Michael Cooley@michaelandrewc765

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.

Name/Alias of the artist: Michael Name of the prompt used/title of the artwork: A machine making a human, 4k digital art Name of tool used: API: newnative.ai To make prompts more in line with what the user likes, I took inspiration from a simulated annealing algorithm. The user begins by giving a prompt of what they'd like to see. From there, a list of synonyms is produced, along with 100 common words and acronyms. The prompt it tweaked slightly and different words are added, removed or moved within the prompt based on user feedback within each stage. This results in an evolving image prompt more toward the user's preference.

🗓️ 48 hours of building (24-26th March) 💻 Create innovative new apps with OpenAI's latest ChatGPT and Whisper APIs 💡 Learn from top AI professionals ⚒️ Combine new GPT 3,5 Turbo and Whisper APIs to build your AI app 🐱‍💻 Now is the time to register and let's get started!

A new text-to-image model called Stable Diffusion is set to change the game once again ⚡️. Stable Diffusion is a latent diffusion model that is capable of generating detailed images from text descriptions. It can also be used for tasks such as inpainting, outpainting, and image-to-image translations. Join us for a epic stable diffusion makers event 11-13 November 👊