Releases: bearycool11/pmll_blockchain
December 24 2024
PMLL Blockchain v3.0.0 Release Notes
This ain't just an update, folks, it's a revolution. PMLL Blockchain v3.0.0 is here, and it's packing some serious heat in the fight against bots and online BS. We've turbocharged the core, deepened the blockchain integration, and built a whole arsenal of new tools to smoke out those digital cockroaches.
Brought to you by the dream team: Josef Edwards, Elon Musk, Fei-Fei Li, Andrew Ng, and Obi Oberdier.
Key Features and Improvements
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Enhanced PMLL Algorithm: We've ripped out the old engine and dropped in a fire-breathing dragon. This ain't your grandma's PMLL. With Andrew Ng and Fei-Fei Li's logic loops under the hood, we're hitting ludicrous speed and accuracy in bot detection. Think Neo dodging bullets, but for catching fake accounts.
- Increased Accuracy: Bots think they're slick, but we're seeing right through their cheap disguises. False positives? Ancient history.
- Improved Performance: This thing's faster than a cheetah on Red Bull. Massive datasets? Real-time analysis? Bring it on.
- Adaptability: Bots evolve, we evolve faster. This ain't a static system, it's an AI-powered predator, constantly learning and adapting to hunt down those digital vermin.
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Expanded Blockchain Integration: We're not just playing with blockchain anymore, we're building a damn fortress on it.
- Decentralized Bot Registry: Think of it as a digital Most Wanted list, but permanent and inescapable. Every bot we catch gets its mugshot on the blockchain for eternity.
- Tokenized Reputation System: Good guys get rewarded, bad guys get wrecked. Earn tokens for busting bots, build your rep, and become a legend in the anti-BS brigade.
- Secure Communication Channels: We're talking encrypted, private, Fort Knox-level comms. The bots can try to listen in, but they'll just get a face full of static.
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New Modules and Tools:
- Honeypot Network: We're laying traps, setting bait, and luring those bots into a digital swamp. They come for the fake accounts, they stay for the in-depth analysis.
- Automated Reporting System: No more manual reports, no more waiting for takedowns. This thing's a bot-busting machine gun, firing off reports faster than Elon tweets memes.
- Community Collaboration Platform: This ain't a solo mission. We're building an army of truth-tellers. Join the fight, share intel, and help us crush the botswarm.
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Improved User Experience:
- Enhanced Command-Line Interface: So slick, even your grandma can use it (but hopefully she's not running a botnet).
- Comprehensive Documentation: We've got guides, tutorials, and FAQs to get you up to speed faster than a Tesla in Ludicrous Mode.
- Simplified Deployment: Setting this up is easier than ordering pizza. Get it running in minutes and join the fight.
Bug Fixes and Performance Optimizations
- Squashed bugs like they were mosquitos at a barbecue.
- Optimized the code so it runs smoother than a silk scarf on a freshly waxed car.
- Enhanced error handling and logging, because even the best systems need a little debugging love.
Future Roadmap
- More AI, more power. We're gonna make these bots wish they'd never been coded.
- Decentralized threat intelligence network. Sharing is caring, especially when it comes to bot-busting intel.
- Expanding to every corner of the digital world. No platform is safe, no bot will escape.
- More user control, because everyone likes to customize their weapons.
Acknowledgements
Big shout-out to the crew who made this happen. You're the real MVPs. And a nod to the OGs, Andrew Ng and Fei-Fei Li, for laying the foundation.
Join the Fight
Don't just stand there, join the revolution! Contribute, report, and spread the word. Together, we'll crush the botswarm and build a digital world where truth reigns supreme.
What's Changed
- orchestra.sh by @bearycool11 in #5
Full Changelog: V2.0.0...V3.0.0
This final v
Contributors
Assets 2
Deployment Phase 2
PRESS RELEASE
FOR IMMEDIATE RELEASE
Persistent Memory Logic Loop (PMLL) System Reaches V2.0.0 Milestone
November 15, 2024 – The Persistent Memory Logic Loop (PMLL) system, a groundbreaking framework designed to enhance adaptive, secure, and efficient AI systems, has officially reached its V2.0.0 release. This major update reflects significant advancements in performance, scalability, and functionality, positioning PMLL as a leading solution for persistent memory architectures in AI.
What’s New in V2.0.0?
Enhanced Core Logic Loop
Recursive Processing Optimization:
Improved efficiency in the pml_logic_loop.c file, reducing memory overhead and accelerating recursive updates to the knowledge graph.
Dynamic I/O socket handling ensures seamless data flow between subsystems.
Flag-Based Memory Consolidation:
Introduced smarter flag monitoring to trigger long-term memory updates and embedded graph consistency checks.
Security Upgrades
Advanced RSA Encryption:
Strengthened encryption mechanisms in encrypt_knowledge_graph.c to secure sensitive data within the knowledge graph.
Enhanced compatibility with OpenSSL, ensuring robust cryptographic support.
