Links

• Notes and resources at ocdevel.com/mlg/mla-24
• Try a walking desk stay healthy & sharp while you learn & code
• Try Descript audio/video editing with AI power-tools

Tool Use in AI Code Agents

• File Operations: Agents can read, edit, and search files using sophisticated regular expressions.
• Executable Commands: They can recommend and perform installations like pip or npm installs, with user approval.
• Browser Integration: Allows agents to perform actions and verify outcomes through browser interactions.

Model Context Protocol (MCP)

• Standardization: MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools.
• Implementation:
  • MCP Client: Converts AI agent requests into structured commands.
  • MCP Server: Executes commands and sends structured responses back to the client.
• Local and Cloud Frameworks:
  • Local (S-T-D-I-O MCP): Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres.
  • Cloud (SSE MCP): SaaS providers offer cloud-hosted MCPs to enhance external integrations.

Expanding AI Capabilities with MCP Servers

• Directories: Various directories exist listing MCP servers for diverse functions beyond programming. modelcontextprotocol/servers
• Use Cases:
  • Automation Beyond Coding: Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management.
  • Creative Solutions: Encourages innovation in automating routine tasks by integrating diverse MCP functionalities.

AI Tools in Machine Learning

• Automating ML Process:
  • Auto ML and Feature Engineering: AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions.
  • Pipeline Construction and Deployment: Facilitates the use of infrastructure as code for deploying ML models efficiently.
• Active Experimentation:
  • Jupyter Integration Challenges: While integrations are possible, they often lag and may not support the latest models.
  • Practical Strategies: Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency.
• Action Plan for ML Engineers:
  • Setup structured folders and documentation to leverage AI tools effectively.
  • Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows.