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

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

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:

Text chunking and span extraction
LLM-assisted labeling with human verification
Embedding generation
Classifier training with cross-validation

Performance

Validated on held-out test set:

Metric	Score
Accuracy	0.78
Macro F1	0.74
Top-2 Accuracy	0.91

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)

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