https://tomarcher.io/learningpaths/ai-for-software-engineers/Recent content in AI for Software Engineers on Signal & SyntaxHugoen-usMon, 10 Nov 2025 06:00:00 -0700https://tomarcher.io/posts/how-large-language-models-handle-context-windows/Mon, 10 Nov 2025 06:00:00 -0700https://tomarcher.io/posts/how-large-language-models-handle-context-windows/When you have a long conversation with a large language model (LLM) such as ChatGPT or Claude , it feels like the model remembers everything you’ve discussed. It references earlier points, maintains consistent context, and seems to “know” what you talked about pages ago. But here’s the uncomfortable truth: the model doesn’t remember anything. It’s not storing your conversation in memory the way a database would. Instead, it’s rereading the entire conversation from the beginning every single time you send a message.https://tomarcher.io/posts/how-large-language-models-read-code/Mon, 06 Oct 2025 09:00:00 -0700https://tomarcher.io/posts/how-large-language-models-read-code/Developers are accustomed to thinking about code in terms of syntax and semantics, the how and the why. Syntax defines what is legal; semantics defines what it means. A compiler enforces syntax with ruthless precision and interprets semantics through symbol tables and execution logic. But a Large Language Model (LLM), reads code the way a seasoned engineer reads poetry, recognizing rhythm, pattern, and context more than explicit rules. The difference may seem subtle, but it has vast consequences.