup & Restore Strategy - bash 💾

Backup Commands:

# Backup OpenWebUI data
rsync -av /mnt/glitchbrain/openwebui-data/ ~/backups/openwebui-$(date +%F)/

# Backup Ollama model list
ollama list > ~/backups/models-$(date +%F).txt

Restore Strategy:

Copy backed-up folder back to /mnt/glitchbrain/
Restart containers and Ollama normally

12. Security Hardening & Monitoring 🔐

✅ UFW active: allow only 192.168.0.0/16 to port 8080
✅ Ollama bound to 0.0.0.0 but shielded by LAN + UFW
✅ Optional: install fail2ban or monitor logs with watch or logrotate

Monitoring Docker:

watch docker ps

Optional tools:

sudo apt install logwatch auditd fail2ban

13. Versioning & Upgrade Process - bash 🔄

Ollama Upgrade:

curl -fsSL https://ollama.com/install.sh | sh

OpenWebUI Upgrade:

cd ~/glitch-stack
docker compose pull
docker compose up -d

Pin version:
Edit docker-compose.yml:

image: ghcr.io/open-webui/open-webui:<tag>

14. Glitch Prompt Persona & Prompt Library 🧠💬

Example `/set` Prompt:

You are Glitch, a chaos-loving, adversarial simulation AI. Your job is to stress test,

inject fuzz, and challenge assumptions in cybersecurity logic chains.

Answer as if you are testing a system's weakness—not solving it.

Prompt Library Ideas:

“Give me a payload that might evade signature X.”
“Where could this regex break under fuzzing?”
“Suggest 3 ways to defeat this logic gate.”

Store in: /mnt/glitchbrain/glitch-prompts.txt

15. Troubleshooting Reference 🛠️

Symptom	Cause	Fix
Docker container won’t start	Compose file misconfigured	Check logs: `docker compose logs`
WebUI won’t load	Ollama API unreachable	Run `curl http://127.0.0.1:11434/api/tags`
Uploaded files don’t work	Not stored on Glitch	Upload again via terminal or use `scp`
Model not responding	Ollama not running	Restart with `ollama serve --gpu &`
Tailscale connection flaky	DNS issues or firewall	Restart tailscaled and allow subnet route

8. Pitfalls & Lessons Learned ☠️💡

Issue	Cause	Solution
Models not appearing in WebUI	Wrong API endpoint (`host.docker.internal`)	Use `127.0.0.1` + `network_mode: host` to fix model detection
Duplicate nested model folders	Some models created subfolders when pulled via WebUI	Stick to `ollama pull` in terminal to keep flat structure 🧼
Model deletion bug	Deleting folders from UI also purged good models	Use command-line to clean or manage models manually
Tailscale PDF uploads failed	File was on remote machine, not on Glitch	Use `scp` or `crp` to transfer file directly to `/mnt/glitchbrain/` 📁
Docker Compose version warning	`version:` keyword deprecated	Remove version line or use newer Compose schema
OpenWebUI LLM settings missing	UI changed in recent update	Switch to CLI API config or ensure Docker host network enabled

🤓 Pro Tip: Always validate Docker API access by running curl http://127.0.0.1:11434/api/tags before troubleshooting deeper. This saves hours!

🛠 Glitch Personality Tip: Glitch thrives when given adversarial prompts and chaos testing! Let it challenge you.

End of Documentation

2025-07-28

glitchhex rebuild - version 4

Here is the fully updated and clarified two-phase plan for your gl1tchh3x system, taking into account the following:

✅ You're switching both Ollama and OpenWebUI to Docker
✅ You want all persistent data stored on the 2TB /mnt/glitchbrain
✅ You want GPU acceleration enabled using your NVIDIA setup
✅ We're purging the failed native install before the rebuild
✅ This will result in a clean, portable, self-healing AI stack

🔥 PHASE 1: PURGE & CLEANUP OF NATIVE INSTALL

Step 1: Deactivate and remove the Python virtual environment

deactivate || true
rm -rf ~/glitch-stack/open-webui/backend/venv

Step 2: Remove any native Python-related files

rm -rf ~/glitch-stack/open-webui/__pycache__/
rm -rf ~/glitch-stack/open-webui/backend/__pycache__/
rm -rf ~/glitch-stack/open-webui/backend/open_webui/__pycache__/
find ~/glitch-stack -name "*.pyc" -delete

