OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Emojiintelligence

Emojiintelligence is a Swift neural-network demonstration for Apple Playground on macOS. It converts a drawing into 64 binary pixel values and processes them through a fully connected network with a sigmoid activation to produce a binary output.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Emojiintelligence — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a learning reference when you want to connect basic image preprocessing with a small neural-network example in Swift.

Who it is for

  • Learners exploring neural networks and machine-learning concepts in Swift.
  • Developers interested in a small example of image-to-binary preprocessing for a neural network.

Documented uses

  • See how a drawn image can be resized and encoded as binary input.
  • Study a three-layer fully connected network and sigmoid activation in a compact example.
  • Explore a simple binary image-classification demonstration.
Before you choose

Requirements & limits

The repository is public and licensed under MIT. The project README says it currently works on macOS and reports an iPad bug.

  • The input representation depends on resizing the drawing to 8×8 pixels and testing for pink pixels.
  • The network produces a binary 0-or-1 output, rather than a general-purpose image description.
  • The documented working platform is macOS with Apple Playground; the README reports an iPad issue.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare an 8×8 drawn image as neural-network input

Convert a drawn image into the 64 binary values used as input to the demo’s neural network.

Before you start

  • A drawn image
  • Apple Playground on macOS

Steps

  1. Resize the drawn image to 8×8 pixels.

  2. Check each pixel for pink.

  3. Add 1 for each pink pixel and 0 for each other pixel.

  4. Use the resulting 64 binary values as input to the network.

Expected outcome

The image is represented as 64 binary inputs for the demo’s neural network.

Guide limitations

  • The project is reported to work on macOS; the README notes an iPad bug.
  • The README describes image encoding but does not provide detailed Playground setup or execution instructions.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
BilalReffas/EmojiIntelligence · Official homepage title · retained in its source language
How we review sourcesSuggest a correction