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Introduction to Database Building with AI

  • 📊 The video introduces database.build, a tool that allows users to design database schemas, generate charts, and query CSV files by chatting with AI.
  • 🤖 The tool initially used Open AI's GPT 40 model, but now allows users to customize the AI model used.
  • 📈 The video aims to compare the performance of different AI models in building a database.

Customizing AI Models

  • 📊 Users can now customize the AI model used with database.build by adding a custom API provider, passing in the provider's API key, and choosing the desired model.
  • 📝 The system prompt can also be customized to get the best results from each model.

Comparison of AI Models

  • 🤖 GPT 40: Created a schema with four tables, including elves, elf assignments, orders, and toys. It required some prompting to add a fulfillment date.
  • 🤖 Claude: Created a schema with three tables, including elves, workshop assignments, and toys. It automatically created sample data and checked its work.
  • 🤖 Quin 2.5: Had some issues with executing SQL queries and required more prompting to create the desired tables.

Local Model Usage

  • 📊 Olama provides a local REST API for interacting with large language models completely offline on the user's machine.
  • 🤖 The video demonstrates how to use Olama with database.build to run a local model, Quin 2.5.

Security and Privacy

  • 🔒 The video explains how bring your own LLM (Large Language Model) works under the hood, with all settings and credentials saved locally in the browser.
  • 🔒 The requests made to each provider are done directly from the browser, ensuring security and privacy.

New Features

  • 📈 The video announces new features in database.build, including live share, deployments, and the ability to drag and drop SQL files.
  • 📊 These features are free and available on GitHub, with a blog post explaining how they work under the hood.
  • 📚 You can check out the database.build blog post for more information on the new features.