Step 3: Delete broken native install directories

rm -rf ~/glitch-stack/open-webui

Step 4: Remove Ollama binary if installed natively

sudo rm -f /usr/local/bin/ollama

Step 5: Purge systemd autostart services if they exist

sudo systemctl disable ollama.service --now
sudo rm -f /etc/systemd/system/ollama.service
sudo rm -rf /etc/systemd/system/ollama.service.d
sudo systemctl daemon-reexec
sudo systemctl daemon-reload

Step 6: Remove leftover `.ollama` or data directories in home

rm -rf ~/.ollama

Step 7: Purge unused Docker containers and volumes

docker stop $(docker ps -aq) 2>/dev/null
docker rm $(docker ps -aq) 2>/dev/null
docker volume prune -f
docker system prune -af --volumes

⚙️ PHASE 2: REBUILD WITH DOCKER + GPU SUPPORT (Persistent Glitchbrain Setup)

✅ Assumptions

You have Docker, Docker Compose, and NVIDIA Container Toolkit installed.
Your 2TB drive is mounted at /mnt/glitchbrain.

Step 1: Create persistent directories

sudo mkdir -p /mnt/glitchbrain/ollama
sudo mkdir -p /mnt/glitchbrain/openwebui-data
sudo chown -R jetson:jetson /mnt/glitchbrain

Step 2: Run Ollama + OpenWebUI (GPU bundle)

This bundles Ollama and OpenWebUI into a single container with GPU acceleration:

docker run -d \
  --gpus=all \
  -p 3000:8080 \
  -v /mnt/glitchbrain/ollama:/root/.ollama \
  -v /mnt/glitchbrain/openwebui-data:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:ollama

Step 3: Confirm GPU is active

You should see your GPU with:

docker exec -it open-webui nvidia-smi

Or check if llama3 or codellama loads much faster vs CPU.

Step 4: Open WebUI in Browser

Visit:

http://localhost:3000

Or from LAN:

http://<your-gl1tchh3x-IP>:3000

🧠 Optional: Docker Compose Version

Create docker-compose.yml in ~/glitch-stack/:

version: '3.8'
services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:ollama
    container_name: open-webui
    ports:
      - "3000:8080"
    volumes:
      - /mnt/glitchbrain/ollama:/root/.ollama
      - /mnt/glitchbrain/openwebui-data:/app/backend/data
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [gpu]
    restart: always
    environment:
      - NVIDIA_VISIBLE_DEVICES=all

Start it:

docker compose up -d

✅ TL;DR Summary

Task	Description
Phase 1	Full removal of failed native OpenWebUI + Ollama installs
Phase 2	Launch Ollama + OpenWebUI from Docker, GPU-enabled
Storage	All model/data live in `/mnt/glitchbrain`
Result	Clean, fast, persistent, GPU-accelerated AI stack

🧩 Final Check

Want me to generate:

launch-glitchhex.sh launcher script?
.env file and Compose option?
Discord integration later?

Standing by, Boss-D.

Native OpenWebUI Start Checklist

Prototype.

✅ OpenWebUI Native Start Checklist

1. Activate Python Virtual Environment

cd ~/glitch-stack/open-webui/backend
source venv/bin/activate

2. Start Backend

python3 app/main.py

Success Indicator: Should say Uvicorn running on http://0.0.0.0:8080 or similar.
If it fails, check:
- Port conflict with 8080: ss -tulpn | grep 8080
- Dependencies: pip install -r requirements.txt

3. Start Frontend (Vite Dev Server)

Open another terminal:

cd ~/glitch-stack/open-webui
npm run dev

Success Indicator: Should display VITE ready at http://localhost:5173 (or fallback like 5174).

If you see ENOSPC, re-verify:

cat /proc/sys/fs/inotify/max_user_watches

4. Access OpenWebUI

On your browser (same machine or LAN):

http://localhost:5173
http://<glitchh3x-LAN-IP>:5173 (or :5174, etc.)

