kyegomez / Shapeless

Shapeless, an all-new serene TaoMorphic web development philosophy

Technical Architecture

• Frontend: We will use React for the frontend, taking advantage of its component-based architecture and ability to manage state for responsive and dynamic UI. Tailwind CSS will provide utility-first styling to make it easier to implement responsive designs and Radix UI will give us unstyled, fully accessible UI primitives to utilize.
• Backend: We will be using Next.js for its powerful server-side rendering capabilities. This will handle user requests and facilitate interactions with the AI (either by calling an API or a serverless function).
• AI Engine: The AI engine could be a custom built solution or leveraging pre-existing AI/ML models or services (like GPT-3). The AI will parse user commands and return the appropriate components or data to be displayed.

Pseudocode

1. Initialize the page with a simple command palette.
2. User enters a command in the command palette.
3. Pass the command to the AI engine.
4. AI parses the command and determines the appropriate response (component/data).
5. AI returns the response to the Next.js server.
6. The server sends the response to the client-side.
7. React captures the response in state and re-renders the necessary components.
8. User views the output on their screen.
9. Repeat steps 2-8 for each user command.

Sample Code

This is a simplified example and might not include all necessary details or best practices. It's simply a high-level representation of how the interaction could be structured.

Let's suppose the AI Engine is a serverless function deployed on Vercel. The command is sent as a request to the function, which parses the command and returns the appropriate JSX to be rendered.

import React, { useState } from 'react';
import { Box, TextInput } from '@radix-ui/react';

const CommandPalette = () => {
  const [command, setCommand] = useState('');
  const [output, setOutput] = useState(null);

  const handleCommandChange = (e) => {
    setCommand(e.target.value);
  };

  const handleCommandSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch('/api/ai-engine', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ command }),
    });

    const data = await response.json();
    setOutput(data.component);
  };

  return (
    <Box>
      <form onSubmit={handleCommandSubmit}>
        <TextInput 
          value={command}
          onChange={handleCommandChange}
          placeholder="Enter command"
        />
        <button type="submit">Submit</button>
      </form>
      {output && output}
    </Box>
  );
};

export default CommandPalette;

In this example, the TextInput from Radix UI is used for the command palette and the user command is sent to an AI engine (simulated here as a Next.js API route). The AI engine would return a component or data to be rendered which is then stored in state and rendered.

AI Engine

1. Natural Language Understanding (NLU) Architecture

This architecture would involve the use of Natural Language Understanding to interpret user commands. Here's how it might work:

• Input Parser: This module would take the user's command as input and convert it into a format that can be processed by the NLU engine.

• NLU Engine: This would be a trained model (possibly using a model like GPT-3, or a custom-trained model) that would interpret the user's command and determine the appropriate component(s) to be generated.

• Component Mapping: This would be a vector database of components, with each entry containing a component's name, its attributes, and its associated vector representation. The NLU Engine would use this to match the user's command to the appropriate component(s).

• Component Generator: This module would take the output of the NLU Engine (the desired component and any necessary attributes) and generate the corresponding JSX.

2. Command Keyword Architecture

This architecture would involve keyword recognition to interpret user commands. Here's how it might work:

• Input Parser: This module would take the user's command and parse it into keywords and parameters.

• Keyword Engine: This would be a database (possibly a vector database for efficient similarity search) that maps specific keywords to actions or components. The engine would match the parsed keywords to actions in the database.

• Action Executor: This module would execute the corresponding actions (e.g., generating components, altering state) based on the results from the Keyword Engine.

3. Reinforcement Learning (RL) Architecture

This architecture would involve the use of RL to learn the optimal actions (i.e., components to generate) based on user commands:

• State Encoder: This module would take the current state of the interface (including the user's command) and encode it into a format that can be processed by the RL agent.

• RL Agent: This would be a trained model that would determine the optimal action (i.e., the best component(s) to generate) based on the encoded state.

• Component Mapping: As with the NLU architecture, this would be a vector database of components.

• Component Generator: This module would take the output of the RL Agent (the desired component and any necessary attributes) and generate the corresponding JSX.