founder-game-classifier 0.1.0

Classify content into 6 founder games using trained ML models

Founder Game Classifier

Classify content into 6 founder games using trained ML models.

Installation

pip install founder-game-classifier

Quick Start

from founder_game_classifier import GameClassifier

# Load the model (downloads from Hugging Face Hub on first use)
classifier = GameClassifier.from_pretrained("leoguinan/founder-game-classifier")

# Classify text
result = classifier.predict("Here's a tactic you can steal for your next launch...")

print(result["primary_game"])      # "G2"
print(result["confidence"])        # 0.72
print(result["probabilities"])     # {"G1": 0.05, "G2": 0.72, ...}

The 6 Founder Games

Game	Name	Description
G1	Identity/Canon	Recruiting into identity, lineage, belonging, status
G2	Ideas/Play Mining	Extracting reusable tactics, heuristics; "do this / steal this"
G3	Models/Understanding	Building mental models, frameworks, explanations
G4	Performance/Competition	Winning, execution, metrics, zero-sum edges
G5	Meaning/Therapy	Healing, values, emotional processing, transformation
G6	Network/Coordination	Community building, protocols, collective action

Usage

Single Text Classification

result = classifier.predict("Here's the mental model I use...")

print(result)
# {
#     "primary_game": "G3",
#     "secondary_game": "G2",
#     "confidence": 0.68,
#     "probabilities": {"G1": 0.05, "G2": 0.22, "G3": 0.68, ...},
#     "primary_description": "Model/Understanding: building mental models...",
#     "secondary_description": "Idea/Play Mining: extracting reusable..."
# }

Batch Classification

texts = [
    "Here's the mental model I use for thinking about systems...",
    "Join our community of builders who are changing the world...",
    "I tried 47 different tactics. Here's what actually worked...",
]

results = classifier.predict_batch(texts)

for text, result in zip(texts, results):
    print(f"{result['primary_game']}: {text[:50]}...")
# G3: Here's the mental model I use for thinking about...
# G6: Join our community of builders who are changing...
# G2: I tried 47 different tactics. Here's what actual...

Aggregate Game Signature

Analyze the overall game distribution of a corpus:

# Load your content
texts = [
    open(f).read() for f in Path("my_blog_posts/").glob("*.txt")
]

# Get aggregate signature
signature = classifier.get_game_signature(texts)

print(signature)
# {'G1': 0.05, 'G2': 0.42, 'G3': 0.18, 'G4': 0.20, 'G5': 0.08, 'G6': 0.07}

Local Model Loading

If you have the model files locally:

classifier = GameClassifier.from_local(
    model_dir="./models/founder_classifier",
    manifolds_path="./game_manifolds.json",  # Optional
)

Model Details

• Embedding Model: all-MiniLM-L6-v2 (384 dimensions)
• Classifier: Logistic Regression
• Training Data: Labeled founder content (podcasts, blogs, tweets)

Requirements

• Python 3.9+
• sentence-transformers
• scikit-learn
• huggingface-hub

License

MIT License

Links

• Hugging Face Model
• GitHub Repository