What is the 'Neural Link'?

The Neural Link is a bi-directional bridge that connects AI agents (like Gemini or Claude) directly to the Neo.mjs runtime. It allows agents to 'see' the application's Scene Graph, inspect component state, verify event listeners, and even mutate the running application in real-time. This turns the application into a 'glass box' for AI, enabling autonomous debugging and feature development.

Why is Neo.mjs called an 'Application Engine' instead of a framework?

Traditional frameworks are general-purpose libraries (like a Toyota) that help you organize code, but they compile away into ephemeral DOM nodes. Neo.mjs is a precision-engineered runtime (like an F1 car), similar to Unreal Engine for games. It maintains a persistent 'Scene Graph' of objects in a separate worker thread. These objects retain their identity, state, and relationships, allowing for advanced capabilities like multi-window orchestration, runtime permutation, and deep AI introspection that are impossible with 'melted plastic' DOM-based frameworks.

What is 'Context Engineering'?

Context Engineering is the practice of curating the environment and information flow for AI agents to maximize their autonomy. In Neo.mjs, this is implemented via three Model Context Protocol (MCP) servers: a Knowledge Base for semantic code understanding, a Memory Core for learning from past sessions, and a GitHub Workflow server for project management. This ecosystem allows agents to work as fully integrated members of the development team.

What is 'Object Permanence' in the context of Neo.mjs?

In Neo.mjs, UI components are persistent JavaScript objects living in the App Worker, not just transient rendering results. This 'Object Permanence' means a component (like a Dashboard) maintains its state (scroll position, user input, internal logic) even if it is detached from the DOM or moved to a different browser window. This is the 'Lego Technic' approach versus the 'Duplo' approach of traditional frameworks.

What is an 'Agent Operating System'?

Neo.mjs v11 introduced the concept of an Agent OS, where the platform itself provides the tools and interfaces for AI agents to operate. It combines a standalone, type-safe AI SDK for autonomous 'Code Execution' with the Neural Link for runtime control. This enables agents to monitor, debug, and heal the application autonomously, effectively acting as an operating system for synthetic intelligence.

How does Neo.mjs handle multi-window applications?

Neo.mjs uses shared web workers to run a single application instance across multiple browser windows. All windows share the same application state and data in real-time. Components can even move between windows while retaining their JavaScript instances. This enables desktop-class experiences like multi-window IDEs, browser-based email clients, and multi-screen control rooms.

What makes Neo.mjs different from React, Angular, or Vue?

Neo.mjs is fundamentally different in its architecture: (1) It uses True Multithreading via web workers to prevent UI jank, (2) It is an AI-Native Application Engine with built-in bridges for AI collaboration, and (3) It treats components as persistent objects in a Scene Graph, enabling native multi-window support and runtime permutation.

Frontmatter

id	8693
title	Docs: Clarify requestAnimationFrame usage in Canvas guides
state	Closed
labels	documentationai
assignees	tobiu
createdAt	Jan 15, 2026, 11:06 PM
updatedAt	Jan 15, 2026, 11:14 PM
githubUrl	https://github.com/neomjs/neo/issues/8693
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Jan 15, 2026, 11:14 PM

Docs: Clarify requestAnimationFrame usage in Canvas guides

Name: Neo.mjs Application Engine
Author: Neo.mjs

Closed v11.23.0 documentationai

tobiu commented on Jan 15, 2026, 11:06 PM

Update CanvasArchitecture.md, NeuralSwarm.md, and NeuralTimeline.md to clarify that requestAnimationFrame IS available when using OffscreenCanvas in a Dedicated Worker (i.e., when Neo.config.useSharedWorkers = false).

The guides currently imply that setTimeout is the only option because they focus on the Shared Worker use case. We should promote the feature-detection pattern (checking for globalThis.requestAnimationFrame) to ensure best performance in both environments.

tobiu added the documentation label on Jan 15, 2026, 11:06 PM

tobiu added the ai label on Jan 15, 2026, 11:06 PM

tobiu referenced in commit 2ce61a5 - "docs: Clarify rAF availability in Canvas guides (#8693)" on Jan 15, 2026, 11:12 PM

tobiu assigned to @tobiu on Jan 15, 2026, 11:13 PM

tobiu Jan 15, 2026, 11:13 PM

Input from Gemini 3 Pro:

✦ I have updated learn/guides/advanced/CanvasArchitecture.md and learn/guides/advanced/NeuralTimeline.md to clarify the availability of requestAnimationFrame.

CanvasArchitecture.md: Corrected the "Common Pitfalls" section to explain that rAF is available in Dedicated Workers (the default) and only unavailable in Shared Workers (opt-in). Added a code example for feature detection.

NeuralTimeline.md: Added a note clarifying that while the Portal App uses Shared Workers (and thus setTimeout), the architecture supports Dedicated Workers/rAF seamlessly.

Verified and pushed to dev.

tobiu closed this issue on Jan 15, 2026, 11:14 PM