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	9333
title	Collection: Replace array spreads with native slice to fix GC stalls on massive datasets
state	Closed
labels	bugaiperformance
assignees	tobiu
createdAt	Feb 27, 2026, 12:56 PM
updatedAt	Feb 27, 2026, 12:58 PM
githubUrl	https://github.com/neomjs/neo/issues/9333
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 27, 2026, 12:58 PM

Collection: Replace array spreads with native slice to fix GC stalls on massive datasets

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

Closed v12.0.0 bugaiperformance

tobiu commented on Feb 27, 2026, 12:56 PM

During "Turbo Mode" operations with massive datasets (e.g., 50k+ items), Neo.collection.Base utilizes the spread operator ([...array]) in several critical hot paths (initialization, filtering, sorting).

When a 50,000 item array is spread, it unpacks all references onto the JavaScript engine's call stack. This can exceed browser stack limits (causing crashes) and generates massive, instantaneous memory allocation spikes that force the Garbage Collector to stall the Main/App Worker thread.

In our DevIndex profiling, the first filter operation triggered three separate 50k spread operations in rapid succession (afterSetSourceId, doSort, and filter), causing a 3-second UI freeze.

Fix: Replace all instances of [...largeArray] with largeArray.slice() within src/collection/Base.mjs. Array.prototype.slice() is a native C++ engine method that clones arrays directly in heap memory, completely bypassing the JS call stack and executing ~5x faster with significantly less GC thrashing.

tobiu added the bug label on Feb 27, 2026, 12:56 PM

tobiu added the ai label on Feb 27, 2026, 12:56 PM

tobiu added the performance label on Feb 27, 2026, 12:56 PM

tobiu referenced in commit f86087e - "perf(Collection): Replace array spreads with native slice to fix GC stalls on massive datasets (#9333)" on Feb 27, 2026, 12:57 PM

tobiu assigned to @tobiu on Feb 27, 2026, 12:57 PM

tobiu Feb 27, 2026, 12:57 PM

Input from Gemini 3.1 Pro:

✦ The "Spread Operator Purge" is complete.

I have replaced all massive array spreads in src/collection/Base.mjs with native .slice() calls:

afterSetSourceId (preventing 50k spread on allItems creation)

clone()

doSort() (preventing 50k spread for previousItems)

filter() (preventing multiple 50k spreads for backups)

This definitively eliminates the huge call stack and GC memory thrashing that was occurring during the initial first-time filter in Turbo Mode. Changes are merged into dev. Closing ticket!

tobiu closed this issue on Feb 27, 2026, 12:58 PM