Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models
Abstract
The integration of Large Language Models (LLMs) into software development environments has significantly enhanced developer productivity through intelligent code generation. However, securely and efficiently executing AI-generated code remains a challenge due to potential security risks and infrastructure overhead. This paper presents the design and implementation of a MERN-based web application that integrates the Gemini LLM for multi-language code generation and utilizes browser-based Web Containers for secure sandboxed execution. The proposed system eliminates the need for local setup and server-side execution by leveraging client-side isolated runtime environments. Performance evaluation demonstrates reduced backend load and reliable multi-user execution.
Keywords
Large Language Models, AI Code Generation, MERN Stack, Web Containers, Sandboxed Execution, BrowserBased IDE
Citations
IRE Journals:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni "Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 362-369 https://doi.org/10.64388/IREV9I10-1715992
IEEE:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni
"Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715992
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