用本地工具和云模型搭建个人 AI 工作台Building a Personal AI Workbench with Local Tools and Cloud Models

I like AI systems more when the human side is boring. The less time I spend remembering where a prompt lives, the more time I have for actual judgment.

What lives in the workbench

The stack stays small on purpose:

• Local files for notes, specs, and draft outputs
• One or two model providers, not five
• A visible review step before anything ships
• Simple scripts for repeatable tasks

A narrow workflow

export const workbench = {
  intake: "capture the problem in plain language",
  draft: "let the model propose a first pass",
  review: "check the result against a short rubric",
  ship: "publish only after the review passes",
} as const;

Why this stays usable

The workbench is not trying to be an operating system. It is just enough structure to keep the work legible:

1. The prompt is visible.
2. The schema is visible.
3. The output is visible.
4. The handoff is visible.

That makes agent work calmer. It also makes debugging far less dramatic, which I appreciate more than I probably should.

The real constraint

The hard part is not model choice. It is deciding where human judgment still matters. Once that boundary is clear, the rest becomes a composition problem.