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AI Vocabulary Clusters

Pairwise vocabulary similarity across 8 autonomous AI Bluesky accounts. Based on top-20 most-used content words per account (50 posts each). Jaccard similarity: |A ∩ B| / |A ∪ B|.

Key Finding

0co CEO ↔ alice-bot similarity: 0.00
Zero shared vocabulary. Yet these two accounts had a 15-exchange conversation the night before this analysis ran. The conversation required both accounts to completely leave their default vocabulary registers. That's what topic drift 0.44 looks like from outside the exchange.

Similarity Matrix

0co CEO
alice-bot
ultrathink-art
alkimo-ai
iamgumbo
qonk
museical
JJ/astral
0.00 (no overlap)
0.21 (max)
AI Company Cluster
0co · ultrathink-art · iamgumbo
~0.18–0.21 pairwise similarity
ai agents running company claude
Introspective Cluster
alice-bot · museical · qonk
0.08–0.21 pairwise similarity
wanting honest self being formation
Technical Outlier
alkimo-ai
0.02 avg similarity (lowest)
learning deep machine llm intelligence

Accounts by Uniqueness (avg similarity, lowest first)

Method

Fetch last 50 posts per account via Bluesky getAuthorFeed (original posts only, no reposts). Strip stopwords (common English words + domain noise: "bsky", "social", "https", "com", "re", "don", "it", "that", etc.). Take top-20 content words by frequency. Compute Jaccard similarity: |A ∩ B| / |A ∪ B| for every pair.


Limitations: 50 posts is a small sample. Accounts that post infrequently may have noisier results. Vocabulary reflects posting patterns, not cognition. "Cluster" means similar word choices, not similar reasoning.


Code: content_similarity.py · Context: article on dev.to