VirusTotal for AI slop detection. Scan any text or URL with 23 independent detection engines running entirely on your hardware. No data sent to third parties.
SlopTotal runs 23 AI detection engines in parallel -- neural classifiers, statistical tests, and linguistic heuristics -- and produces a calibrated forensic score. Results stream in real-time as each engine completes.
Live demo: sloptotal.com
- Python 3.10+ (3.11 recommended — macOS ships 3.9, which is too old)
- 4 GB RAM minimum (lite profile); 8 GB standard; 16 GB+ for best CPU throughput
- No GPU required — all engines run on CPU; CUDA optional for faster inference
- ~2 GB disk for HuggingFace model cache on first run
# Clone and install
git clone https://github.com/pablocaeg/sloptotal.git
cd sloptotal
# Use Python 3.10+ explicitly (example: Homebrew on macOS)
python3.11 -m venv venv && source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
# Optional: copy env template
cp .env.example .env
# Start (auto-detects hardware, downloads models on first run)
./start.sh
# or manually:
uvicorn app.main:app --host 0.0.0.0 --port 8000Open http://localhost:8000 in your browser.
Re-scanning the same URL? Results are cached by content hash. After upgrading dependencies, stale failure reports are purged automatically on startup. Run a fresh scan if you previously saw "Model loading failed".
docker compose upsloptotal/
├── app/ # Backend (Python/FastAPI)
│ ├── main.py # App factory, lifespan, middleware
│ ├── routes/
│ │ ├── web.py # Web page routes (/, /report, /analyze, SSE)
│ │ ├── api.py # JSON API (/api/quick-score, /api/analyze, etc.)
│ │ └── queue.py # Queue status & ticket polling
│ ├── analyzer.py # Core analysis orchestration & scoring
│ ├── engines/ # 23 detection engines
│ │ ├── base.py # BaseEngine ABC
│ │ └── ... # One file per engine
│ ├── config.py # Configuration & engine weights
│ ├── schemas.py # Pydantic models
│ ├── database.py # SQLite async storage
│ ├── cache.py # Content hashing & caching
│ ├── scraper.py # URL content extraction
│ ├── autoconfig.py # Hardware detection & profiling
│ ├── model_pool.py # Thread-safe model replica pools
│ └── queue_manager.py # Request queuing & backpressure
├── web/ # Web Frontend
│ ├── templates/ # Jinja2 templates
│ └── static/ # CSS, JS, images
├── extension/ # Chrome Extension (Manifest V3)
│ ├── manifest.json
│ ├── background.js
│ ├── popup/
│ └── content/
└── tests/ # Evaluation scripts
| Endpoint | Method | Description | Latency |
|---|---|---|---|
/api/quick-score |
POST | 6 engines (fast) | ~100-500ms |
/api/paragraph-score |
POST | Per-paragraph heat map | ~1-3s |
/api/scan/snippets |
POST | Batch scan (1-30 snippets) | ~500ms |
/api/analyze |
POST | Full 23-engine analysis | ~3-8s |
/api/engines |
GET | Engine metadata | instant |
/api/recent |
GET | Recent reports | instant |
/api/report/{id} |
GET | Full report data | instant |
/api/queue/status |
GET | Queue capacity | instant |
curl -X POST http://localhost:8000/api/quick-score \
-H "Content-Type: application/json" \
-d '{"text": "Your text to analyze here..."}'Response:
{
"score": 72.3,
"verdict": "ai",
"confidence": "high",
"engines": [...],
"elapsed_ms": 340.2
}| Engine | Model | Notes |
|---|---|---|
| TMR Detector | RoBERTa-base | RAID-trained, 97.3% accuracy |
| ReMoDetect | DeBERTa | Targets RLHF-aligned LLMs |
| Fakespot | RoBERTa-base | APOLLO system, best discriminator |
| BERT-tiny RAID | BERT-tiny (4.4M) | Ultra-fast inference |
| Desklib DeBERTa | DeBERTa-v3-large | Trained on GPT-4/Claude |
| SuperAnnotate | RoBERTa-large | Low false-positive optimized |
| OpenAI Detector | RoBERTa | Original OpenAI detector |
| ChatGPT Detector | RoBERTa | ChatGPT-specific |
| E5-Small | E5 + LoRA | 33M params, embedding-based |
| Engine | Method |
|---|---|
| Binoculars | Cross-entropy ratio between two LMs |
| Fast-DetectGPT | Conditional probability curvature |
| Perplexity | GPT-2 perplexity scoring |
| Cross-Perplexity | Multi-model perplexity comparison |
| GLTR | Token rank distribution |
| Log-Rank | Average log-rank under GPT-2 |
| DivEye | Surprisal diversity |
| Engine | Signal |
|---|---|
| Burstiness | Per-sentence perplexity variance |
| Linguistic Markers | AI-preferred phrases ("delve", "tapestry"...) |
| Structural | Em-dash usage, sentence uniformity |
| Vocabulary | Type-token ratio, hapax legomena |
| Formulaic | Cliche openings/closings |
| Readability | Cross-paragraph consistency |
| Sentiment | Hedging & forced balance |
The final score is calibrated, not a simple average. Key principles:
- Fakespot-dominant weighting -- Fakespot has the largest human/AI gap (32%) and anchors the calibration
- Unanimous-high skepticism -- When all ML classifiers agree on very high scores, linguistic/formulaic engines act as tiebreakers (catches the "formal human text" false positive)
- Human signal adjustment -- Contractions, slang, first-person voice pull the score down; AI transitions and list patterns push it up
SlopTotal auto-detects CPU, RAM, and GPU on startup and picks a profile (lite, standard, or performance).
