Get More Done in Less Time with a GPT-Powered Team Get More Done in Less Time with a GPT-Powered Team

Create content, debug code, generate user personas, get cloud advice & more with's AI-powered team of copywriter, software engineer...


3 min read

Hey there, I'm excited to share with you my latest project, I've been working on this app that uses the ChatGPT model to power an AI team consisting of a Copywriter, software engineer, product manager, and cloud consultant. With, you can accomplish tasks such as generating social media content, blog posts, emails, website landing page content, debugging code, generating code, creating user personas, getting cloud tips and advice on cost-savings and much more (you are likely to see more minds by the time you have read this post).

The inspiration behind came from my desire to automate parts of my personal workflow. I wanted to create a platform that was both easy to use and powerful enough to handle complex tasks. My goal was to build an AI companion that could help users of all levels, from beginners to experienced professionals. That's why I segmented into what I like to call minds, enabling users to select the tasks they want to focus on.

User Experience

Using is quite a breeze. Letโ€™s take a quick example, you are a solopreneur who wants to share a LinkedIn post on a recent product youโ€™ve built - ๐Ÿ˜œ. As seen in the screenshots below, itโ€™s as easy as heading on to, selecting the copywriter in the left Nav, briefly talk about what you want in your post, select the LinkedIn Task and hit Generate and watch the magic happen.

Technical Implementation Details

To build, I used a variety of technologies, including the SolidJS UI framework, the FastAPI backend Python framework, and PostgresDB which drives the SolidJS dynamic UI. I wanted to try out a UI library outside my usual go-to - React. I chose SolidJS because it's lightweight, fast, and easy to learn, and I appreciate its reactive programming model, which is similar to React with a few minor differences.

However, I did encounter some quirks around the concept of accessors and setters for reactivity in SolidJS. Once I got the hang of it, the reactivity feature of SolidJS became a breeze. For example, in the code snippet from the project below, rather than using the createSignal value (equivalent of React useState) as is, you have to call it as a function to get its actual value - due to the concept of accessors.

import { Component, createSignal } from 'solid-js';

const Post: Component<PostProps> = (props) => {
  const [showAlert, setShowAlert] = createSignal(false);

  const toggleAlert = () => {
    setTimeout(() => {
    }, 3000);

    return (
        <Box marginY={2}>
            {showAlert() && (
        <Alert severity="success" color="info">
        startIcon={<AssignmentIcon />}
        onClick={() => {
            String(props.value).replace(/&nbsp;/g, ' ')
        sx={{ marginLeft: 'auto' }}

I also used FastAPI, which made it a breeze to generate Swagger documentation and client libraries for the API. At its core, are engineered and optimised prompts which take the input specified by the user and use this to make calls to OpenAI models as seen below. The result obtained is then processed and returned to the user.

import openai as openai
from config import getenv

openai.api_key = getenv("OPENAI_KEY")

response = openai.Completion().completion_client.create(
result = response["choices"][0]["text"]

Looking Forward

Overall, building has been a rewarding and exciting experience, and I'm excited to share it with you. I believe that this platform will be a valuable tool for many users, enabling them to save time and effort while accomplishing complex tasks. In the coming weeks, I want to make it possible for users to build their own minds on Think Notion for AI assistants! ๐Ÿ™‚

If you'd like to give a try, head over to the website, and let me know what you think!

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