Continue Introduces 'Continuous AI' Concept… Building Custom AI Assistants that Integrate with Codebases and Tools

General-purpose AI like ChatGPT doesn't know a user's codebase or team conventions, creating inefficiency as users must manually input information. To solve this, Continue has introduced the concept of Continuous AI, which operates within the user's environment and tools.

Through the cn CLI and Cookbooks, developers can configure and operate AI assistants directly in the terminal. This approach performs actual deployment, security checks, and user behavior analysis without switching browser tabs or copy-pasting.

Key Implementation Examples

• Product-Aware Development: Integrates with PostHog and GitHub to connect actual user engagement data with code changes after a feature launch. The cause of an engagement drop for a specific feature can be identified immediately at the code level.
• Security-First Development: Integrates with Snyk to automatically identify vulnerable dependencies and affected repositories when a new CVE is disclosed. Rules can be set to enforce security checks before dependency updates.
• Continuous Deployment: Integrates with Netlify to handle build triggers, progress monitoring, and even checking live status via terminal commands. No access to a browser dashboard is required.

Technical Features and Value

By adopting the Model Context Protocol (MCP) as a standard, dependency on proprietary APIs is eliminated, allowing connection to various internal tools such as Sentry and Linear. Through Custom instructions and Enforced rules, team conventions such as TDD compliance or running tests before production deployment are reflected in AI behavior.

Individual developers can reduce context switching, and teams can shorten onboarding time for new hires through shared assistants with built-in security policies and workflows.