🗓️ 48 hours of building (24-26th March)
💻 Create innovative new apps with OpenAI's latest ChatGPT and Whisper APIs
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⚒️ Combine new GPT 3,5 Turbo and Whisper APIs to build your AI app
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ChatGPT API & Whisper API Global Hackathon

No-rank

Apprentice

ianpenrod

Worktern

alainaparr

michaelandrewc

Josh_Nguyen

meerm

xElysia

Our product takes in a job description and helps prepare a user for a job interview. The model uses the job description searches through a database to find the most relevant documents to give chatGPT context. Then it provides them interview questions. After the interview, which uses the Whisper API to transcribe the user's voice response to the interview question, the model provides feedback by looking using the question and the user's response to find relevant documents to give ChatGPT context on what makes a good answer. After the user has completed all interview questions. They are given overall feedback, and a final score on their performance.

Our product takes in a job description and searches through a database to find the most relevant documents to give chatGPT context. Then it provides them interview questions. After the interview, the also get feedback based on a similar database

Interview Perpartation

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

Whisper

ChatGPT is a large language model trained by OpenAI to generate human-like text in a conversational style. It is a variant of the GPT-3 model, which was specifically designed to be used to generate text in response to user input. The ChatGPT model has been trained on a vast amount of text data, including conversations and other types of human-generated text, which allows it to generate text that is similar in style and content to human conversation. ChatGPT can be used to generate responses to questions, code, make suggestions, or provide information in a conversational manner, and it is able to do so in a way that is often indistinguishable from human-generated text. The initial model has been trained using Reinforcement Learning from Human Feedback (RLHF), using methods similar to InstructGPT, but with slight differences in the data collection setup. The model is trained using supervised fine-tuning, where human AI trainers provided conversations in which they played both sides—the user and an AI assistant. The trainers would have had access to model-written suggestions to help them compose their responses.

Ian Penrod@ianpenrod

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🤝 Top Collabolators

Alaina Parr

Michael Cooley

Joshua Nguyen

Meer Mustafa Abbas

🤓 Submissions

Interview Perpartation

👌 Attended Hackathons

ChatGPT API & Whisper API Global Hackathon

📝 Certificates

ChatGPT API & Whisper API Global Hackathon | Certificate