How to Run OpenCode Inside an Autonomous Claude Code AI Agent

· Source: All About AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

An AI agent running on a Mac Mini was taught a new skill: autonomously benchmarking large language models (LLMs) on creative tasks, generating comparison videos, and posting them to X. The process involves using Cloud Code to execute OpenCode CLI commands, allowing parallel testing of multiple LLMs (e.g., GLM5, Opus 4.6, Gemini 3 Pro, Minimax 2.5) with the same prompt. Outputs, such as HTML files for a "Space Invader game," are saved and then converted into a grid-style comparison video using Remotion. This video, showcasing how different models respond to the same creative prompt, is then drafted for autonomous posting on X, complete with a descriptive text comparing the models' performance. This setup automates the entire workflow from testing to social media sharing.

Key takeaway

For AI Engineers evaluating LLM performance on creative tasks, this automated benchmarking and visualization workflow offers a streamlined approach. You can configure your AI agent to run parallel tests across various models, generate comparative videos, and even draft social media posts, significantly reducing manual effort and accelerating insights into model capabilities. Consider implementing a similar system to continuously monitor and share LLM advancements.

Key insights

Automate LLM benchmarking and comparison video generation for social media sharing using an AI agent.

Principles

Method

Use Cloud Code to run OpenCode CLI with specified models and prompts in parallel. Save HTML outputs, convert to grid video with Remotion, and draft social media posts for autonomous sharing.

In practice

Topics

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by All About AI.