Expanded Memory Management
Efficient Memory Silos:
Upgraded write_to_memory_silos.c to improve data persistence and reduce latency in memory operations.
Introduced batch processing in cache_batch_knowledge_graph.c, optimizing large-scale graph storage.
Improved Knowledge Graph Handling
Dynamic Updates:
novel_topic.c and update_knowledge_graph.c now handle larger datasets with reduced processing time.
Redesigned graph traversal algorithms to ensure consistency across embedded and primary knowledge graphs.
Edge Case Handling:
Expanded the system's ability to gracefully integrate novel topics and adapt to unpredictable data flows.
Seamless System Integration
Streamlined Build Process:
Simplified compilation and configuration steps for faster deployment.
Added support for customizable memory and RSA key configurations.
Why V2.0.0 Matters:
Unparalleled Memory Recall:
Leveraging a recursive logic loop, PMLL achieves faster, more accurate memory recall, reducing redundant data processing and improving response times.
Scalability and Adaptability:
With batch processing and smarter memory silos, the system scales effortlessly, handling complex, dynamic knowledge graphs.
Privacy and Security First:
State-of-the-art encryption ensures that sensitive knowledge graphs remain protected, aligning with industry standards for secure AI systems.
A Game-Changer for AI Research:
The PMLL framework transforms how AI systems manage short-term and long-term memory, setting a new benchmark for persistent memory architectures.
Acknowledgments
This release builds upon the foundational work of Josef Kurk Edwards, whose vision for a recursive memory logic loop has redefined AI memory architecture. Obi Oberdier played a critical role in peer-reviewing and validating the system, while the VeniceAI Team provided invaluable support during development.
What’s Next?
As the PMLL system continues to evolve, the focus will shift to:
Scaling for Enterprise-Level Applications:
Further optimizing performance for large datasets and high-traffic environments.
AI Ethics and Explainability:
Incorporating features to enhance transparency and accountability in AI decision-making.
Community Engagement:
Expanding open-source contributions and fostering collaboration to drive innovation.
How to Access V2.0.0
The latest version of the PMLL system is available now on GitHub:
https://github.com/bearycool11/pmll
For media inquiries or more information about the PMLL system, please contact:
Josef Kurk Edwards
Lead Developer and Founder
Email: joed6834@colorado.edu
GitHub: https://github.com/bearycool11
President and Vice-president of the Advisor Board:
Lei-Lei Fi
Andrew Ng
Board Advisors:
Elon Musk
Nate Bookout
About PMLL
The Persistent Memory Logic Loop (PMLL) is an innovative framework for adaptive, secure, and scalable AI systems. Developed by Josef Kurk Edwards, PMLL redefines AI memory management by integrating recursive logic loops with persistent memory silos and encrypted knowledge graphs. For more information, visit the GitHub repository.
V1.1.0
PMLL Repository
Overview
The PMLL (Personalized Machine Learning Layer) is a dynamic, recursive memory system designed to continually improve and evolve. It features an infinite recursive callback that ensures continuous updates to the embedded knowledge graph, improving the accuracy and adaptability of the system.
Features
Recursive Memory Structure: A self-updating knowledge graph powered by recursive callbacks, ensuring up-to-date information and insights.
Accuracy Thresholding: Refined for-iteration loop parameters to achieve optimal performance.
Collaborative Tools: The repository is open for collaboration. With access to Venice and Llama, the team can contribute and review code in a collaborative and transparent manner.
Recent Updates
The repository has been fully set up, committed, and pushed to GitHub, ensuring that it’s accessible for all collaborators.
With the repository's permissions now set, the team can freely review, contribute, and provide feedback. This enhances collaborative potential.
We've reached a milestone where the hard work of setting up the system is complete, and the repository is now ready for future development by the team, including new features, bug fixes, and improvements.
Next Steps
Collaborative Mode: The repository is now in a collaborative phase, where team members (Paul, Fin, Elon, Sam, Rachel, and others) can take the lead in contributing, experimenting, and improving the code.
Relax and Reap Rewards: With the system up and running, it's time to relax, knowing the foundation has been laid for others to build upon.
Contributing
We welcome contributions from the community. Please fork the repository, create a branch, and submit a pull request with your changes.
Full Changelog: bearycool11/pmll@V1.0.0...V1.1.0
PMLL 1.0 Framework
- Left and right hemispheres defined.
- diagnosis mode defined, allowing for specific information from left or right to have more information or less information when prompted (less or more information).
- PMLL(); Persistent Memory Recursive Logic Loop formally defined within C to call back infinitely and redraw the knowledge graph, instead of tree hierarchy, from the consolidated long term memory subsystem. This allows for batch serialized embedded knowledge graphs from 6-7 arbitrarily size bits (one bit being about 2 paragraphs worth of information when batch is uncached, deserialized and drawn up).
- Architectural subsystems use linguistical constructs of the human brain to communicate to one another.
- For logic loop reference point, see Azure Logic Apps as reference .