# Pixel-art digit recognition network

> A pixel-art neural-network visualization places handwritten digits on the left, densely connected node layers in the center, and numbered class indicators with a predicted digit on the right. Frames show blue and magenta connection highlights.

- Kind: Video
- Page: https://meetreference.com/v/ref-pixel-neural-network-training/
- Ready-to-use agent handoff: https://meetreference.com/v/ref-pixel-neural-network-training/agent.txt
- Authorship: AI-made, per the source
- Made with: Not stated
- Model: Claude Opus 5.5
- Built with: not stated
- Style: Math & science; pixel art, neural network, dark background, neon accents, technical diagram, retro computing
- Length and format: 56 sec, 16:9 (1280x720 source)
- Playback: a muted visual reference. The original soundtrack is available only at the source.
- Shared by @DotCSV in X, Sep 23, 2026: https://x.com/DotCSV/status/2102737776219168939
- Prompt fidelity: Unavailable in this entry. The recreation brief is written by Reference.

## Before you start

Guidance from Reference, separate from the creator's instructions.

1. Read the reference and inspect the project, installed libraries, and available agent skills before writing code. Reuse suitable tools already present.
2. Prefer the creator-reported libraries and skills when they fit the task. Explain what useful additions help with, and install missing project dependencies or relevant skills from official sources when the environment and the user's existing permissions allow it. Respect the project's package manager and setup rules; do not ask again for routine setup already authorized.
3. Distinguish tools used by the creator from your own recommendations. A listed tool is not automatically required, and an empty tools or requirements list means the setup is unknown, not that nothing is needed.
4. Ask concise questions when missing inputs or choices materially affect the result: paid services, credentials, the user's assets, or a different production approach. Use existing answers and permissions; continue independent work while waiting.
5. If a useful library, skill, or service cannot be installed or used, explain an available alternative and its expected effect on quality, features, or effort. Get the user's choice before a material compromise; otherwise state the fallback and continue. Never silently simplify the result or claim to have used a tool you did not use.

## Recreating this video

This is a visual reference. No original creator prompt is available. The following brief was written by Reference after inspecting the work.

Build a fixed left-to-right diagram on a coarse pixel grid, keeping inactive nodes and connections dark while reserving saturated color for selected paths and results. Pair an image-like input panel with compact class bars and a larger output glyph to connect the abstract network to a readable outcome. For a new subject, replace the digits and class labels with relevant inputs and outcomes, and reduce connection density or raise scaffold contrast where explanatory clarity matters.

1. Open the live work or source and inspect the frame images below.
2. Adapt its layout, hierarchy, palette, and interaction patterns to the user's own content and brand.
3. Compare the result against the reference. Use your own assets and credit the original creator.

## What it looks like

Written by Reference from the video frames, not by the creator.

Dark, retro pixel-art visualization of a neural network processing handwritten digits, with nodes, connecting lines, and small output indicators.

- Color: Black backgrounds with deep navy and electric-blue network details, white digit sprites, and occasional cyan and magenta highlights.
- Type: Pixel lettering and segmented numeric output labels.
- Framing: Repeated panels place a digit sample on the left, a multilayer network across the center, and numbered output labels on the right.
- Structure: Handwritten digit samples vary alongside highlighted network paths and class indicators, while the left-to-right input, network and output layout stays consistent.

Storyboard, nine frames spread across the video: https://meetreference.com/media/ref-pixel-neural-network-training/storyboard.webp

- Frame 1 at 3.1s: https://meetreference.com/media/ref-pixel-neural-network-training/f1.webp
- Frame 2 at 9.3s: https://meetreference.com/media/ref-pixel-neural-network-training/f2.webp
- Frame 3 at 15.6s: https://meetreference.com/media/ref-pixel-neural-network-training/f3.webp
- Frame 4 at 21.8s: https://meetreference.com/media/ref-pixel-neural-network-training/f4.webp
- Frame 5 at 28.0s: https://meetreference.com/media/ref-pixel-neural-network-training/f5.webp
- Frame 6 at 34.2s: https://meetreference.com/media/ref-pixel-neural-network-training/f6.webp
- Frame 7 at 40.4s: https://meetreference.com/media/ref-pixel-neural-network-training/f7.webp
- Frame 8 at 46.7s: https://meetreference.com/media/ref-pixel-neural-network-training/f8.webp
- Frame 9 at 52.9s: https://meetreference.com/media/ref-pixel-neural-network-training/f9.webp

## Links

- Watch: https://x.com/DotCSV/status/2102737776219168939
- Original post: https://x.com/DotCSV/status/2102737776219168939