Finally, an AI Database That Actually Makes Sense

AI-Powered Database Features [00:00]

• The video introduces "Agentic Postgres" from Tiger Data, highlighting its AI features integrated directly into PostgreSQL databases.
• Key features include instant database forks, built-in vector search (similar to PostgreSQL search), and MCP server connectivity for agentic database operations.
• The fork feature allows for the creation of a safe copy of a database, enabling testing of code and changes without affecting production or development environments.
• A one terabyte database can be forked in under two seconds.

"So definitely stick around as I explain how it works, how to set it up for yourself and how you can start using it right away."

Demo: Agentic Database Modification [01:39]

• A demonstration shows a simple blog application connected to an AI-enabled PostgreSQL database hosted by Tiger Data.
• Using the Cursor AI agent connected via an MCP server, the agent can directly modify the database.
• The agent can perform tasks like summarizing database tables and their relationships upon request.

Demo: Instant Database Forking [03:11]

• The demonstration highlights the ability of the AI model to create a fork of the database.
• Forking the database takes approximately 2 seconds.
• A new service for the forked database appears, with identical tables and data to the original.
• This forked database can then be used by an agent for testing changes to schema, indexes, or new features without impacting the original database.
• Multiple agents can fork databases in parallel for independent work, improving productivity and safety.

"The reason why this is pretty impactful is because now all of my agents, if I'm running multiple of them in parallel, for example, can fork the database, start working on their new features without messing up the data or needing to create new sample data or anything along those lines."

Setting Up Agentic Postgres [04:51]

• A tutorial demonstrates how to set up a free service on Tiger Data.
• Users create an account on Tiger Data and navigate to create a new service, opting for the free tier.
• Upon service creation, connection details for the PostgreSQL database are provided, including a connection string and environment variable file.
• The setup involves downloading the database config and an environment variable file, which can be used in any programming language.

Connecting via AI Tool and MCP Server [06:40]

• To utilize the AI features, the connection is made via an AI tool, specifically by connecting an MCP server.
• This requires installing the Tiger CLI tool.
• The MCP server can be installed for various AI environments like cloud code, warp, or Cursor.
• Installation commands are provided for different operating systems (Windows, Mac, Linux).

Configuring the Tiger CLI and MCP Server [07:47]

• The process involves logging into Tiger CLI using tiger o login, which requires authorization through a web page.
• The database password is then saved using tiger db save password.
• The MCP server is installed using tiger mcp install, with options to select the target AI environment (e.g., Cursor).
• After restarting the AI environment (e.g., Cursor), the Tiger tools are visible in the settings.

Generating Sample Data with an AI Agent [11:08]

• The video demonstrates asking the AI agent to add sample data to the database using MCP tools, without writing code.
• A prompt requests the creation of fake users, comments, posts, and likes, with 200-300 entries.
• The agent successfully generates this data, populating the database with fake entries.
• The generated data can be verified by refreshing the application and observing the populated tables.

"Add a bunch of sample data to this database. I want fake users, fake comments, fake posts, and fake amounts of likes. Let's go with 200 to 300 fake entries. Use the MCP tools. Do not write any code? Just use the MCP tools to add this data."

Developing a New Feature Using Database Forking [12:43]

• The video showcases a more advanced use case: developing a new feature by forking the database.
• The goal is to implement admin user functionality, allowing an admin to delete, review, and edit data.
• The agent is prompted to fork the database and then implement this new feature.
• The new forked credentials are used, and a user is promoted to admin.
• An admin panel is accessed, showing management options for users, posts, comments, and likes.
• The ability to test deleting data in the fork without affecting the original database is highlighted.

"And then the cool thing is, right, I could test deleting all of these users. And that's not going to impact the first database that I had. It's just impacting the fork."

Benefits for Agentic Development and Productivity [15:04]

• The entire process, from data generation to feature development with forking, significantly enhances agentic development flows.
• Multiple agents can work concurrently on different forked databases, boosting productivity without risking data integrity.
• The MCP server alone is deemed highly useful for tasks like creating fake data and performing database operations more simply than traditional management tools.