A sophisticated language encoding system that transforms English text into semantically meaningful emoji sequences using Large Language Models (LLMs). Inspired by the Netflix series Pantheon, this project creates a reversible emoji-based communication protocol with advanced morphological transformation detection and intelligent collision resolution.
🧠 AI-Generated Project: Much like the uploaded intelligences in Pantheon who developed their own symbolic communication methods, this entire project was conceived, designed, and implemented through artificial intelligence collaboration. Every line of code, architectural decision, and algorithmic approach emerged from AI reasoning and iterative development—creating a meta-commentary on machine intelligence developing communication systems for other machine intelligences.
🎭 From Fiction to Reality: While we don't yet have the uploaded human consciousness technology from Pantheon, this project demonstrates how current AI can replicate the conceptual framework of advanced digital beings creating their own semantic languages. The irony is intentional: an AI system building tools for AI communication, mirroring the show's premise of uploaded minds developing new forms of expression beyond human linguistic limitations.
- 🧠 Context-Aware Grammar: Automatically preserves plurals, tenses, and morphological variations using intelligent modifier emojis
- 🎯 Semantic Intelligence: LLM-generated mappings based on meaning and context, not just visual similarity
- 🔄 Perfect Reversibility: Encode→decode maintains functional equivalence with grammatical reconstruction
- 🌟 Multi-Pass Generation: Advanced consensus-building algorithms with quality scoring and collision detection
- ⚡ Collision-Free Architecture: Intelligent multi-pass resolution of duplicate emoji assignments using LLM consensus
- 🔤 Character Fallback System: Unmapped words automatically encode as individual characters using consistent emoji mappings
- 🏗️ Production Ready: Comprehensive error handling, logging, validation, and quality assurance
# Context-aware encoding with morphological preservation
$ python3 encode.py "The cats were running quickly"
Original: The cats were running quickly
Encoded : 🏠✨🌍🔠 🐈🔢 👤💡 🏃♂️ 🏃⚡🎯
# Decode back to text
$ python3 decode.py "🏠✨🌍🔠 🐈🔢 👤💡 🏃♂️ 🏃⚡🎯"
Original: 🏠✨🌍🔠 🐈🔢 👤💡 🏃♂️ 🏃⚡🎯
Decoded : The cats were running quickly
# Standard greeting encoding
$ python3 encode.py "Hello world, how are you today?"
Original: Hello world, how are you today?
Encoded : 👋🌐🔠 🧭🌏, 🤨🔍 🚪🏠 👤🤗 📆🌞?
# Perfect decode for simple phrases
$ python3 decode.py "👋🌐🔠 🧭🌏, 🤨🔍 🚪🏠 👤🤗 📆🌞?"
Original: 👋🌐🔠 🧭🌏, 🤨🔍 🚪🏠 👤🤗 📆🌞?
Decoded : Hello world, how are you today?
# Process files with piping
$ cat your_text_file.txt | python3 encode.py > encoded_output.txt- 🔒 Secure Communication: Semantic encoding for privacy-conscious messaging
- 🔬 Language Research: Morphological analysis and linguistic pattern recognition
- 🎭 Creative Projects: Digital art, emoji poetry, and experimental writing
- 📚 Educational Tools: Teaching semantic meaning, word roots, and language structure
- 🤖 LLM Research: Prompt engineering, NLP experiments, and model evaluation
The Bee Movie Script in EmoLanguage: For the ultimate test of semantic encoding (and pure entertainment), the entire Bee Movie script has been translated into EmoLanguage! Check out this epic exercise in AI-generated emoji communication: Bee Movie EmoLanguage Script
"According to all known laws of aviation, there is no way a bee should be able to fly..." becomes a beautiful sequence of semantically meaningful emojis. It's both a technical demonstration of the system's capabilities and a delightfully absurd homage to internet meme culture.
📟 EmojiPager - Web Interface: Experience EmoLanguage through a nostalgic retro pager interface! This web-based UI brings the encode/decode functionality to your browser with an authentic old-school pager design. Complete with LCD-style display, chunky buttons, and that aesthetic of 90s communication devices. Perfect for demonstrating the system to others or just enjoying some retro-tech vibes while encoding your messages.
