reddacted 0.2.6

Analyze Reddit comments for PII and other sensitive information using local or OpenAI API compatible LLMs and perform sentiment analysis, edit and remove comments.

🛡️ reddacted

AI-Powered Reddit Privacy Suite

Local LLM powered, highly performant privacy analysis leveraging AI, sentiment analysis & PII detection
to provide insights into your true privacy with bulk remediation

For aging engineers who want to protect their future political careers 🏛️

✨ Key Features

🛡️	PII Detection Analyze the content of comments to identify anything that might reveal PII that you may not want correlated with your anonymous username
🤫	Sentiment Analysis Understand the emotional tone of your Reddit history, combined with upvote/downvote counts & privacy risks to choose which posts to reddact
🔒	Zero-Trust Architecture Client-side execution only, no data leaves your machine unless you choose to use a hosted API. Fully compatible with all OpenAI compatible endpoints
⚡	Self-Host Ready Use any model via Ollama, llama.cpp, vLLM or other platform capable of exposing an OpenAI-compatible endpoint. LiteLLM works just dandy.
📊	Smart Cleanup Preserve valuable contributions while removing risky content - clean up your online footprint without blowing away everything

🔐 Can I trust this with my data?

You don't have to - read the code for yourself, only reddit is called

reddacted user yourusername --local-llm "http://localhost:11434"

• ✅ Client-side execution only, no tracking or external calls
• ✅ Session-based authentication if you choose - it is optional unless you want to delete
• ✅ Keep your nonsense comments with lots of upvotes and good vibes without unintentionally doxing yourself

reddacted user taylorwilsdon --limit 3

📋 Table of Contents

• Key Features
• Can I trust this with my data?
• Installation
• Usage
  • Available Commands
  • Common Arguments
  • LLM Configuration
• How accurate is the PII detection?
• FAQ
• Troubleshooting
• Authentication
• Advanced Usage
• Development
• Testing
• Common Exceptions
• Support & Community

📥 Installation

# Install from brew (recommended)
brew install taylorwilsdon/tap/reddacted

# Install from PyPI (recommended)
pip install reddacted

# Or install from source
git clone https://github.com/taylorwilsdon/reddacted.git
cd reddacted
pip install -e ".[dev]"  # Installs with development dependencies

🚀 Usage

# Most basic possible quick start - this will walk you through selecting your LLM in the command line
reddacted user spez

# Analyze a user's recent comments with local LLM specified
reddacted user spez \
  --limit 5 \
  --local-llm "http://localhost:11434" \
  --model "qwen2.5:3b" \
  --sort new

# Analyze controversial comments with OpenAI
export OPENAI_API_KEY="your-api-key"
reddacted user spez \
  --sort controversial \
  --time month \
  --model "gpt-4" \
  --limit 10 \
  --pii-only

# Analyze a specific subreddit post with PII filter disabled
reddacted listing r/privacy abc123 \
  --local-llm "http://localhost:11434" \
  --model "qwen2.5:3b" \
  --disable-pii \
  --sort new

# Search for specific content (requires auth)
reddacted user spez \
  --enable-auth \
  --text-match "python" \
  --skip-text "deleted" \
  --sort top \
  --time all

# Bulk comment management
reddacted delete abc123,def456 --batch-size 5  # Delete comments
reddacted update abc123,def456                 # Replace with standard redaction message
reddacted update abc123,def456 --use-random-string  # Replace with random UUID

Available Commands

Command	Description
user	Analyze a user's comment history
listing	Analyze a specific post and its comments
delete	Delete comments by their IDs
update	Replace comment content with r/reddacted

Common Arguments

Argument	Description
--limit N	Maximum comments to analyze (default: 100, 0 for unlimited)
--sort	Sort method: hot, new, controversial, top (default: new)
--time	Time filter: all, day, hour, month, week, year (default: all)
--output-file	Save detailed analysis to a file
--enable-auth	Enable Reddit API authentication
--disable-pii	Skip PII detection
--pii-only	Show only comments containing PII
--text-match	Search for comments containing specific text
--skip-text	Skip comments containing specific text pattern
--batch-size	Comments per batch for delete/update (default: 10)
--use-random-string	Use random UUID instead of standard message when updating comments

LLM Configuration

Argument	Description
--local-llm URL	Local LLM endpoint (OpenAI compatible)
--openai-key KEY	OpenAI API key
--openai-base URL	Custom OpenAI API base URL
--model NAME	Model to use (default: gpt-4 for OpenAI)

Note: For cloud-based analysis using OpenAI, you can either use the --openai-key flag or set the environment variable:

export OPENAI_API_KEY="your-api-key"

❓ How accurate is the PII detection, really?

