Section 001, Coding exercises, speech-to-text/dictation software, handwriting detection, AI
This document proposes a tool to help professionals and students entering the development workforce practice whiteboard-style interview questions.product will generate interview-style coding questions and prompt the user to reason out their solution, either through text or voice dictation, and generate feedback on the thought process to guide the user through the correct thought process to solve the problem. The user can then attempt the problem with actual code or using handwriting dictation to determine correctness, optimization, and efficiency and then provide the feedback.
This app will:
Generate interview questions
Interpret user written input or spoken input
Give feedback to user prior to attempting the problem
Interpret user’s solution to problem and analyze correctness and optimization
Detail specifically where user can improve
For ease of access and simplicity of code, I believe this would best be done as a web app with optimization for tablets where users can actually write out their code with a stylus or their finger. This would ideally make the product available across both mobile and desktop platforms and help with accessibility if the user is unable to use any specific type of device. I would propose JS/React for the front end with a Python backend for processing.
This project will ideally make use of various open source softwares in order to meet project requirements. For speech to text dictation, OpenAI’s open source Whisper could be leveraged, as well as Open AI for the language processing. This product hopes to improve on existing technologies used to test coding aptitude for interviews such as LeetCode or Codewars which simply use unit testing to determine if a solution is correct or incorrect, rather than testing the thought process of the user which is a major part of interview whiteboard questions.
This project would require background knowledge of large language models and research into the most efficient uses for this specific problem. Additional research into how best to implement handwriting detection and pseudocode detection would additionally be needed. Additionally, members of the team would need access to a tablet with a stylus for testing purposes.
Dean Roos |
Ben Stephenson |
Vince Lubkan |
Renxuan Yao |
Dhruvil Patel |