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	9382
title	E2E: Implement Dynamic RTT Measurement for VDOM Updates
state	Closed
labels	aiarchitectureperformance
assignees	tobiu
createdAt	Mar 7, 2026, 10:13 PM
updatedAt	Mar 7, 2026, 10:50 PM
githubUrl	https://github.com/neomjs/neo/issues/9382
author	tobiu
commentsCount	2
parentIssue	9380
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Mar 7, 2026, 10:50 PM

E2E: Implement Dynamic RTT Measurement for VDOM Updates

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

Closed v12.1.0 aiarchitectureperformance

tobiu commented on Mar 7, 2026, 10:13 PM

This ticket tracks the implementation of dynamic, real-time measurement of the VDOM pipeline's roundtrip time (RTT).

Goal: Instead of hardcoding a latency guess, we will use performance.now() to measure the exact time between a createViewData call and the moment the VDOM worker finishes applying the deltas.

Tasks:

Hook into Neo.manager.VDomUpdate to measure the completion of a specific ownerId's update cycle.
Maintain an Exponential Moving Average (EMA) of the last N roundtrips to smooth out frame spikes.
Expose this dynamic expectedRtt so that ScrollManager can accurately calculate how far a scrollbar will travel during the next update cycle based on current velocity.

This is a sub-task of the Epic to resolve Grid Scroll Thrashing.

tobiu added the ai label on Mar 7, 2026, 10:13 PM

tobiu added the architecture label on Mar 7, 2026, 10:13 PM

tobiu added the performance label on Mar 7, 2026, 10:13 PM

tobiu added parent issue #9380 on Mar 7, 2026, 10:13 PM

tobiu cross-referenced by #9384 on Mar 7, 2026, 10:30 PM

tobiu referenced in commit abc0c61 - "perf: Add VDOM update performance hooks (#9382) on Mar 7, 2026, 10:39 PM

tobiu assigned to @tobiu on Mar 7, 2026, 10:39 PM

tobiu Mar 7, 2026, 10:40 PM

Input from Gemini 3.1 Pro:

✦ I have implemented the RTT telemetry hooks.

Neo.mixin.VdomLifecycle now provides beforeExecuteVdomUpdate and afterExecuteVdomUpdate. These strictly measure the updateBatch worker call and the subsequent Main thread paint latency, intentionally excluding any App-worker queue wait time.

Neo.grid.Body implements these hooks to record the grid.scroll:<id> duration using the new Performance utility, but only when isScrolling is true to minimize noise.

The baseline metrics are now available for ScrollManager to query. We can close this ticket and move on to the predictive math in #9383.

tobiu referenced in commit b2e9ef3 - "perf: Add createViewData performance benchmark (#9382) on Mar 7, 2026, 10:49 PM

tobiu Mar 7, 2026, 10:49 PM

Input from Gemini 3.1 Pro:

✦ I have added the second benchmark inside GridBody.mjs -> createViewData().

This will track grid.createViewData:<id>, explicitly measuring the synchronous execution time ($T_{gen}$) required to map the records to the row pool and calculate the new VDOM structure.

With both the synchronous generation time and the asynchronous cross-worker roundtrip ($T_{rtt}$) now actively monitored by Neo.util.Performance, we have all the data required for the predictive scroll math.

I will close this sub-ticket, and we can proceed to #9383 (Predictive Delta Injection).

tobiu closed this issue on Mar 7, 2026, 10:50 PM