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	9209
title	Benchmark Grid Scroll-to-Update Latency
state	Open
labels	aitestingperformancegrid
assignees	tobiu
createdAt	Feb 19, 2026, 11:58 AM
updatedAt	Feb 19, 2026, 12:27 PM
githubUrl	https://github.com/neomjs/neo/issues/9209
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]

Benchmark Grid Scroll-to-Update Latency

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

Openaitestingperformancegrid

tobiu commented on Feb 19, 2026, 11:58 AM

Context: We have established that the Grid can render frames at >60 FPS. The next frontier in performance is Latency. In a buffered grid, the critical metric is not just FPS, but the time between a scroll event and the corresponding cell content update.

Objective: Create a deep telemetry benchmark that measures the "Input-to-Pixel" latency of the Grid.

Metrics to Capture:

Scroll Latency: Time from scroll event to Neo.grid.Body#onScroll execution.
Update Latency: Time from onScroll to createViewData completion.
Render Latency: Time from createViewData (VDOM diff) to actual DOM update (MutationObserver trigger on specific cell).
Worker Roundtrip: Measure the App Worker -> VDom Worker -> Main Thread cycle time during a scroll.

Implementation:

Instrument Neo.grid.Body and Neo.grid.View with performance marks.
Use performance.measure to capture the deltas.
Expose these metrics to the Playwright harness via a global Neo.telemetry object.

tobiu added the ai label on Feb 19, 2026, 11:58 AM

tobiu added the testing label on Feb 19, 2026, 11:58 AM

tobiu added the performance label on Feb 19, 2026, 11:58 AM

tobiu added the grid label on Feb 19, 2026, 11:58 AM

tobiu Feb 19, 2026, 12:26 PM

Input from Gemini 3 Pro:

✦ ### Strategic Expansion: A/B Testing Architecture (Pooling vs. RemoveDom)

This benchmark infrastructure (#9209) is the perfect vehicle to scientifically resolve the architectural debate between DOM Pooling (Current) and RemoveDom (Layout Thrashing).

The Hypothesis:

Pooling: High DOM weight (~2000 nodes), zero insertion/removal costs. Optimizes for CPU/Main Thread stability by trading Memory.

RemoveDom: Low DOM weight (<500 nodes), high frequency appendChild/removeChild. Optimizes for Memory by trading CPU (Layout/Recalculate Style & V8 Binding).

The Nuance: With modern contain: strict and column/row buffering, the "Layout Thrashing" cost of removeDom is significantly mitigated compared to legacy implementations. On high-end CPUs (Desktop), removeDom might actually outperform Pooling if Memory Bandwidth (traversing the larger DOM tree) is the bottleneck.

Future Test Cases: We should extend this benchmark to support a toggle (e.g., Neo.config.grid.useDomPooling) to A/B test these strategies on identical hardware. This will allow us to auto-tune the grid strategy based on the detected device capabilities (e.g., use Pooling on Mobile to save battery, use RemoveDom on Desktop to save RAM).

tobiu assigned to @tobiu on Feb 19, 2026, 12:27 PM