founder-game-classifier

Founder Game Classifier

A trained classifier that identifies which of 6 founder games a piece of content belongs to.

Model Description

This model classifies text content into one of six "founder games" - patterns of communication and content creation common among founders, creators, and thought leaders.

The 6 Games

Usage

Installation

pip install founder-game-classifier

Basic Usage

from founder_game_classifier import GameClassifier

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

# Classify a single 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, "G3": 0.10, ...}

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]}...")

Get Aggregate Signature

Useful for analyzing a corpus of content:

texts = load_my_blog_posts()  # List of strings
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}

Model Architecture

• Embedding Model: all-MiniLM-L6-v2 (384 dimensions)
• Classifier: Logistic Regression (sklearn)
• Manifold System: Mahalanobis distance to game centroids (optional)

Training Data

The model was trained on labeled founder content spanning:

• Podcast transcripts
• Blog posts
• Twitter threads
• Newsletter content

Training used a multi-stage pipeline:

1. Text chunking and span extraction
2. LLM-assisted labeling with human verification
3. Embedding generation
4. Classifier training with cross-validation

Performance

Validated on held-out test set:

The model performs best on clear examples of each game and may show lower confidence on boundary cases or mixed content.

Limitations

• Trained primarily on English content from tech/startup domain
• May not generalize well to non-business contexts
• Short texts (<50 words) may have lower accuracy
• Cultural and domain biases from training data

Citation

@misc{guinan2024foundergameclassifier,
  title={Founder Game Classifier},
  author={Leo Guinan},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/leoguinan/founder-game-classifier}
}

License

MIT License - free for commercial and non-commercial use.

Files

• classifier.pkl - Trained LogisticRegression model (19KB)
• label_encoder.pkl - Label encoder for game classes (375B)
• metadata.json - Model metadata and configuration (143B)
• game_manifolds.json - Manifold centroids and covariances for geometric analysis (29MB)