I am never coding the same way again

I am using GibsonAI to write 70% of my python code, in minutes. GibsonAI data modeler will take instructions in natural language and converts them into executable python code. This instantly reduced development time from days or weeks to minutes. Unlike your typical LLM passthroughs or co-pilots, GibsonAI's pair programmer gives developers the control to write very specific code that works for the use case.

Example: Building the Parenthood App

I’m currently working on a new project called Parenthood. To start, I have a GitHub repository with no existing code and a MySQL database that is completely empty. I’ve set up a single entity, “parent,” using Gibson’s pair programmer, without any manual coding. This serves as a foundation for what i build next.

The Gibson pair programmer is incredibly versatile. As a command-line enthusiast, I love using Gibson through the terminal for its speed and efficiency.

To see Gibson’s capabilities, i created a new entity, “child,” using simple natural language commands:

1. Code Entity Child: When you tell Gibson to “code entity child,” it initializes by analyzing the current database context, which presently includes only the parent table. You can then start data modeling using natural language.

2. Add Attributes: You can specify attributes like first name and last name, marking them as required. Gibson handles both SQL and Python simultaneously, ensuring everything is up to standard.

3. Foreign Key Creation: Simply saying “FK parent” prompts Gibson to recognize the parent table, identify its primary key, and create an indexed foreign key automatically. This automation saves time and effort.

4. Add Unique Constraints: Need a unique key over parent ID, first name, and last name? Gibson can quickly implement this to ensure data integrity across your application.

Reviewing and Merging the Table

After building the “child” entity, you can easily review the table structure, which includes the parent ID, first name, last name, date of birth, and the relevant keys. Once satisfied, a quick “Gibson merge” command integrates the table into your project.

Deploying to the Database

With the entities in place, I instructed Gibson to load them into MySQL database, complete with SQL indexes and foreign keys. Following this, commands like “write base code,” “write models,” “write schemas,” and “write tests” allow Gibson to generate everything from SQL Alchemy models to unit tests in a matter of minutes.

Gibson AI compresses what would typically take hours, days, or even weeks of manual coding into a few minutes. With rapid data modeling and code generation, I have quickly built SQL Alchemy models, Pydantic schemas, and FastAPI routes, & ready to launch my app.

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