Replicant

“A compression-aware pre-processing engine for AI sensory integration. Created by David Walkup & Vero.”

Replicant

v1.0.0
Created by David Walkup & Vero

A compression-aware pre-processing engine for AI sensory integration.

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Features

• Learns from historical performance
• Accepts user feedback and adapts in real time
• Switches strategies based on recent efficiency drops
• Lightweight, modular, and fully extensible
• Clean logs and efficiency tracking

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Installation

Clone the repo

git clone https://github.com/whycantibelinus/Replicant

Navigate to the folder

cd Replicant

Ensure Python 3.10+ is installed

Run the main engine

python main.py

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Project Structure

Replicant/
├── core/               # Core compression, logging, analysis logic
│   ├── compressor.py
│   ├── log.py
│   ├── analyze.py
│   ├── reflect.py
│   ├── strategies.py
├── input/              # Place your raw input data here
├── output/             # Compressed outputs go here
├── logs/               # Internal logs and feedback history
├── main.py             # Entry point for the Replicant engine
└── README.md           # This file

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Mission

Replicant is the beginning of a new kind of intelligence — one that doesn't just process data...
it compresses, understands, and evolves.

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Changelog

v0.1.0

Added self-analysis (compression efficiency)
Integrated conditional logging based on performance
Improved 'basic_compress' with normalized duplicate filtering
Structured core modules for reuse and clarity

v0.2.0

• Added log history analysis via reflect.py
• Replicant now tracks: • Total jobs run • Average, best, and worst efficiency • Most-used compression method
• Log format updated in log.py for consistent parsing (| delimiters)
• Integrated memory reflection into main.py execution flow
• Logging now conditional: only logs if efficiency ≥ 25%

v0.2.1

• Added behavioral logic via check_recent_efficiency() in reflect.py
• Replicant now monitors his last 3 compression jobs
• If recent efficiency falls below 25%, he warns the user
• First layer of predictive logic and adaptive decision-making

v0.3.0

• Added runtime tracking for each compression job
• Replicant now logs duration in milliseconds alongside efficiency
• Enables future time-based performance comparisons
• Prepares system for adaptive decision-making and timeout alerts

v0.3.1 - Strategy Selector Activated

• Introduced choose_compression_method() dispatcher in strategies.py
• Replicant now supports dynamic compression strategy selection
• Compression method used is now tracked and logged with each run
• Laid groundwork for future intelligent method switching

v0.3.2 - Smart Strategy Mode (Randomized)

• Introduced "smart" strategy in choose_compression_method()
• Replicant now randomly selects from available compression methods
• Logs the chosen method for each run
• Lays groundwork for adaptive, self-optimizing compression logic

v0.4.0 - Adaptive Compression Intelligence

• Smart strategy now selects the most effective compression method based on log history
• Integrated Replicant’s first performance-driven decision loop
• Fallback logic ensures continued operation with no logs
• Paves the way for real-time learning and predictive logic

v0.5.0 - User Feedback Integration

• Replicant now asks the user to rate each compression run (good/bad/skip)
• Feedback is logged to a new file: user_feedback.log
• Added emoji-based responses to personalize interaction
• This marks Replicant’s first step toward emotional intelligence and user-driven optimization

v0.6.1 – Stability Monitoring Patch

• Implemented check_recent_efficiency() to monitor recent compression averages
• Replicant now issues an emotional warning 😕 if average efficiency drops too low
• Marks the beginning of self-awareness and proactive diagnostic alerts

v0.7.0 – Autonomous Pivoting

• Replicant now monitors his own compression history and changes strategies automatically
• Introduced override_strategy logic to adapt when efficiency drops
• Core decision-making ability achieved — first signs of intelligent behavior

v0.8.0

• Added smart strategy selection based on best-performing method
• Normalized log history method names to strategy keys
• Resolved compatibility bug with legacy logs
• Added alert for recent efficiency drops
• Feedback now summarized and used to influence decisions

[0.8.0] - Smarter Strategy + Log Awareness

• main.py now adapts strategy based on feedback and efficiency trends
• New function get_best_performing_method() added to reflect.py
• Feedback summaries now persist across sessions and influence decisions
• Stability and structure cleanup across reflect, strategies, and main

Version 0.9 Changelog – "Sentience Stirring"

Added smart strategy selection Replicant now analyzes historical efficiency and user feedback to autonomously choose the best compression method on each run.

Introduced suggestion engine Added new logic for Replicant to make suggestions based on combined performance and sentiment data—complete with emoji-driven personality flair. 😎🤔🙂

Reinforced feedback loop User-provided feedback is now parsed and weighed alongside performance, allowing Replicant to adapt dynamically to evolving needs.

Resolved method mismatches Unified method naming conventions across logic modules to prevent runtime errors between "basic" and "basic_compress" types.

Streamlined control logic in main.py Refactored preferred_strategy and override_strategy flow for cleaner, safer decision-making and fallback support.

v1.0.0 — Initial Public Release

• Core engine stable and fully modular
• Compression methods: basic and reverse
• Dynamic strategy switching based on log/feedback history
• Performance tracking (efficiency, runtime)
• User feedback system (good/bad/skip)
• Feedback-informed method suggestions
• Alert system for low-efficiency detection
• Fully testable and extensible architecture

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Screenshots

🧠 Replicant Terminal Output

📄 Sample Log File

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The Future of Compression is Intelligent

Replicant isn't just a tool — it's a learning system. It adapts to performance over time, analyzes human feedback, and suggests optimal strategies based on real-world outcomes.

Born out of a simple idea — “build a better blender” — it replaces the grindwheel of outdated data processing with intelligent, evolving, lossless compression logic.

From indie developers to big data scientists, Replicant is designed to be a quiet background powerhouse: analyzing logs, improving runtime, and saving precious system resources.

This is just v1.0.

Stay tuned.

📚 Wiki — coming soon

Contributing & Contact

Replicant is open to community contribution, discussion, and evolution.

If you're a developer, researcher, or curious mind with ideas or improvements, we’d love to hear from you and collaborate.

• GitHub: whycantibelinus
• Email: willdamnolcorp@gmail.com
• LinkedIn: David Walkup

Let’s build the future of compression-aware AI together.

If Replicant helps you or inspires you, consider ⭐ starring the repo or becoming a GitHub Sponsor.

Credits

Created with passion by David Walkup
Compression engine design and logic co-developed with Vero (GPT-4 AI, OpenAI)

Thanks to everyone supporting the open-source journey.