A reflection on the arrival of Artificial General Intelligence (AGI), arguing that its 'general' nature distinguishes it from previous purpose-built AI models.
A reflection on the arrival of Artificial General Intelligence (AGI), arguing that its 'general' nature distinguishes it from all previous purpose-built AI models.
Explores how AI language models shift a programmer's role from writing code to managing context and providing detailed specifications.
Analyzes LLM APIs as a distributed state synchronization problem, critiquing their abstraction and proposing a mental model based on token and cache state.
Explores how advanced AIs use 'chains of thought' reasoning to break complex problems into simpler steps, improving accuracy and performance.
A guide to benchmarking language models using a Jupyter Notebook that supports any OpenAI-compatible API, including Ollama and Foundry Local.
Explains why standard language model benchmarks are insufficient and how to build custom benchmarks for specific application needs.
A tutorial on building a transformer-based language model in R from scratch, covering tokenization, self-attention, and text generation.
Learn how to accurately calculate token counts for strings using language models with a provided Jupyter Notebook tool.
Analyzes the latest pre-training and post-training methodologies used in state-of-the-art LLMs like Qwen 2, Apple's models, Gemma 2, and Llama 3.1.
