hailey.at
+29
Dockerfile
+29
Dockerfile
···
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# Build stage
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FROM golang:1.25.5-alpine AS builder
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WORKDIR /build
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# Install build dependencies
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RUN apk add --no-cache git ca-certificates
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# Copy go mod files
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COPY go.mod go.sum ./
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RUN go mod download
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# Copy source code
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COPY . .
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# Build the application
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RUN CGO_ENABLED=0 GOOS=linux go build -a -installsuffix cgo -o bloblens ./cmd/bloblens
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# Runtime stage
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FROM alpine:latest
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RUN apk --no-cache add ca-certificates
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WORKDIR /app
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# Copy the binary from builder
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COPY --from=builder /build/bloblens .
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ENTRYPOINT ["/app/bloblens"]
+67
README.md
+67
README.md
···
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# bloblens
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Tracks similar avatars and banners across Bluesky by hashing profile images and comparing them.
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## What it does
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Listens to the ATProto firehose for profile updates, hashes any avatar/banner images using PDQ perceptual hashing, and stores them in a vector database. When it finds multiple profiles using similar images, it logs them.
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Useful for finding coordinated behavior, tracking ban evasion, or whatever else
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## Setup
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You'll need three things running:
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- **bloblens** (this)
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- **Retina** - image hashing service (handles PDQ hashing)
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- **Qdrant** - vector database (stores and searches hashes)
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### With Docker Compose
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```bash
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docker-compose up -d
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```
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That's it. It'll create the Qdrant collection automatically on first run.
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### Manual
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Build:
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```bash
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go build -o bloblens ./cmd/bloblens
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```
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Run:
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```bash
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./bloblens \
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--retina-host=http://localhost:8080 \
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--websocket-host=wss://jetstream.atproto.tools/subscribe \
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--qdrant-host=localhost \
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--qdrant-port=6334 \
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--qdrant-collection=blobs \
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--max-hamming-distance=31 \
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--seen-threshold=5
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```
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## Configuration
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| Flag | Description | Default |
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|------|-------------|---------|
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| `--retina-host` | URL of your Retina instance | required |
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| `--websocket-host` | ATProto firehose URL | required |
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| `--qdrant-host` | Qdrant server hostname | required |
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| `--qdrant-port` | Qdrant gRPC port | required |
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| `--qdrant-collection` | Collection name for storing hashes | required |
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| `--max-hamming-distance` | Max distance to consider images "similar" | 31 |
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| `--seen-threshold` | How many matches before logging | required |
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| `--max-limit` | Max results to fetch from Qdrant per search | required |
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| `--max-search-time` | Timeout for vector searches | required |
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## How it works
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1. Watches the firehose for `app.bsky.actor.profile` records
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2. Extracts avatar and banner blob refs
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3. Sends them to Retina for PDQ hashing
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4. Stores the 256-dimensional vector in Qdrant
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5. Searches Qdrant for similar existing hashes
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6. Logs when threshold is exceeded
+49
docker-compose.yml
+49
docker-compose.yml
···
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services:
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qdrant:
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image: qdrant/qdrant:latest
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ports:
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- "6333:6333"
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- "6334:6334"
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volumes:
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- qdrant_storage:/qdrant/storage
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environment:
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- QDRANT_HOST=0.0.0.0
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restart: unless-stopped
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retina:
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image: # INSERT YOUR GHCR LINK HERE
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ports:
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- "8080:8080"
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restart: unless-stopped
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bloblens:
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build:
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context: .
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dockerfile: Dockerfile
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depends_on:
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- qdrant
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- retina
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environment:
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- RETINA_HOST=http://retina:8080
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- WEBSOCKET_HOST=wss://jetstream.atproto.tools/subscribe
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- QDRANT_HOST=qdrant
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- QDRANT_PORT=6334
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- QDRANT_COLLECTION=blobs
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- MAX_SEARCH_TIME=5s
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- MAX_LIMIT=100
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- SEEN_THRESHOLD=5
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- MAX_HAMMING_DISTANCE=31
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command:
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- --retina-host=${RETINA_HOST}
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- --websocket-host=${WEBSOCKET_HOST}
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+
- --qdrant-host=${QDRANT_HOST}
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- --qdrant-port=${QDRANT_PORT}
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- --qdrant-collection=${QDRANT_COLLECTION}
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- --max-search-time=${MAX_SEARCH_TIME}
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- --max-limit=${MAX_LIMIT}
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- --seen-threshold=${SEEN_THRESHOLD}
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+
- --max-hamming-distance=${MAX_HAMMING_DISTANCE}
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restart: unless-stopped
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+
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volumes:
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qdrant_storage: