hailey.at
this repo has no description
license, readme
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.env.example
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.env.example
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JETSTREAM_URL=""
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LABELER_URL=""
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LABELER_KEY=""
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REDIS_ADDR=""
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PDS_URL="https://bsky.social"
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ACCOUNT_HANDLE="your-handle.bsky.social"
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ACCOUNT_PASSWORD="your-app-password"
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WATCHED_OPS="did:plc:example1,did:plc:example2"
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JETSTREAM_URL="wss://jetstream2.us-west.bsky.network/subscribe"
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LABELER_URL="http://localhost:3000"
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LABELER_KEY="your-secret-key-here"
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LMSTUDIO_HOST="http://localhost:1234"
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LICENSE
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LICENSE
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Copyright 2025 me@haileyok.com
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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README.md
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README.md
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# dontshowmethis
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An AI-powered Bluesky content moderation system that automatically labels replies to monitored accounts using LLM-based content analysis.
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## Overview
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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.
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The system consists of two components:
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1. **Go Consumer** - Monitors the Jetstream firehose and analyzes replies using an LLM
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2. **Skyware Labeler** - TypeScript server that manages and emits content labels
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## Architecture
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```
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Jetstream → Go Consumer → LM Studio (LLM) → Labeler Service → Bluesky
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```
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1. The Go consumer subscribes to Jetstream and monitors replies to specified accounts
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2. When a reply is detected, it fetches the parent post and sends both to LM Studio
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3. The LLM classifies the reply based on the system prompt
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4. Labels are emitted via the Skyware labeler service
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5. Labels are propagated to Bluesky's labeling system
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## Prerequisites
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- [LM Studio](https://lmstudio.ai/) with a compatible model loaded
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- A Bluesky account for the labeler
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## Installation
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### Clone the repository
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```bash
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git clone https://github.com/haileyok/dontshowmethis.git
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cd dontshowmethis
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```
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### Install Go dependencies
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```bash
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go mod download
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```
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### Install labeler dependencies
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```bash
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cd labeler
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yarn install
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cd ..
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```
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## Configuration
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Copy the example environment file and configure it:
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```bash
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cp .env.example .env
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```
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### Environment Variables
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**For the Go Consumer:**
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- `PDS_URL` - Your Bluesky PDS URL (e.g., `https://bsky.social`)
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- `ACCOUNT_HANDLE` - Your Bluesky account handle
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- `ACCOUNT_PASSWORD` - Your Bluesky account password
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- `WATCHED_OPS` - Comma-separated list of DIDs to monitor for replies
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- `JETSTREAM_URL` - Jetstream WebSocket URL (default: `wss://jetstream2.us-west.bsky.network/subscribe`)
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- `LABELER_URL` - URL of your labeler service (e.g., `http://localhost:3000`)
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- `LABELER_KEY` - Authentication key for the labeler API
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- `LMSTUDIO_HOST` - LM Studio API host (e.g., `http://localhost:1234`)
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**For the Skyware Labeler:**
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- `SKYWARE_DID` - Your labeler's DID
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- `SKYWARE_SIG_KEY` - Your labeler's signing key
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- `EMIT_LABEL_KEY` - Secret key for the emit label API (must match `LABELER_KEY` above)
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## Running the Services
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### 1. Start LM Studio
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1. Open LM Studio
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2. Load a compatible model (recommended: `google/gemma-3-27b` or similar)
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3. Start the local server (usually runs on `http://localhost:1234`)
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### 2. Start the Labeler Service
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```bash
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cd labeler
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npm start
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```
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The labeler will start two servers:
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- Port 14831: Skyware labeler server
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- Port 3000: Label emission API
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### 3. Start the Go Consumer
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```bash
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go run .
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```
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Or build and run:
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```bash
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go build -o dontshowmethis
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./dontshowmethis
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```
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## Usage
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Once running, the system will:
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1. Connect to Jetstream and monitor the firehose
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2. Watch for replies to accounts specified in `WATCHED_OPS`
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3. Automatically analyze and label qualifying replies
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4. Log all actions to stdout
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### Finding Account DIDs
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To monitor specific accounts, you need their DIDs. You can find a DID by:
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```bash
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curl "https://bsky.social/xrpc/com.atproto.identity.resolveHandle?handle=username.bsky.social"
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```
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Add the returned DID to your `WATCHED_OPS` environment variable.
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## How Content Classification Works
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See `lmstudio.go:147` for the system prompt.
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## Development
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### Project Structure
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```
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.
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├── main.go # CLI setup and consumer initialization
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├── handle_post.go # Post handling and labeling logic
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├── lmstudio.go # LLM client and content classification
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├── sets/
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│ └── domains.go # Political domain list (currently unused)
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├── labeler/
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│ ├── index.ts # Skyware labeler service
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│ └── package.json # Labeler dependencies
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├── .env.example # Example environment configuration
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└── README.md # This file
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```
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### Adding New Labels
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1. Add the label constant in `main.go`:
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```go
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const LabelNewLabel = "new-label"
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```
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2. Add it to the labeler's allowed labels in `labeler/index.ts`:
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```typescript
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const LABELS: Record<string, boolean> = {
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'bad-faith': true,
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'off-topic': true,
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'funny': true,
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'new-label': true, // Add here
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}
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```
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3. Update the LLM schema in `lmstudio.go` to include the new classification
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4. Update the handling logic in `handle_post.go` to emit the new label
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## License
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MIT
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## Acknowledgments
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- [Jetstream](https://github.com/bluesky-social/jetstream) - Real-time firehose for Bluesky
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- [Skyware](https://skyware.js.org/) - Labeler framework
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- [LM Studio](https://lmstudio.ai/) - Local LLM inference
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sets/domains.go
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sets/domains.go
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package sets
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var PolDomains = []string{
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"nytimes.com",
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"theguardian.com",
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"open.substack.com",
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"npr.org",
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"cnn.com",
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"washingtonpost.com",
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"wsj.com",
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"newrepublic.com",
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"apnews.com",
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"politico.com",
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"reut.rs",
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"reuters.com",
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"nbcnews.com",
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"kfor.com",
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"huffpost.com",
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"rollingstone.com",
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"rawstory.com",
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"cbsnews.com",
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"bbc.com",
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"cbc.ca",
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"nymag.com",
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"wapo.st",
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"newsweek.com",
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"theatlantic.com",
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"variety.com",
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"nyti.ms",
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"talkingpointsmemo.com",
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"thedailybeast.com",
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"axios.com",
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"publicnotice.co",
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"semafor.com",
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"msnbc.com",
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"liberation.fr",
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"newyorker.com",
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"independent.co.uk",
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"whitehouse.gov",
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"motherjones.com",
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"bbc.co.uk",
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"nbcnews.to",
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"theglobeandmail.com",
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"cnbc.com",
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"defector.com",
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"propublica.com",
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"nypost.com",
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"bloomberg.com",
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"ctvnews.ca",
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"thetimes.com",
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"abc.net.au",
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"bloom.bg",
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"democracydocket.com",
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"cnn.it",
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"latimes.com",
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"publico.es",
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"politico.eu",
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"kyivindependent.com",
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"abcnews.go.com",
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"irishtimes.com",
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}