From Bugs to Hugs: Decoding LLMs Tools and Extensions for Constrained Coding

Computational notebooks allow developers, data scientists, and programmers to express their ideas, develop software applications, and write various programs. Developers and researchers rely substantially on these computational notebooks. However, these notebooks raise concerns such as complex codebases and challenges with clarifying coding structures. The domain of Artificial Intelligence (AI) generative has seen significant advancements in the past few months, leading to the creation of numerous tools and libraries aimed at enhancing the coding experience. The objective of this paper is to explore and enhance an existing extension explicitly developed for computational notebooks. This extension aims to enhance the software development experience by providing code generation and contextual understanding using LLM. Developers can anticipate a streamlined, effective, and satisfying experience that will alter their involvement with programming languages. By examining the features and functionalities of this extension, we aim to contribute to the ongoing efforts to improve the coding experience.