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@LiorOnAI

The real breakthrough isn't that Computer can handle complex projects. It's that it runs 19 different models in parallel, each working on different pieces of your task at the same time. Most AI agents work like a single person doing everything sequentially: research, then write, then code, then deploy. Computer works like a team where everyone starts simultaneously. One model researches APIs while another drafts documentation while a third writes code. A coordinator (Opus 4.6) assigns each subtask to whichever model is best at that specific job: Gemini for research, Nano Banana for images, Veo 3.1 for video, ChatGPT 5.2 for long-context recall. When one agent hits a problem, Computer spins up a new specialist agent to solve it without stopping the others. Everything runs asynchronously in isolated environments with filesystem access, browser control, and API connections. This architecture unlocks three things that were impractical before: 1. Month-long autonomous projects that run in the background and self-correct 2. Multi-domain work where you need world-class performance in research AND design AND code simultaneously 3. True cost control since you pick which model handles which subtask and set spending caps Every other AI company now faces a choice: build orchestration infrastructure to coordinate multiple models, or accept being positioned as a single-purpose tool.

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