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Automated intelligence gathering system that collects, analyzes, and synthesizes the latest AI news from Reddit and YouTube using local LLMs

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Source Content Retrieval and Intelligence Bot Engine

Automated Multi-Topic Intelligence Gathering System

What is SCRIBE?

SCRIBE is an automated monitoring bot that watches Reddit and YouTube, analyzes content with a local AI (Ollama), and generates daily reports. It uses a package-based architecture allowing you to monitor multiple topics independently.

In short: You create a package for your topic → SCRIBE collects → AI analyzes → You receive a Markdown report (+ optional Discord notification).

Key Features

  • Multi-package architecture - Monitor multiple topics independently (AI, cybersecurity, gaming, etc.)
  • Multi-source - Reddit and YouTube with customizable sources per package
  • Local AI - Uses Ollama (Mistral, Phi4, Llama3, Qwen3...) - no cloud API needed
  • Smart deduplication - Semantic detection of similar content (TF-IDF + SimHash)
  • Professional reports - Structured Markdown with insights and metrics
  • Multiple notification channels - Discord (rich embeds) and Synology Chat webhooks
  • SQLite cache - Per-package isolation, avoids reprocessing
  • Multilingual - Reports in 11 languages (en, fr, es, de, it, pt, nl, ru, zh, ja, ar)

How Does It Work?

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   REDDIT    │     │   YOUTUBE   │     │   DISCORD   │
│    subs     │     │ transcripts │     │  + SYNOLOGY │
└──────┬──────┘     └──────┬──────┘     └──────▲──────┘
       │                   │                   │
       └─────────┬─────────┘                   │
                 ▼                             │
        ┌─────────────────┐                    │
        │   1. COLLECT    │                    │
        │   Posts/Videos  │                    │
        └────────┬────────┘                    │
                 ▼                             │
        ┌─────────────────┐                    │
        │   2. FILTER     │                    │
        │  (SQLite Cache) │                    │
        └────────┬────────┘                    │
                 ▼                             │
        ┌─────────────────┐                    │
        │  3. AI ANALYSIS │                    │
        │      (LLM)      │                    │
        │  Score 1-10     │                    │
        │  Category       │                    │
        │  Insights       │                    │
        └────────┬────────┘                    │
                 ▼                             │
        ┌─────────────────┐                    │
        │ 4. DEDUPLICATE  │                    │
        │   (Semantic)    │                    │
        └────────┬────────┘                    │
                 ▼                             │
        ┌─────────────────┐                    │
        │   5. REPORT     │────────────────────┘
        │   (Markdown)    │
        └─────────────────┘

The 9-Step Pipeline

  1. Cache cleanup - 90-day retention policy
  2. Data collection - Reddit posts + YouTube videos with transcripts
  3. Content preparation - Unified format for all sources
  4. Cache filtering - Skip already processed content
  5. AI Analysis - Ollama scores relevance (1-10), categorizes, extracts insights
  6. Relevance filtering - Keep only score >= threshold (default: 7/10)
  7. Deduplicate - Semantic duplicate detection (TF-IDF + SimHash)
  8. Report - Generate Markdown + Discord notification with rich embeds
  9. Summary - Optional concise daily summary to separate Discord webhook

Quick Installation

Prerequisites

  • Python 3.10+
  • Ollama installed and running
  • Git (for automatic updates)

1. Install Ollama

# Download from https://ollama.ai/
# Then:
ollama pull qwen3:14b  # or mistral, phi4, llama3

2. Clone and First-Time Setup (Recommended)

git clone https://github.com/your-repo/SCRIBE.git
cd SCRIBE

Windows - Simple method:

# Double-click setup.bat or run:
setup.bat

This will automatically:

  • Create the virtual environment
  • Install all dependencies
  • Copy .env.example to .env (if needed)
  • Run configuration tests to verify everything works

3. Configure Your Credentials

Edit the .env file with your API keys:

# Reddit (https://reddit.com/prefs/apps)
REDDIT_CLIENT_ID=your_id
REDDIT_CLIENT_SECRET=your_secret
REDDIT_USER_AGENT=SCRIBE/1.0

# YouTube (https://console.cloud.google.com/)
YOUTUBE_API_KEY=your_key

# Ollama
OLLAMA_HOST=http://localhost:11434

# Package-specific webhooks (Discord and/or Synology Chat)
DISCORD_AI_TRENDS_WEBHOOK=your_discord_webhook
DISCORD_AI_TRENDS_SUMMARY_WEBHOOK=your_discord_summary_webhook

# Optional: Synology Chat webhooks
SYNOLOGY_AI_TRENDS_WEBHOOK=https://your-synology:5001/webapi/entry.cgi?api=SYNO.Chat.External&method=incoming&version=2&token=xxx
SYNOLOGY_AI_TRENDS_SUMMARY_WEBHOOK=https://your-synology:5001/webapi/entry.cgi?api=SYNO.Chat.External&method=incoming&version=2&token=yyy

Note: Without Reddit/YouTube credentials, the app will still work but without those sources.

