The field of code-enabled language models is rapidly advancing, with a focus on improving reasoning capabilities, code generation, and editing. Researchers are exploring new methods to enhance the performance of large language models (LLMs) in code-related tasks, such as incorporating code execution, program synthesis, and visual-programmatic interfaces. These innovations have the potential to significantly improve the efficiency and effectiveness of LLMs in coding tasks. Noteworthy papers in this area include: Once Upon an Input, which introduces Per-Instance Program Synthesis to improve multi-step reasoning, and JanusCoder, which establishes a visual-programmatic interface for generating code from textual instructions, visual inputs, or a combination of both. These advancements are expected to have a significant impact on the development of more sophisticated and human-like coding capabilities in LLMs.