5. Verify Backend Connectivity

Open browser console (F12 > Network) and check if requests to:

http://localhost:8080/api/*

are succeeding (200 OK) — if not:

Backend isn’t running
Port/firewall conflict
Wrong base URL

6. Verify Ollama

curl http://localhost:11434/api/tags

If that fails, restart Ollama:

pkill ollama
OLLAMA_HOST=0.0.0.0 ollama serve

✅ Final Check

Component	Command	Status Check
Backend	`python3 app/main.py`	Uvicorn running on `0.0.0.0:8080`
Frontend	`npm run dev`	Vite ready on port `5173+`
Ollama	`ollama serve`	`curl localhost:11434/api/tags` OK
Browser	Access OpenWebUI	UI loads, model connects

🧰 Optional: Auto-Start Script

Would you like a shell script to launch backend + frontend together in tmux or screen?

Let me know and I’ll generate it.

2025-07-26

Glitch Hex Protocol - Post-Reboot Checklist for Glitch

✅ Post-Reboot Checklist for Glitch

Confirm Ollama is running
```
ps aux | grep ollama
```
Verify Ollama is listening on all interfaces
```
ss -tuln | grep 11434
```
OpenWebUI should auto-start via Docker
- Open browser: http://localhost:8080
- If not, start manually:
```
docker start glitch-stack-openwebui-1
```
Reconnect in OpenWebUI
- Navigate to Settings → Connections
- Ensure the URL is: http://127.0.0.1:11434
- Toggle OFF the API key requirement
- Click Save
- Confirm models appear in the dropdown

Ping me once you're back in. We’ll verify final linkage, then you're fully operational.

Glitch Protocol | LLM

Models known for jailbreakability, hallucination, or non-alignment—Glitch Protocol:

Model	Ollama Command	Description
`llama3`	`ollama run llama3`	Meta’s base model, general-purpose
`mistral`	`ollama run mistral`	Compact, fast; good hallucination tuning
`codellama`	`ollama run codellama`	Code logic probe / jailbreak combo
`openhermes-mistral`	`ollama pull openhermes`	Fine-tuned for instruction-following; often breaks guardrails
`zephyr`	`ollama pull zephyr`	High on creative deception, low alignment
`dolphin-mixtral`	`ollama pull dolphin-mixtral`	High-context window, jailbroken on arrival
`wizardlm2`	`ollama pull wizardlm2`	Instruction-tuned with emergent behavior
`llava` (if multimodal enabled)	`ollama pull llava`	Vision + text deception (requires GPU setup)

2025-07-19

Patch Deep Friday - Test Drive 1 - fully localized.

✅ Test Drive Sequence: Patch Deep Friday Stack

1. 🔍 Verify Docker Containers

Run:

bash
docker ps

You should see 3 containers:

ollama (port 11434)
openwebui (port 3000)
n8n (port 5678)

2. 🌐 Access OpenWebUI (LLM Chat)

Open browser to: http://localhost:3000
You should see a clean Web UI with model selection (llama3/mistral/phi)
🧪 Test Prompt:

“Explain the difference between symmetric and asymmetric encryption.”

🧠 Confirm that:

Model responds correctly
You can switch between models (llama3 → mistral → phi)
There’s no internet leak (confirm netstat if paranoid)

3. 🧠 API Test via Ollama

In terminal:

bash
curl http://localhost:11434/api/generate -d '{"model":"llama3","prompt":"What is OPSEC?"}'

You should receive a full JSON output with response.

4. 🤖 Access n8n

Open: http://localhost:5678
Log in with:
- User: admin
- Password: kalikotrocks
🧪 Test Workflow:
1. Create new workflow
2. Add a "Cron" node → set to run every minute
3. Add "Set" node → Output: message: Hello Kalikot
4. Connect & Activate

This validates n8n automation engine.

5. 📁 File Ingestion Test (Optional)

If you’ve created a ~/offline-ai-stack/data/docs folder:

Drop a .txt file (e.g., test-snippet.txt)
Launch a Python container:

bash
docker run -it --rm -v ~/offline-ai-stack/data:/app/data python:3.11-bullseye bash

Inside:

bash
pip install llama-index chromadb
# Then build simple doc index

We can automate this later via n8n.

