This Talk explores the world of coding assistants powered by language models (LLMs) and their use cases in software development. It delves into challenges such as understanding big code and developing models for context in LLMs. The importance of ranking and code context is discussed, along with the use of weak supervision signals and fine-tuning models for code completion. The Talk also touches on the evaluation of models and the future trends in code AI, including automation and the role of tasks, programming languages, and code context.