The field of code generation using Large Language Models (LLMs) is rapidly evolving, with a focus on improving the efficiency, scalability, and accuracy of these models. Recent developments have centered around addressing the challenges of composing new APIs from large libraries, enhancing the reasoning capabilities of LLMs, and evaluating the true coding competence of these models. Notably, researchers are exploring the integration of techniques such as Automatic Prompt Optimization and Reinforcement Learning to refine the code generation process. Additionally, there is a growing interest in designing lightweight, autonomous agents that can faithfully evaluate the coding capabilities of LLMs without relying on complex, hand-crafted workflows. These advancements have the potential to significantly impact the field of software development, enabling more efficient and effective code generation. Noteworthy papers include: APRIL, which achieves substantial improvements in API synthesis through the combination of LLM-based synthesis with Automatic Prompt Optimization and Reinforcement Learning. Lita, which introduces a lightweight, autonomous agent that operationalizes the principle of minimizing manual design while retaining the essential elements of a fully autonomous agent, achieving competitive or superior performance compared to workflow-based and agentic baselines.