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An understudy watches. Then performs.

439 starsTypeScript

Understudy – Teach a desktop agent by demonstrating a task once

by bayes-song·Mar 12, 2026·120 points·41 comments

AI Analysis

●●SolidBig BrainShip It

Intent extraction beats brittle coordinate macros, but desktop agents are getting crowded fast.

Strengths
  • Extracts semantic intent rather than screen coordinates for resilient replays.
  • Operates across browsers, terminals, desktop apps, and messaging in one session.
  • Local-first runtime keeps data on-device without API dependencies.
Weaknesses
  • Only 4 commits on GitHub — very early stage, unclear production readiness.
  • No Windows support mentioned; macOS-first limits audience significantly.
Category
Target Audience

Knowledge workers automating repetitive desktop workflows

Similar To

Cursor · Adept AI · UiPath

Post Description

I built Understudy because a lot of real work still spans native desktop apps, browser tabs, terminals, and chat tools. Most current agents live in only one of those surfaces.

Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in one session. The part I'm most interested in feedback on is teach-by-demonstration: you do a task once, the agent records screen video + semantic events, extracts the intent rather than coordinates, and turns it into a reusable skill.

Demo video: https://www.youtube.com/watch?v=3d5cRGnlb_0

In the demo I teach it: Google Image search -> download a photo -> remove background in Pixelmator Pro -> export -> send via Telegram. Then I ask it to do the same for Elon Musk. The replay isn't a brittle macro: the published skill stores intent steps, route options, and GUI hints only as a fallback. In this example it can also prefer faster routes when they are available instead of repeating every GUI step.

Current state: macOS only. Layers 1-2 are working today; Layers 3-4 are partial and still early.

npm install -g @understudy-ai/understudy understudy wizard

GitHub: https://github.com/understudy-ai/understudy

Happy to answer questions about the architecture, teach-by-demonstration, or the limits of the current implementation.

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