The field of AI-assisted code development is rapidly evolving, with a focus on improving code comprehension, generation, and review. Recent research has explored the use of large language models (LLMs) to enhance code exploration workflows, generate effective in-context examples, and assist in codebase generation. Additionally, there is a growing interest in developing multi-agent systems that can leverage LLMs to improve code generation and validation. Another area of research is the use of LLMs to automate code review and improve developer productivity. Overall, these advancements have the potential to significantly improve the efficiency and effectiveness of software development. Noteworthy papers include: AI-Guided Exploration of Large-Scale Codebases, which introduces a hybrid approach that integrates deterministic reverse engineering with LLM-guided visual exploration. What Builds Effective In-Context Examples for Code Generation, which systematically investigates the impact of various code features on in-context learning. Context Engineering for Multi-Agent LLM Code Assistants, which proposes a novel context engineering workflow that combines multiple AI components to improve code generation and validation. DeputyDev -- AI Powered Developer Assistant, which develops an AI-powered code review assistant to address inefficiencies in the software development process. Next Edit Prediction, which introduces the task of predicting the next edit in a sequence of related edits to provide seamless code edit suggestions.