Build Anything with OpenAI o3, Here’s How

Key Concepts

• OpenAI O3 and O4 Mini: New, more powerful AI models from OpenAI.
• Codex CLI: A new open-source repository from OpenAI; an autonomous AI coding agent that can run in your terminal.
• Multimodal Capabilities: The ability of AI models to process and reason about different types of data, such as text and images.
• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions.
• Upwork Projects: Real-world projects posted on the Upwork platform that can be completed using AI models.
• Vibe Learning: Learning by interacting with AI models and having them teach you concepts as you go.
• Full Auto Mode (YOLO Mode): A mode in Codex CLI where the AI agent is given full autonomy to complete tasks without requiring approval for each step.
• Cursor: An IDE (Integrated Development Environment) used for coding.
• Cond Environment: A virtual environment used for managing Python packages and dependencies.
• Poetry: A Python tool for dependency management and packaging.
• MVP (Minimum Viable Product): A version of a product with just enough features to satisfy early customers and provide feedback for future development.

Benchmarks and Model Improvements

• AME 2024: A competition-level mathematics benchmark where O4 Mini outperformed O3.
• Codeforces: A competitive programming platform where scoring 2700 is equivalent to being in the top 200 programmers in the world. The new models show significant improvement in coding capabilities.
• SWE Lancer Benchmark: Measures the ability of AI models to complete real Upwork projects. O3 and O4 Mini have been able to complete projects worth significantly more money than previous models.
• Scaling and Compute: OpenAI found that more compute leads to better performance in reasoning models, similar to their findings with GPT models.
• Latency and Cost: O3 delivers much higher performance than O1 at the same latency and cost.

Image Analysis Capabilities

• Location Identification: O3 can identify the location of a place in the world from just a simple screenshot.
  • Example: Identifying a specific region in South Africa from an image from a Rainbolt video.
• Zooming and Reasoning: O3 can zoom into images and use the zoomed-in portions for reasoning.
  • Example: Analyzing a physics paper by zooming into different parts of the paper, reading paragraphs, and looking at charts.
• Contract Analysis: O3 can analyze images of contracts, look through different parts, and provide detailed analysis.

Completing Upwork Projects with O3 and Codex CLI

• Finding Upwork Listings: Using O3 to browse the web and find simple Upwork listings that can be easily built with Codex CLI.
• Codex CLI Setup:
  1. Open the terminal.
  2. Install Homebrew (if not already installed).
  3. Check for Node.js and Git (install if necessary).
  4. Create a folder for the project.
  5. Navigate to the folder in the terminal.
  6. Run npm install -g @openai/codex to install Codex CLI.
  7. Set the OpenAI API key using export OPENAI_API_KEY=<your_api_key>.
  8. Run a smoke test using codex "print hello codex".
• Git Commit to Changelog Project:
  • Identified as an easy project to complete with O3 and Codex CLI.
  • The project involves creating a CLI tool that analyzes Git commits and generates a changelog.
• Full Auto Mode Demonstration:
  • Using Codex CLI in full auto mode to create a fancy to-do list app.
  • The AI agent handles the entire process, from setting up the project to creating the front end.
  • The resulting to-do list app includes features such as drag and drop, colorful tags, and light/dark mode.

Building a GitHub Analysis Tool

• Project Goal: To create a pip installable Python CLI tool that analyzes Git commits and generates a clear summary of the commits in a markdown file.
• Steps:
  1. Initialize a Git repository.
  2. Create a Conda environment.
  3. Install necessary packages (e.g., build).
  4. Create the project structure (e.g., src folder).
  5. Create Python files (log.py, llm.py, cli.py) with the necessary code.
  6. Add the OpenAI API key to an .env file in the project root.
  7. Build the project using python -m build.
  8. Run a smoke test to verify that the tool is working correctly.
• Troubleshooting:
  • Addressing errors related to missing dependencies, temperature settings, and API key access.
  • Using O3 to explain the errors and provide solutions.
• Outcome: A working CLI tool that generates a report summarizing the Git commits.

Notable Quotes

• "Anyone that tells you like AI progress is slowing down, they don't know what they're talking about and they clearly are not paying attention."
• "I'm actually not sure what I'm going to ask what is the purpose of this line what are we trying to achieve maybe we're just testing if it works i'm not sure i'm going to ask GBT why it suggested that but uh this was that boom print hello codex blah blah show command allow command okay so we have different options we can either allow this specific command or approve this exact command for the rest of the session or edit or give feedback okay so we have five different things we can actually do this is interesting"
• "We do live in the future and there's going to be two types of people the ones that don't do anything about it and the ones that do something about it and they're obviously going to greatly improve their life they're going to automate a lot of boring work they're going to make more money they're going to you know make new connections and just get further and further ahead while the others just watch from the sidelines obviously hopefully you guys are in the second second category"

Technical Terms

• Tokens: Units of text used by AI models for processing. O3 has a 200,000 token window.
• CLI: Command-Line Interface.
• API Key: A code used to authenticate requests to an API (Application Programming Interface).
• YAML: A human-readable data serialization language.
• IDE: Integrated Development Environment, a software application that provides comprehensive facilities to computer programmers for software development.
• Markdown: A lightweight markup language with plain text formatting syntax.
• Virtual Environment: An isolated space for Python projects to manage dependencies.
• Package Manager: A tool for automating the process of installing, upgrading, configuring, and removing computer programs.

Synthesis/Conclusion

The video demonstrates the power and versatility of OpenAI's new O3 and O4 Mini models. These models exhibit significant improvements in reasoning, coding, and multimodal capabilities. The video showcases how these models, combined with tools like Codex CLI, can be used to complete real-world Upwork projects and automate various tasks. The presenter emphasizes the importance of adapting to these new AI technologies and using them to enhance productivity and create new opportunities. The video also highlights the potential for AI to assist in learning and problem-solving, even for individuals without extensive technical expertise. The key takeaway is that AI progress is rapidly accelerating, and those who embrace these technologies will be well-positioned for success in the future.