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dontshowmethis#

An AI-powered Bluesky content moderation system that automatically labels replies to monitored accounts using LLM-based content analysis.

Overview#

This project monitors Bluesky posts in real-time via Jetstream and uses a local LLM (via LM Studio) to classify replies to watched accounts. It automatically applies labels such as "bad-faith", "off-topic", and "funny" to help users filter and moderate content.

The system consists of two components:

  1. Go Consumer - Monitors the Jetstream firehose and analyzes replies using an LLM
  2. Skyware Labeler - TypeScript server that manages and emits content labels

Architecture#

Jetstream → Go Consumer → LM Studio (LLM) → Labeler Service → Bluesky
  1. The Go consumer subscribes to Jetstream and monitors replies to specified accounts
  2. When a reply is detected, it fetches the parent post and sends both to LM Studio
  3. The LLM classifies the reply based on the system prompt
  4. Labels are emitted via the Skyware labeler service
  5. Labels are propagated to Bluesky's labeling system

Prerequisites#

  • LM Studio with a compatible model loaded
  • A Bluesky account for the labeler

Installation#

Clone the repository#

git clone https://github.com/haileyok/dontshowmethis.git
cd dontshowmethis

Install Go dependencies#

go mod download

Install labeler dependencies#

cd labeler
yarn install
cd ..

Configuration#

Copy the example environment file and configure it:

cp .env.example .env

Environment Variables#

For the Go Consumer:

  • PDS_URL - Your Bluesky PDS URL (e.g., https://bsky.social)
  • ACCOUNT_HANDLE - Your Bluesky account handle
  • ACCOUNT_PASSWORD - Your Bluesky account password
  • WATCHED_OPS - Comma-separated list of DIDs to monitor for replies
  • JETSTREAM_URL - Jetstream WebSocket URL (default: wss://jetstream2.us-west.bsky.network/subscribe)
  • LABELER_URL - URL of your labeler service (e.g., http://localhost:3000)
  • LABELER_KEY - Authentication key for the labeler API
  • LMSTUDIO_HOST - LM Studio API host (e.g., http://localhost:1234)

For the Skyware Labeler:

  • SKYWARE_DID - Your labeler's DID
  • SKYWARE_SIG_KEY - Your labeler's signing key
  • EMIT_LABEL_KEY - Secret key for the emit label API (must match LABELER_KEY above)

Running the Services#

1. Start LM Studio#

  1. Open LM Studio
  2. Load a compatible model (recommended: google/gemma-3-27b or similar)
  3. Start the local server (usually runs on http://localhost:1234)

2. Start the Labeler Service#

cd labeler
npm start

The labeler will start two servers:

  • Port 14831: Skyware labeler server
  • Port 3000: Label emission API

3. Start the Go Consumer#

go run .

Or build and run:

go build -o dontshowmethis
./dontshowmethis

Usage#

Once running, the system will:

  1. Connect to Jetstream and monitor the firehose
  2. Watch for replies to accounts specified in WATCHED_OPS
  3. Automatically analyze and label qualifying replies
  4. Log all actions to stdout

Finding Account DIDs#

To monitor specific accounts, you need their DIDs. You can find a DID by:

curl "https://bsky.social/xrpc/com.atproto.identity.resolveHandle?handle=username.bsky.social"

Add the returned DID to your WATCHED_OPS environment variable.

How Content Classification Works#

See lmstudio.go:147 for the system prompt.

Development#

Project Structure#

.
├── main.go              # CLI setup and consumer initialization
├── handle_post.go       # Post handling and labeling logic
├── lmstudio.go         # LLM client and content classification
├── sets/
│   └── domains.go      # Political domain list (currently unused)
├── labeler/
│   ├── index.ts        # Skyware labeler service
│   └── package.json    # Labeler dependencies
├── .env.example        # Example environment configuration
└── README.md           # This file

Adding New Labels#

  1. Add the label constant in main.go:

    const LabelNewLabel = "new-label"

  2. Add it to the labeler's allowed labels in labeler/index.ts:

    const LABELS: Record<string, boolean> = {
  'bad-faith': true,
  'off-topic': true,
  'funny': true,
  'new-label': true,  // Add here
}

  3. Update the LLM schema in lmstudio.go to include the new classification

  4. Update the handling logic in handle_post.go to emit the new label

License#

MIT

Acknowledgments#