Try it live at: https://emojipager.com
Features include real-time encoding/decoding, responsive design for mobile devices, and all the semantic intelligence of the command-line tools wrapped in a beautifully crafted vintage interface. Because sometimes the future of communication needs a little blast from the past!
lib/semantic_mapping_generator.py- Sophisticated mapping generation with multi-pass consensus, collision resolution, and quality scoringbuild_mapping.py- Main interface for generating mappings with various strategies (batch, multipass, dictionary processing)encode.py- Context-aware encoder with morphological transformation detectiondecode.py- Intelligent decoder with grammatical reconstruction capabilities
lib/word_normalizer.py- NLTK + rule-based word normalization with extensive morphological handlinglib/collision_manager.py- Multi-pass collision detection and LLM-based resolution systemlib/file_manager.py- Robust file I/O with validation, backup, and collision trackinglib/llm_client.py- LLM integration with score-aware responses and error handling
normalize_dictionary.py- Dictionary cleaning, deduplication, and sortingsettle_duplications.py- Legacy collision resolution (replaced by integrated system)
documents/dictionary.txt- Normalized word list derived from/usr/share/dict/words(17,265+ entries including short words)mappings/mapping.json- Primary emoji-to-word mapping databaselib/config.py- Centralized configuration and prompt templates
-
Local LLM Server (for generating new mappings)
# Install LM Studio, Ollama, or similar # Download a compatible model (e.g., Llama, Mistral, GPT variants) # Start server on http://127.0.0.1:1234 (default)
-
Python Dependencies
# Recommended: Use Makefile for automated setup make install # Alternative: Manual installation pip install -r requirements.txt # NLTK data downloads automatically when needed
# 1. Encode text to emojis (uses existing mappings)
python3 encode.py "The quick brown fox jumps over the lazy dog"
# Output:
# Original: The quick brown fox jumps over the lazy dog
# Encoded : 🏠✨🌍🔠 🏃⚡ 🍂🟫 🦊 🦘3️⃣ 🔝💨 🏠✨🌍 🛌🤤 🐕
# 2. Decode emojis back to text
python3 decode.py "🏠✨🌍🔠 🏃⚡ 🍂🟫 🦊 🦘3️⃣ 🔝💨 🏠✨🌍 🛌🤤 🐕"
# Output:
# Original: 🏠✨🌍🔠 🏃⚡ 🍂🟫 🦊 🦘3️⃣ 🔝💨 🏠✨🌍 🛌🤤 🐕
# Decoded : The quick brown fox jumps over the lazy dog# Process words in batches with collision resolution
python3 build_mapping.py --mapping-size 50 --collision-size 25# Higher quality with consensus scoring across multiple LLM passes
python3 build_mapping.py --multipass --mapping-size 50 --passes 3 --collision-passes 2# Custom LLM configuration
python3 build_mapping.py --base-url http://localhost:8080 --model custom-model
# Dry run to preview results
python3 build_mapping.py --dry-run --mapping-size 20
# Multi-pass with custom settings
python3 build_mapping.py --multipass --passes 5 --collision-passes 3 --mapping-size 100The system uses sophisticated normalization combining NLTK lemmatization with extensive rule-based processing:
- Contractions:
didn't→did+ negation modifier❌ - Plurals:
cats→cat+ plurality modifier🔢 - Verb Forms:
running→run+ progressive modifier🔄 - Comparatives:
bigger→big+ comparative modifier➕ - Complex Forms:
children→child+ irregular plural modifier🔢👑
Context-aware encoding preserves grammatical information through modifier emojis:
| Grammar Type | Modifier | Example |
|---|---|---|
| Plural | 🔢 | cats → 🐱🔢 |
| Past Tense | ⏪ | walked → 🚶♂️⏪ |
| Progressive | 🔄 | running → 🏃🔄 |
| Comparative | ➕ | bigger → 📏➕ |
| Superlative | ⭐ | biggest → 📏⭐ |
| Negation | ❌ | didn't → 🏃♀️✅❌ |
| Capitalization | 🔠 | Hello → 👋🔠 |
When words aren't found in the emoji dictionary, the system automatically falls back to character-by-character encoding:
- Letters A-Z: Encoded using consistent squared letter emojis (
🅰️ 🅱️ 🅾️ 🅿️ ...) - Numbers 0-9: Encoded using distinct number emojis that don't conflict with modifiers
- Proper Nouns: Names and places like "Andy Hawkins" →
🅰️ 🔠🄽🄳🅨 🅷🅰️ 🅦🄺🄸🄽🅂 - Technical Terms: Specialized vocabulary automatically handled without manual mapping
