Kids

Explore the real-time entropy processing modules ("kids") used in the EMS pipeline architecture. EMS Kids are written in C, orchestrated by Rust, and capable of transforming, compressing, tagging, and scoring entropy streams in milliseconds.

šŸ§  What Are EMS Kids?

EMS Kids are plug-and-play entropy processors that handle real-time quantum data inside WaterSlide pipelines. Each kid is optimized for throughput, low memory usage, and latency under 1ms. EMS uses Rust to manage execution, memory safety, and inter-process communication between Kids and the FastAPI backend.

Kids are custom C-based modules that perform specific tasks such as JSON parsing, entropy scoring, CURBy decoding, and advanced compression algorithms. They integrate seamlessly with the WaterSlide framework and can be deployed, tested, and monitored through automated deployment scripts.

šŸ¦¾ Super Kids

Super Kids are next-generation EMS modules enhanced with MCP (Model Context Protocol) and ACP (Agent Communication Protocol). They understand AI context, make smart decisions, and can communicate with other agents or APIs. These are the cognitive layer of entropy routing, ideal for QKD, synthetic data generation, and AI pipelines.

šŸš€ Compression Kid (Flagship Super Kid)

The flagship Compression Kid uses Microsoft LLMLingua to intelligently condense large AI prompts by up to 80%. This enables 3X more usage with ChatGPT, Claude, or local LLMs ā€” dramatically reducing API costs while preserving meaning.

80% Token Reduction
3X More ChatGPT Usage
<23ms Processing Time
GPT-4 Integrated

Features: Gentle/Balanced/Aggressive compression modes, keyword preservation, real token savings tracking, and seamless integration with major AI APIs. Works with text now, with upcoming support for image, code, and document compression.

Compression Kid Hero
āš” Experience Compression Kid

šŸ“¦ Available EMS Kids

Found 5 active kids (4 standard + 1 super kid). Last scanned:

compression_kid
Super Kid
Description: Context Optimization Algorithm using Microsoft LLMLingua for intelligent AI prompt compression. Reduces token usage by up to 80% while preserving semantic meaning.
Version: 2.0
Size: 2.3 MB
Framework: Python + FastAPI + LLMLingua
80%
Token Reduction
<23ms
Avg Latency
GPT-4
Integrated
1,847
Compressed
proc_standardized_quality
Active
Description: Applies fair entropy scoring using Shannon entropy, bias detection, chi-square analysis, and autocorrelation testing.
Version: 1.0
Size: 156 KiB
Framework: WaterSlide C Kid
>15K/sec
Throughput
<1ms
Latency
<1MB
Memory
5,895
Processed
proc_entropy_quality
Active
Description: Baseline quality scorer using entropy bias and bit balance analysis for rapid quality assessment.
Version: 1.0
Size: 156 KiB
Framework: WaterSlide C Kid
>10K/sec
Throughput
<1ms
Latency
<1MB
Memory
243
Processed
proc_json_entropy
Active
Description: Converts entropy into JSON-structured payloads for API transmission and database storage.
Version: 1.0
Size: 156 KiB
Framework: WaterSlide C Kid
>15K/sec
Throughput
<1ms
Latency
<1MB
Memory
5,732
Processed
proc_curby_parser
Active
Description: Parses raw entropy into NSA-style CURBy metadata tags for enhanced classification and routing.
Version: 1.0
Size: 27 KiB
Framework: WaterSlide C Kid
>20K/sec
Throughput
<1ms
Latency
<512KB
Memory
13,264
Processed

šŸ§Ŗ Live Kid Testing Interface

Test any available kid with custom entropy data. For the Compression Kid, use the dedicated interface above.

Ready for testing. Select a kid and enter entropy data above. šŸ’” Tips: ā€¢ Use hex format for raw entropy testing ā€¢ Try different test modes for detailed analysis ā€¢ Check the Compression Kid interface for AI prompt optimization ā€¢ All tests run against live EMS pipeline endpoints

šŸ”§ Integration & Deployment

EMS Kids are deployed using automated scripts and integrate seamlessly with the WaterSlide framework. Each kid can be independently deployed, tested, and monitored.

šŸš€ Deployment

ā€¢ Automated deployment scripts
ā€¢ WaterSlide framework integration
ā€¢ PostgreSQL database connectivity
ā€¢ Real-time monitoring and logging

šŸ“Š Monitoring

ā€¢ Performance metrics tracking
ā€¢ Real-time processing statistics
ā€¢ Memory and CPU usage monitoring
ā€¢ Automated health checks

šŸ”Œ API Integration

ā€¢ FastAPI backend connectivity
ā€¢ WebSocket streaming support
ā€¢ RESTful API endpoints
ā€¢ Multi-server architecture support

šŸ“š Developer Resources

Template System: Use our template system to create new kids with automatic build scripts and deployment integration.
Documentation: Complete development guides, API references, and integration examples.
Testing: Comprehensive test suites with performance benchmarks and memory leak detection.