Artificial Intelligence

You Do Not Need to Train Giant Models to Learn How LLMs Work

June 7, 2026

LLM Mechanisms Learning Guide Cover featuring interpretability pathways without model training.

Most foundational interpretability skills can be learned by analyzing pretrained models with lightweight experiments, modest hardware, and practical workflows rather...

Why Open-Source Language Models Still Feel Like Black Boxes

June 7, 2026

Open-Source LLMs - The Transparency Misconception

Open-source LLMs may expose their code and weights, but that does not automatically make their behavior understandable. The real challenge...

How Simple Machine Learning Methods Can Expose Hidden Patterns Inside LLMs

June 7, 2026

Reverse-Engineer LLM Behavior Using Simple Machine Learning Tools

Large language models may look impossibly complex, but many of their hidden behaviors can be studied using familiar machine learning...

Why LLMs Are High-Dimensional Systems, Not Simple Algorithms

June 7, 2026

Mechanistic interpretability concept exploring why large language models act as high-dimensional systems.

Understanding large language models isn’t about reading weights; it’s about analyzing emergent patterns across vast high-dimensional spaces, where behavior arises...

What Token Counts Can Tell Us About How Language Really Works

June 7, 2026

Token Counts and Language Structure Analysis Cover

Counting characters, words, and GPT-style tokens across real books reveals something important: different tokenization methods expose completely different structural patterns...

Why GPT-2 Treats “stable” and “ stable” as Separate Tokens

June 7, 2026

GPT-2 Tokenizer Analysis - Why Leading Spaces Change Token Identity

GPT-style tokenizers encode leading spaces as meaningful statistical markers, so “stable” and “ stable” are treated differently. Understanding this distinction...

How Tokenization Shapes LLM Context Windows and Model Efficiency

June 7, 2026

LLM Tokenization Compression impact on Context Windows and Model Efficiency

Tokenization isn’t just a preprocessing step—it directly impacts how much meaningful text a large language model can handle and how...

Why Modern LLMs Split Text Into Subwords Instead of Full Words

June 7, 2026

Subword Tokenization Engineering Tradeoffs in Modern Language Models Cover

GPT-style tokenization works because it avoids two expensive extremes: character-level systems that waste context space and word-level systems that explode...