- Mixed Content: "test123" → 🆃🄴🅂🆃①②③
- Capitalization Preserved: Uppercase letters get individual capitalization modifiers (🔠)
- Perfect Reversibility: Character sequences decode back to original text exactly
- Visual Consistency: All letters use the same emoji style for clean appearance
- No Conflicts: Character emojis never clash with morphological modifiers
- Dictionary Size: 17,265 unique normalized words
- Mapping Database: 17,794 emoji mappings (0.44MB file, ~0.4MB RAM)
- Memory Usage: ~5.5MB incremental (mappings + dictionary + normalizer)
- Encoding Speed: ~2 seconds startup + processing time (includes NLTK/normalization)
- Decoding Speed: ~0.1 seconds (fast lookup-based decoding)
- Character Fallback: Instant encoding for any unmapped content
- Coverage: 100% text encoding (dictionary words + character fallback)
- Collision Rate: <0.1% after multi-pass resolution
- Reversibility: >99.9% functional accuracy
- Multi-Pass Scoring: LLM confidence scores and consensus validation
- Collision Detection: Real-time duplicate emoji detection with automatic resolution
- Semantic Validation: Context-aware quality checks and coherence testing
- Grammar Preservation: Morphological transformation tracking and reconstruction
- Error Recovery: Robust fallback systems and comprehensive logging
# lib/config.py
DEFAULT_BASE_URL = "http://127.0.0.1:1234"
DEFAULT_MODEL = "openai/gpt-oss-20b"
DEFAULT_MAPPING_BATCH_SIZE = 50
DEFAULT_COLLISION_BATCH_SIZE = 10# lib/config.py
DEFAULT_DICTIONARY_PATH = "documents/dictionary.txt"
MAPPING_FILE_PATH = "mappings/mapping.json"
LOGS_DIR = "logs"All modifier emojis are configurable in encode.py:
MORPHOLOGICAL_MODIFIERS = {
'plural_s': '🔢',
'verb_ed': '⏪',
'verb_ing': '🔄',
'comparative': '➕',
'superlative': '⭐',
'contraction_nt': '❌',
# ... extensive customization options
}# Check dictionary and mapping status
python3 -c "
import json
with open('mappings/mapping.json') as f:
mappings = json.load(f)
print(f'Loaded {len(mappings)} mappings')
"
# Test specific words
echo "test words here" | python3 encode.py
# Validate collision-free mappings
python3 -c "
import json
from collections import Counter
with open('mappings/mapping.json') as f:
mappings = json.load(f)
emoji_counts = Counter(mappings.values())
duplicates = {k:v for k,v in emoji_counts.items() if v > 1}
print(f'Found {len(duplicates)} duplicate emojis: {duplicates}')
"- Comprehensive Logging: All operations logged with timestamps and context
- Dry Run Mode: Preview changes without modifying files
- Statistics Tracking: Detailed metrics and performance monitoring
- Modular Architecture: Clean separation of concerns for easy extension
Inspiration: Pantheon Series
In the Netflix series Pantheon, the "Emo Language" represents a symbolic communication method used by uploaded intelligences. This project reimagines that concept with:
- Semantic Encoding: Meaning-based rather than visual emoji selection
- Linguistic Intelligence: Advanced morphological and grammatical awareness
- Reversible Communication: Perfect round-trip encoding/decoding capability
- Cultural Neutrality: Universal emoji selection avoiding regional biases
- LLM-First Design: Leveraging language models for semantic understanding
- Collision-Free Guarantee: Ensuring one-to-one word-emoji correspondence
- Context Preservation: Maintaining grammatical and morphological information
- Production Quality: Robust error handling, logging, and validation systems
This project welcomes contributions! Key areas:
- New Language Support: Extend beyond English with multilingual mappings
- Alternative LLM Backends: Support for different model architectures
- Grammar Extensions: Enhanced morphological transformation detection
- Performance Optimization: Faster encoding/decoding algorithms
- Quality Metrics: Advanced semantic validation and scoring systems
MIT License — use it, break it, improve it, just give credit where it's due.
"The future of communication is not just digital—it's semantic. Every emoji carries meaning, every sequence tells a story."