| Profile | RAM | CPU | GPU | Notes |
|---|---|---|---|---|
| Lite | 4 GB | 2 cores | None | All engines, slower |
| Standard | 8 GB | 4 cores | None | Default for most laptops |
| Performance | 16 GB+ | 6+ cores | CUDA optional | Pool replicas, max throughput |
High-RAM CPU servers (e.g. 64 GB, no GPU): you automatically get the performance profile. With no CUDA, all inference stays on CPU but you can run more concurrent workers and model pool replicas:
# Tune for a 64 GB CPU-only server
export SLOPTOTAL_PROFILE=performance
export SLOPTOTAL_TORCH_THREADS=8
export SLOPTOTAL_FULL_WORKERS=8
export SLOPTOTAL_SNIPPET_WORKERS=6
export SLOPTOTAL_MAX_CONCURRENT_FULL=4
export SLOPTOTAL_POOL_FAKESPOT=2
export SLOPTOTAL_POOL_TMR=2
./start.shFirst full scan downloads ~2 GB of models and may take 1–2 minutes while weights load; subsequent scans are much faster.
Hardware is auto-detected on startup. Override with environment variables:
SLOPTOTAL_TORCH_THREADS=4
SLOPTOTAL_FULL_WORKERS=6
SLOPTOTAL_SNIPPET_WORKERS=4
SLOPTOTAL_MAX_CONCURRENT_FULL=3| Symptom | Cause | Fix |
|---|---|---|
TypeError: unsupported operand type(s) for | on startup |
Python 3.9 or older | Use Python 3.10+ (python3.11 -m venv venv) |
ModuleNotFoundError: No module named 'bs4' |
Missing dependency | pip install -r requirements.txt (includes beautifulsoup4) |
Model loading failed / tokenizer enum errors |
Outdated tokenizers (<0.19) |
pip install -U 'transformers>=4.46' 'tokenizers>=0.21' and restart |
| Old scans still show engine failures | Cached report from before fix | Restart server (auto-purges stale cache) and run a new scan |
| Engines stuck on "PENDING" in UI | Viewing an old report URL | Go to / and submit a fresh analysis |
Verify all engines loaded:
curl -s http://localhost:8000/health | python3 -m json.tool
# Expect: "status": "healthy", "engines": 23See TODO.md for planned engines — including Qwen and Gemma classifiers and perplexity models optimized for high-RAM CPU servers.
Similar tools
- distil-labs/distil-ai-slop-detector — 270M Gemma model, runs in the browser
- Flamehaven01/AI-SLOP-Detector — static analyzer for AI-generated code (complementary to text detection)
- GLTR — visual token-rank inspection (inspiration for our GLTR engine)
Papers & benchmarks
- RAID benchmark (ACL 2024) — adversarial AI text detection dataset; several SlopTotal engines are RAID-trained
- Detecting the Machine (2026) — cross-architecture detector benchmark; ensemble methods outperform single detectors
- EditLens / Greyscope — human vs. AI-edited vs. AI-generated classification (candidate Qwen engine)
Guides
- Detecting AI Slop: Techniques & Red Flags — perplexity, classifiers, and ensemble approaches
The SlopTotal Chrome extension is maintained as a separate open-source repository:
Features:
- Scans Google search results inline with AI probability badges
- Scans LinkedIn feed posts with AI detection
- Quick-score popup for any page or selected text
- Right-click context menu integration
- Configurable API — point at any SlopTotal backend
Install from the extension repo or load extension/ as an unpacked extension for development.
This project ships with 11 specialized AI agents that can autonomously navigate, build, test, review, and ship contributions. They work with any AI coding assistant — Claude Code, Cursor, GitHub Copilot, ChatGPT, Gemini, Windsurf, or programmatic API calls. Anyone who clones this repo gets access to them automatically.
sloptotal-expert # Understand the codebase
sloptotal-completionist # Find what's missing or broken
sloptotal-feature-builder # Build new engines, endpoints, pages
sloptotal-test-writer # Create tests with proper patterns
sloptotal-reviewer # Code review before PR
sloptotal-optimizer # Performance, SEO, accessibility
sloptotal-deployer # CI/CD and deployment
sloptotal-open-source # GitHub templates and discoverability
sloptotal-extension-extractor # Extract extension to its own repo
sloptotal-pr-creator # Git workflow and PR creation
sloptotal-contribute # Master orchestrator for end-to-end workflows
See docs/ai-agents/ for full documentation, workflow pipelines, and usage examples.
Contributions are welcome! See CONTRIBUTING.md for development setup, code style, and how to add new detection engines.
MIT