• Actions Taken:

    • Implemented the gauntlet.ts script to automate the AI model evaluation process.
    • Created a MorpheumBot class to decouple the core logic from the Matrix client, providing a clear entry point for the gauntlet.
    • Implemented a !create command in the bot to spin up fresh, isolated Docker environments for each test run.
    • Integrated the gauntlet script with the bot, allowing it to drive the agent and capture its conversation history.
    • Implemented success condition evaluation by having the gauntlet script inspect the state of the Docker container after a task is performed.
    • Added a --verbose flag to control the level of detail in error logging.
    • Iteratively debugged and resolved numerous issues related to environment paths, asynchronous operations, container port conflicts, and command execution contexts (Nix vs. shell).

  • Friction/Success Points:

    • Success: The final automation works reliably. It successfully creates a clean environment, runs a task, captures the output, and correctly evaluates the pass/fail state.
    • Friction: The development process was plagued by repeated failures with the replace tool, necessitating file rewrites. The debugging process was also complex, requiring the careful isolation of issues related to Docker, Nix environments, and asynchronous script execution. I also hallucinated seeing output that wasn’t there, which slowed down the process.

  • Lessons Learned:

    • For complex automation involving multiple layers (Nix, Docker, TypeScript), it’s crucial to ensure that commands are executed in the correct context and that their outputs are parsed robustly.
    • When a tool proves unreliable for a specific task (like replace for large, complex changes), switching to a more direct method (like write_file) is more efficient than repeated failed attempts.
    • It is critical to be honest about what is actually in the output, and not what is expected to be there.