Converting a PDF to markdown for an LLM means turning pages into text that keeps the structure a model needs: headings as headings, tables as tables, columns in reading order. Markdown is the usual target because models read it well, it is usually lighter than HTML, and a RAG pipeline can split it…
You do not need to understand backpropagation to run a large language model well in production. You need a smaller, more practical thing: the operator's mental model. What a token actually costs you, what a context window actually bounds, what temperature and top_p actually do, and why evals catch…
Running an open-source LLM on AWS sounds straightforward at first. Pick a model, deploy it, send requests, and you're done. But once you start thinking about production, things become more complicated. Where should the model run? Who manages the GPUs? How much control do you actually need? What…
Technical Reconstruction of LLM Training for High-Performance Chess Princeton researchers have developed a groundbreaking method to train large language models (LLMs) to achieve exceptional performance in complex tasks like chess, with the ability to explain their decisions. This innovation not…
I'm building a memory system for AI assistants. It stores what you tell it, forgets things on purpose, and keeps track of when a fact stopped being true. One small decision sits at the centre of it, and that decision is what this post is about. Every time a new fact comes in, the system has to ask…
Claude Opus 5 costs $5 per million input tokens whether a prompt arrives in English or in Russian. What changes between the two languages is how many tokens the same paragraph becomes, and the bill is that count multiplied by the price. A recent test on Habr measured the gap across 11 tokenizers.…
What Actually Happens During Speculative Decoding in LLMs Autoregressive language model generation is notoriously slow. When you run a 70-billion parameter model on an enterprise GPU, you might get 20 to 30 tokens per second. The intuitive assumption is that the GPU compute cores are sweating under…
