The speaker champions GLM-4.7 as a highly cost-effective and swift AI model for background coding tasks, positing it as superior to Sonnet and significantly cheaper than Opus, despite being slightly less capable than the latter. This preference stems from a need for an economical agent for long-running processes that doesn't deplete financial resources. For orchestrating these agents, Conductor is the chosen tool on macOS, with alternatives like Claudia (now Opode) or Conduit recommended for cross-platform users.
Here's a structured summary of the workflow:
- 💰 Cost-Effectiveness: GLM-4.7's coding plan, costing approximately $280 annually, is drastically more affordable than equivalent Claude plans. This makes it ideal for sustained background coding, avoiding rapid usage limit consumption, especially when the speaker already maintains a Verdant subscription for primary coding.
- ⚙️ Conductor Setup: Configuring Conductor involves obtaining a GLM API key (a $3 plan is suggested for initial testing). Users can either configure their Claude Code settings, which Conductor automatically integrates (ensuring Sonnet and Opus defaults are set), or directly set GLM-4.7 specific environment variables within Conductor's settings to redirect all model calls.
- 🌳 Git Work Trees: The workflow heavily relies on Git work trees, which are essential for maintaining isolated project branches and preserving historical states. Conductor natively leverages this, creating separate branches (e.g., named after cities) for each task. This streamlines code changes and facilitates pull request (PR) creation, mirroring Verdant's robust work tree functionalities.
- 🪜 Workflow Steps: A typical task begins by creating a new Git branch and initiating a thread in Conductor. In "plan mode," the speaker collaborates with the AI to formulate an implementation strategy, benefiting from Conductor's prompts for follow-ups. Subsequently, implementation proceeds with Conductor managing auto-approval and Git operations. Code review is then conducted within PRs, focusing on overall architectural soundness and code quality, rather than minute individual changes.
- 🖥️ Advantages: This setup offers substantial cost savings through GLM-4.7, significantly improved memory efficiency compared to running full VS Code, and a heightened focus by dedicating the environment solely to task execution.
Takeaway: This integrated workflow, combining the cost-efficiency of GLM-4.7 with the robust task management of Conductor and Git work trees, provides a highly effective and streamlined approach to AI-assisted software development, particularly for resource-intensive and long-duration coding endeavors.