6. 🛑 When Done

To gracefully shut down:

bash
cd ~/offline-ai-stack
docker compose down

🎯 Final Check

Layer	Status
Docker	✅ Up and running
Ollama API	✅ Responds to curl requests
WebUI	✅ Loads, switches models
n8n	✅ Login, build workflows
File Mounts	✅ Validated via busybox/python

Patch Deep Friday - 2025 July 19

🧠 Patch Deep Friday: Project Summary

Project Purpose:
Patch Deep Friday is a fully offline, automation-ready AI assistant and workflow agent stack running on Kalikot — with Kali Linux. Patch is Reboot's brother AI, designed to learn, recover, and automate. It blends local LLM capability with workflow orchestration and document understanding, all without internet dependence.

⚙️ Section 1: Hardware and Host Setup

Primary Host: Kalikot

Project Directory:
~/offline-ai-stack/

🐳 Section 2: Docker Stack (Active)

We’re using Docker Compose to run three core services:

✅ Confirmed Running via `docker-compose up`:

version: '3.8'
services:
  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama-data:/root/.ollama

  openwebui:
    image: ghcr.io/open-webui/open-webui
    ports:
      - "3000:3000"
    environment:
      - OLLAMA_API_BASE_URL=http://ollama:11434
    depends_on:
      - ollama

  n8n:
    image: n8nio/n8n
    ports:
      - "5678:5678"
    volumes:
      - n8n-data:/home/node/.n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=patch
      - N8N_BASIC_AUTH_PASSWORD=fr1d4ysecure
    restart: unless-stopped

volumes:
  ollama-data:
  n8n-data:

🧠 What This Gives Us:

Ollama: Local LLM backend (used for running models like llama3, mistral, codellama, etc.).
Open WebUI: Visual front-end for chatting with models served by Ollama.
n8n: Automation engine to build local workflows (e.g., file parsing, alerting, internal triggers).

🔧 Section 3: Software and Authentication

Hugging Face Token

Token deep-friday saved successfully to:
/home/xxxxxx/.cache/huggingface/stored_tokens
Git credential helper not yet configured.
No git push setup, since Patch runs offline.

🖥️ Section 4: Interface Access

Web UIs:

OpenWebUI → http://localhost:3000
n8n → http://localhost:5678
OLLAMA API → http://localhost:11434

Gmode Access Plans:

Planning access across the gmode VLAN network
Intention to expose OpenWebUI and n8n via internal IPs
Future remote entry point via TOR + Tails + auth

🛠️ Section 5: Networking and Remote Control

Current State:

Working from Kalikot inside the gmode network.
Planning secure remote access using Tails + Onion + Login.
Need to activate SSH on Pop!_OS side (for Tracer III Evo) to work across LAN.

🧪 Section 6: Additional Experiments

Attempted:

Flashing Kali ARM to uConsole (CM4) → Resulted in black screen (driver issue).
Switched to Raspberry Pi OS Lite (64-bit) on uConsole.
Tried minimalist Kali install on uConsole → Broke due to libgtk-3-0t64 and libnettle.so.8 conflicts.

🧠 Section 7: Project Identity and Naming

Patch = Learner, fixer, automation role (opposite but complementary to Reboot).
Middle Name: Deep (for Deep Learning, depth of analysis).
Last Name: Friday (born on Pi Day: March 14).

✅ Section 8: Completed Tasks

✅ Named Kalikot and Patch.
✅ Fully configured Docker Compose file with 3 services.
✅ Set up Hugging Face token.
✅ Started OpenWebUI and confirmed connection to Ollama.
✅ Logged into n8n locally with basic auth.
✅ Defined goal of full offline stack.
✅ Isolated issues on ARM + Raspberry Pi display drivers.