4. Run the AI Trends Package

# Windows - Simple method:
run_ai_trends.bat

# Or manually:
python main.py --package ai_trends --mode once

Available Batch Files

File Description
setup.bat First-time setup: venv, dependencies, .env, and tests
run_ai_trends.bat Run the AI Trends package
test_connections.bat Run API connection tests (Reddit, YouTube, Ollama, Discord)

Usage

List Available Packages

python main.py --list-packages

Run a Specific Package

python main.py --package ai_trends --mode once

The report will be generated in data/ai_trends/reports/.

Run with Specific Language

python main.py --package ai_trends --mode once --lang en  # English
python main.py --package ai_trends --mode once --lang fr  # French

View Package Statistics

python main.py --package ai_trends --mode stats

Shows: processed content, relevance rate, breakdown by source/category.

Verify API Connections

python tests/test_connections.py

Creating a New Package

SCRIBE's package system allows you to create independent monitoring configurations for any topic.

1. Copy an Existing Package

cp -r packages/ai_trends packages/cybersecurity

2. Edit Package Settings

packages/cybersecurity/settings.yaml:

package:
  name: cybersecurity
  display_name: "Cybersecurity Watch"
  description: "Monitor cybersecurity news and threats"

reddit:
  subreddits:
    - netsec
    - cybersecurity
    - hacking
    - ReverseEngineering
  posts_limit: 10
  comments_limit: 5
  timeframe: "day"

youtube:
  keywords:
    - "cybersecurity news"
    - "malware analysis"
  channels:
    - "@JohnHammond"
    - "@LiveOverflow"
  videos_per_source: 5

analysis:
  relevance_threshold: 7
  similarity_threshold: 0.85

categories:
  - Malware Analysis
  - Vulnerability Research
  - Threat Intelligence
  - Network Security
  - Incident Response

reporting:
  language: "en"
  min_insights: 1

discord:
  enabled: true
  webhook_env: "DISCORD_CYBERSECURITY_WEBHOOK"
  rich_embeds: true
  summary:
    enabled: true
    webhook_env: "DISCORD_CYBERSECURITY_SUMMARY_WEBHOOK"

# Optional: Synology Chat
synology:
  enabled: false
  webhook_env: "SYNOLOGY_CYBERSECURITY_WEBHOOK"

3. Customize LLM Prompts

packages/cybersecurity/prompts.yaml:

Adapt the system prompts for your domain:

  • relevance_analyzer - How to score content relevance
  • insight_extractor - How to extract key insights
  • executive_summary - How to write the report summary
  • daily_summary - How to write the Discord summary

4. Add Webhooks to .env

DISCORD_CYBERSECURITY_WEBHOOK=https://discord.com/api/webhooks/...
DISCORD_CYBERSECURITY_SUMMARY_WEBHOOK=https://discord.com/api/webhooks/...

5. Run Your New Package

python main.py --package cybersecurity --mode once

Configuration

Global Settings (config/global.yaml)

Shared configuration for all packages:

ollama:
  model: "qwen3:14b"  # or mistral, phi4, llama3
  parameters:
    temperature: 0.3
    num_ctx: 32768

Package Settings (packages//settings.yaml)

Each package has its own:

  • Sources - Subreddits, YouTube channels/keywords
  • Thresholds - Relevance score, similarity detection
  • Categories - Domain-specific classification
  • Discord - Separate webhooks per package

Notifications

SCRIBE supports multiple notification channels that can be enabled independently per package.

Discord Notifications

Main Notification (Step 8):

  • Rich embeds with images (Reddit posts + YouTube thumbnails)
  • Detailed insights per category
  • Configured via discord.webhook_env in package settings

Summary Notification (Step 9, Optional):

  • Concise AI-generated overview (<2000 chars)
  • Sent to separate webhook
  • Enable in package settings:
discord:
  enabled: true
  webhook_env: "DISCORD_AI_TRENDS_WEBHOOK"
  summary:
    enabled: true
    webhook_env: "DISCORD_AI_TRENDS_SUMMARY_WEBHOOK"

Synology Chat Notifications

Main Notification (Step 8b):

  • Formatted text messages with category, insights, and metadata
  • Configured via synology.webhook_env in package settings

Summary Notification (Step 9b, Optional):

  • Concise AI-generated overview (same as Discord)
  • Sent to separate webhook

Enable Synology Chat in package settings:

synology:
  enabled: true
  webhook_env: "SYNOLOGY_AI_TRENDS_WEBHOOK"
  summary:
    enabled: true
    webhook_env: "SYNOLOGY_AI_TRENDS_SUMMARY_WEBHOOK"

Get your Synology webhook URL: Synology Chat > Integration > Incoming Webhook

Test Synology integration:

python tests/test_synology.py

Daily Scheduler (Windows)

To run SCRIBE automatically every day:

Using Task Scheduler GUI

  1. Press Win + R, type taskschd.msc, press Enter
  2. Click Create Basic Task
  3. Name: "SCRIBE AI Trends" → Next
  4. Trigger: Daily at your preferred time → Next
  5. Action: Start a program
  6. Program: python
  7. Arguments: main.py --package ai_trends --mode once
  8. Start in: C:\path\to\SCRIBE

Command Line

schtasks /create /tn "SCRIBE AI Trends" /tr "python main.py --package ai_trends --mode once" /sc daily /st 08:00

Generated Report Example

# SCRIBE - CYBERSECURITY INTELLIGENCE REPORT
## 2025-01-15 | 08:00

New ransomware variants targeting critical infrastructure
detected this week, with increased activity from APT groups...

## Malware Analysis
   3 insight(s)

### 1. New Ransomware Strain Targets Healthcare
**Source**: Reddit
**Link**: https://reddit.com/r/netsec/...
**Relevance**: 9/10
**Author**: u/malware_analyst

**Insights**: A new ransomware variant has been discovered
targeting healthcare systems with sophisticated evasion...

---

## Threat Intelligence
   2 insight(s)

### 1. APT Group Activity Analysis
**Source**: YouTube
**Link**: https://youtube.com/watch?v=...
**Relevance**: 8/10

**Insights**: Detailed breakdown of recent APT campaign...

---

*Report generated by SCRIBE - 5 total insights*

Troubleshooting

"Model not found"

ollama list              # View installed models
ollama pull qwen3:14b    # Install missing model

"Package not found"

python main.py --list-packages  # List available packages

Ensure your package directory exists in packages/ with valid settings.yaml.

"Reddit credentials invalid"

Check that your .env contains the correct values from https://reddit.com/prefs/apps

"YouTube quota exceeded"

YouTube API has a free daily limit. Reduce videos_per_source in your package settings.

Ollama too slow

  • Use a lighter model (phi4 vs qwen3:14b)
  • Reduce content limits in package settings

Not enough insights

  • Lower relevance_threshold (e.g., 5 instead of 7)
  • Increase posts_limit or videos_per_source
  • Add more subreddits/channels

Testing

All tests are in the tests/ directory:

python tests/test_connections.py       # Verify all API connections (Reddit, YouTube, Ollama, Discord, Synology)
python tests/test_discord_split.py     # Test Discord message splitting
python tests/test_discord_images.py    # Test Discord image embeds
python tests/test_synology.py          # Test Synology Chat webhook integration

Logs

Per-package logging:

  • Application: logs/<package_name>.log
  • Raw data: data/<package_name>/raw_logs/

Adding New Collectors

  1. Create collector in src/collectors/ following existing patterns
  2. Constructor must accept config: dict
  3. Return list of dicts with: id, source, title, text, url, timestamp, metadata
  4. Register in main.py SCRIBE class

Roadmap

  • Reddit and YouTube collection
  • Local AI analysis (Ollama)
  • Semantic deduplication
  • Markdown reports
  • Discord notifications with rich embeds
  • Multi-package architecture
  • Per-package Discord webhooks
  • Synology Chat webhook support
  • Daily summary feature
  • Emerging trend detection

Contributing

The project is designed to be extensible:

  • Add packages: packages/<your_topic>/
  • Add sources: src/collectors/
  • New processors: src/processors/
  • Report templates: src/storage/report_generator.py

License

MIT License - Free to use and modify

SCRIBE - Your Automated Multi-Topic Intelligence Assistant

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Automated intelligence gathering system that collects, analyzes, and synthesizes the latest AI news from Reddit and YouTube using local LLMs

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