Surprisingly good. Good enough that I run it against my own stuff in delete mode. It's basically a defense-in-depth approach combining these methods:

📊 AI Detection

Doesn't need a crazy smart model, don't waste your money on r1 or o1.

• Cheap & light models like gpt-4o-mini, gpt-3.5-turbo, qwen2.5:3b or 7b and Mistral are all plenty
• Don't use something too dumb or it will be inconsistent, a 0.5b model will produce unreliable results
• Works well with cheap models like qwen2.5:3b (potato can run this) and gpt-4o-mini (~15¢ per million tokens)

🔍 Pattern Matching

50+ regex rules for common PII formats does a first past sweep for the obvious stuff

🧠 Context Analysis

Are you coming off as a dick? Perhaps that factors into your decision to clean up. Who could say, mine are all smiley faces.

💡 FAQ

Q: How does the AI handle false positives?

Adjust confidence threshold (default 0.7) per risk tolerance. You're building a repo from source off some random dude's github - don't run this and just delete a bunch of stuff blindly, you're a smart person. Review your results, and if it is doing something crazy, please tell me.

Q: What LLMs are supported?

Local: any model via Ollama, vLLM or other platform capable of exposing an openai-compatible endpoint.
Cloud: OpenAI-compatible endpoints

Q: Is my data sent externally?

If you choose to use a hosted provider, yes - in cloud mode - local analysis stays fully private.

🔧 Troubleshooting

If you get "command not found" after installation:

1. Check Python scripts directory is in your PATH:

# Typical Linux/Mac location
export PATH="$HOME/.local/bin:$PATH"

# Typical Windows location
set PATH=%APPDATA%\Python\Python311\Scripts;%PATH%

2. Verify installation location:

pip show reddacted

🔑 Authentication

Before running any commands that require authentication, you'll need to set up your Reddit API credentials:

Step 1: Create a Reddit Account

If you don't have one, sign up at https://www.reddit.com/account/register/

Step 2: Create a Reddit App

• Go to https://www.reddit.com/prefs/apps
• Click "are you a developer? create an app..." at the bottom
• Choose "script" as the application type
• Set "reddacted" as both the name and description
• Use "http://localhost:8080" as the redirect URI
• Click "create app"

Step 3: Get Your Credentials

After creating the app, note down:

• Client ID: The string under "personal use script"
• Client Secret: The string labeled "secret"

Step 4: Set Environment Variables

export REDDIT_USERNAME=your-reddit-username
export REDDIT_PASSWORD=your-reddit-password
export REDDIT_CLIENT_ID=your-client-id
export REDDIT_CLIENT_SECRET=your-client-secret

These credentials are also automatically used if all environment variables are present, even without the --enable-auth flag.

🧙‍♂️ Advanced Usage

Text Filtering

You can filter comments using these arguments:

Argument	Description
--text-match "search phrase"	Only analyze comments containing specific text (requires authentication)
--skip-text "skip phrase"	Skip comments containing specific text pattern

For example:

# Only analyze comments containing "python"
reddacted user spez --text-match "python"

# Skip comments containing "deleted"
reddacted user spez --skip-text "deleted"

# Combine both filters
reddacted user spez --text-match "python" --skip-text "deleted"

👨‍💻 Development

This project uses UV for building and publishing. Here's how to set up your development environment:

1. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install UV:

pip install uv

3. Install in development mode with test dependencies:

pip install -e ".[dev]"

4. Build the package:

uv build --sdist --wheel

5. Create a new release:

./release.sh

The release script will:

• Build the package with UV
• Create and push a git tag
• Create a GitHub release
• Update the Homebrew formula
• Publish to PyPI (optional)

That's it! The package handles all other dependencies automatically, including NLTK data.

🧪 Testing

Run the test suite:

pytest tests

Want to contribute? Great! Feel free to:

• Open an Issue
• Submit a Pull Request

⚠️ Common Exceptions

too many requests

If you're unauthenticated, reddit has relatively low rate limits for it's API. Either authenticate against your account, or just wait a sec and try again.

the page you requested does not exist

Simply a 404, which means that the provided username does not point to a valid page.

Pro Tip: Always review changes before executing deletions!

🌐 Support & Community

Join our subreddit: r/reddacted

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