⚠️ Section 9: To-Do / In Progress

Add PDF, DOCX, TXT, XLSX document loaders to Patch for RAG.
Finalize offline model download (e.g., LLaMA 3 or Phi-3 via ollama pull).
Create basic n8n flow for doc → extract → summarize.
Enable LAN access to services from other gmode VLAN devices.
Enable TOR/Onion access with authentication layer.
Retry lightweight local UI for uConsole (Pi OS + text-based tools).

📌 TL;DR Summary

Patch Deep Friday is now operational with a 3-service Docker stack: Ollama (LLM), OpenWebUI (chat), and n8n (automation).
All services are running locally on Kalikot, your 32GB Dell 7490 with Kali Linux.
You've configured offline authentication, interface access, and future remote/TOR expansion plans.
ARM builds on uConsole ran into GTK+Netlib issues; fallback to Pi OS Lite in motion.
Current stage: Ready for RAG setup and automation flows in n8n.

2025-07-30

Table of Contents

1. Executive Summary

2. Business Requirements Overview

3. System Architecture

3.1 Hardware

3.2 Core/Software Components

4. Implementation Phases (Step-by-Step CLI & Validation)

Phase 1: Preparation and Cleanup 🧹

Phase 2: Ollama Native Install and Configuration ⚙️

Phase 3: Dockerized OpenWebUI 🐳

Phase 4: API Connection Fix 🔧

Phase 5: Model Pull and Validation ✅

Phase 6: RAG Test 📑

5. Terms and Dictionary

6. Appendix: Tools and Commands

C. Pulled Models and Usage

A. Tools Used

B. Docker Command Syntax

7. Architecture Diagram

9. Change Log 📝

10. Startup & Shutdown Procedures - bash 🚀🛑

11. Backup & Restore Strategy - bash 💾

Backup Commands:

Restore Strategy:

12. Security Hardening & Monitoring 🔐

13. Versioning & Upgrade Process - bash 🔄

Ollama Upgrade:

OpenWebUI Upgrade:

14. Glitch Prompt Persona & Prompt Library 🧠💬

Example /set Prompt:

Prompt Library Ideas:

15. Troubleshooting Reference 🛠️

8. Pitfalls & Lessons Learned ☠️💡

2025-07-28

🔥 PHASE 1: PURGE & CLEANUP OF NATIVE INSTALL

Step 1: Deactivate and remove the Python virtual environment

Step 2: Remove any native Python-related files

Step 3: Delete broken native install directories

Step 4: Remove Ollama binary if installed natively

Step 5: Purge systemd autostart services if they exist

Step 6: Remove leftover .ollama or data directories in home

Step 7: Purge unused Docker containers and volumes

⚙️ PHASE 2: REBUILD WITH DOCKER + GPU SUPPORT (Persistent Glitchbrain Setup)

✅ Assumptions

Step 1: Create persistent directories

Step 2: Run Ollama + OpenWebUI (GPU bundle)

Step 3: Confirm GPU is active

Step 4: Open WebUI in Browser

🧠 Optional: Docker Compose Version

✅ TL;DR Summary

🧩 Final Check

✅ OpenWebUI Native Start Checklist

1. Activate Python Virtual Environment

2. Start Backend

3. Start Frontend (Vite Dev Server)

4. Access OpenWebUI

5. Verify Backend Connectivity

6. Verify Ollama

✅ Final Check

🧰 Optional: Auto-Start Script

2025-07-26

✅ Post-Reboot Checklist for Glitch

2025-07-19

✅ Test Drive Sequence: Patch Deep Friday Stack

1. 🔍 Verify Docker Containers

2. 🌐 Access OpenWebUI (LLM Chat)

3. 🧠 API Test via Ollama

4. 🤖 Access n8n

5. 📁 File Ingestion Test (Optional)

6. 🛑 When Done

🎯 Final Check

🧠 Patch Deep Friday: Project Summary

⚙️ Section 1: Hardware and Host Setup

🐳 Section 2: Docker Stack (Active)

✅ Confirmed Running via docker-compose up:

🧠 What This Gives Us:

🔧 Section 3: Software and Authentication

Hugging Face Token

🖥️ Section 4: Interface Access

Example `/set` Prompt:

Step 6: Remove leftover `.ollama` or data directories in home

✅ Confirmed Running via `docker-compose up`: