What Token Counts Can Tell Us About How Language Really Works
June 7, 2026
Counting characters, words, and GPT-style tokens across real books reveals something important: different tokenization methods expose completely different structural patterns...
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Why GPT-2 Treats “stable” and “ stable” as Separate Tokens
June 7, 2026
GPT-style tokenizers encode leading spaces as meaningful statistical markers, so “stable” and “ stable” are treated differently. Understanding this distinction...
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How Tokenization Shapes LLM Context Windows and Model Efficiency
June 7, 2026
Tokenization isn’t just a preprocessing step—it directly impacts how much meaningful text a large language model can handle and how...
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Why Modern LLMs Split Text Into Subwords Instead of Full Words
June 7, 2026
GPT-style tokenization works because it avoids two expensive extremes: character-level systems that waste context space and word-level systems that explode...
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