https://signal-and-syntax.com/tags/embeddings/Recent content in Embeddings on Signal & SyntaxHugoen-usTue, 31 Mar 2026 06:00:00 -0700https://signal-and-syntax.com/posts/discrete-math-in-large-language-models/Tue, 31 Mar 2026 06:00:00 -0700https://signal-and-syntax.com/posts/discrete-math-in-large-language-models/A recent LinkedIn post from Michael Palmer described how discrete mathematics is the foundation for how computers reason about problems. That thread got me thinking about just how many discrete math concepts show up inside systems that seem purely statistical. LLMs are often described in terms of neural networks, gradient descent, and probability distributions. If you’ve taken discrete mathematics and wondered what it has to do with modern AI, the answer is: more than you’d expect.https://signal-and-syntax.com/posts/how-large-language-models-think/Tue, 07 Oct 2025 06:00:00 -0700https://signal-and-syntax.com/posts/how-large-language-models-think/When you enter a sentence into a Large Language Model (LLM) such as ChatGPT or Claude , the model does not process words as language. It represents them as numbers. Each word, phrase, and code token becomes a vector — a list of real-valued coordinates within a high-dimensional space. Relationships between meanings are captured not by grammar or logic but by geometry. The closer two vectors lie, the more similar their semantic roles appear to the model.