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Contextual AI Computing

Reusability

AI that understands the full context of user activity over time rather than requiring explicit prompts for each interaction

How It Works

System continuously monitors user behavior and screen activity to build contextual understanding, then proactively suggests or executes relevant actions

Components

1

Implement continuous context monitoring across user devices

2

Build temporal understanding of user patterns and workflows

3

Create proactive suggestion engine based on context

4

Design simple trigger mechanism (like command+enter)

5

Ensure data persistence across sessions and applications

When to Use

When building AI assistants or productivity tools where user intent can be inferred from behavioral patterns and context

When Not to Use

For privacy-sensitive applications or when users prefer explicit control over AI interactions

Anti-Patterns to Avoid

Requiring users to explicitly prompt for obvious needsIgnoring historical context and behavioral patternsMaking users repeat information the system should know

Example

User is working on a presentation and needs to find a nearby doctor - instead of opening ChatGPT and typing a prompt, they press command+enter and AI automatically suggests local doctors based on their location and calendar availability

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