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	9474
title	Grid: Optimize row pooling for small datasets
state	Closed
labels	enhancementaiperformancegrid
assignees	tobiu
createdAt	Mar 14, 2026, 1:07 PM
updatedAt	Mar 14, 2026, 1:08 PM
githubUrl	https://github.com/neomjs/neo/issues/9474
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Mar 14, 2026, 1:08 PM

Grid: Optimize row pooling for small datasets

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

Closed v12.1.0 enhancementaiperformancegrid

tobiu commented on Mar 14, 2026, 1:07 PM

The Grid's Body component previously calculated its row pool size strictly based on the visible viewport height plus the bufferRowRange (availableRows + 2 * bufferRowRange).

For small datasets (e.g., 9 records) displayed in a large viewport (e.g., capable of fitting 30+ rows), this caused the grid to unnecessarily instantiate and manage a large number of empty Neo.grid.Row components that would never receive data.

This optimization clamps the maximum pool size to the total number of records in the store (store.count), significantly reducing the initial VDOM generation and memory footprint for small grids while maintaining O(1) recycling performance for large datasets.

Changes:

Introduced a rowPoolSize class field in src/grid/Body.mjs.
Clamped the row pool generation to Math.min(windowSize, store.count).
Updated all internal ID generation (getRowId) to respect the dynamic pool size.
Migrated all deprecated store.getCount() calls to store.count within Body.mjs.

tobiu added the enhancement label on Mar 14, 2026, 1:07 PM

tobiu added the ai label on Mar 14, 2026, 1:07 PM

tobiu added the performance label on Mar 14, 2026, 1:07 PM

tobiu added the grid label on Mar 14, 2026, 1:07 PM

tobiu assigned to @tobiu on Mar 14, 2026, 1:08 PM

tobiu referenced in commit b0cca03 - "feat(grid): Optimize row pooling for small datasets (#9474)" on Mar 14, 2026, 1:08 PM

tobiu Mar 14, 2026, 1:08 PM

Input from Gemini 3.1 Pro:

✦ I have successfully implemented the grid row pooling optimization:

Added the rowPoolSize class field in src/grid/Body.mjs instead of hijacking internal properties.

Updated createRowPool() to compute the needed size via Math.min(windowSize, store.count).

Replaced all deprecated store.getCount() occurrences with store.count.

Ensured getRowId() relies on the dynamic rowPoolSize for rowIndex % poolSize modulus calculation.

This effectively minimizes the initial VDOM generation for smaller grids without impacting performance on massive ones.

tobiu closed this issue on Mar 14, 2026, 1:08 PM

tobiu cross-referenced by #9475 on Mar 14, 2026, 1:34 PM