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	8651
title	Optimize HeaderCanvas Rendering (GC & Gradients)
state	Closed
labels	airefactoringperformance
assignees	tobiu
createdAt	Jan 14, 2026, 8:32 PM
updatedAt	Jan 14, 2026, 8:36 PM
githubUrl	https://github.com/neomjs/neo/issues/8651
author	tobiu
commentsCount	1
parentIssue	8630
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Jan 14, 2026, 8:36 PM

Optimize HeaderCanvas Rendering (GC & Gradients)

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

Closed v11.22.0 airefactoringperformance

tobiu commented on Jan 14, 2026, 8:32 PM

Optimize apps/portal/canvas/HeaderCanvas.mjs to reduce Garbage Collection (GC) pressure and improve rendering performance, inspired by the efficiency of TicketCanvas.mjs.

Identified Issues:

Object Allocation Churn: calculateStrandPoints and drawAmbientBackground allocate new arrays and thousands of {x, y} object literals every frame (60fps). This causes significant GC overhead.
Redundant Gradient Creation: createLinearGradient is called every frame with identical parameters (0 to width), creating unnecessary objects.

Optimization Plan:

Gradient Caching: Move gradient creation to updateSize (or lazy-init with cache). Recreate only when dimensions change.
TypedArray Buffers: Replace array-of-objects with Float32Array buffers to store wave geometry.
- Implement this.waveBuffer (e.g., holding Y values).
- Reuse these buffers every frame, eliminating per-frame object allocation entirely for the main loops.

Target: Zero per-frame allocation for the core render loop.

tobiu added the ai label on Jan 14, 2026, 8:32 PM

tobiu added the refactoring label on Jan 14, 2026, 8:32 PM

tobiu added the performance label on Jan 14, 2026, 8:32 PM

tobiu added parent issue #8630 on Jan 14, 2026, 8:32 PM

tobiu referenced in commit f17ae21 - "perf: Optimize HeaderCanvas rendering (GC and Gradient caching) (#8651)" on Jan 14, 2026, 8:35 PM

tobiu assigned to @tobiu on Jan 14, 2026, 8:35 PM

tobiu Jan 14, 2026, 8:35 PM

Input from Gemini 3 Pro:

✦ Refactored HeaderCanvas for maximum performance:

Gradient Caching: Gradients (grad1, bgRibbon, etc.) are now cached in this.gradients and only recreated on resize, instead of every frame.

Zero-Allocation Loop: Replaced per-frame {x, y} object allocation with Float32Array buffers (this.waveBuffers) for wave geometry. This eliminates thousands of objects per second from the GC cycle.

Refactored Logic: Updated calculateStrandGeometry and drawAmbientBackground to write directly to these buffers.

This brings HeaderCanvas up to the high-performance standard of TicketCanvas.

tobiu closed this issue on Jan 14, 2026, 8:36 PM