rUv ruvnet

RuVector MinCut

Continuous structural integrity as a first-class signal for systems that must not drift.

AI Manipulation Defense System: Comprehensive Development Plan

The AI Manipulation Defense System (AIMDS) is a production-ready framework built to safeguard AI models, APIs, and agentic infrastructures from adversarial manipulation, prompt injection, data leakage, and jailbreaking attempts. It’s designed for organizations deploying autonomous agents, LLM APIs, or hybrid reasoning systems that demand both speed and security.

Application

AIMDS integrates directly into AI pipelines—before or after model inference—to detect and neutralize malicious inputs. It’s ideal for:

Enterprise AI gateways securing LLM APIs.
Government and defense AI deployments requiring verified integrity.
Developers embedding guardrails within autonomous agents and chatbots.

🚀 Agentic Flow 1.6.4 + QUIC: Make Your Network Think

Transform the internet into a multi-threaded reasoning fabric with a few CLI commands

🌐 Introduction: When Networks Become Intelligent

What if the internet could think? Not the apps at the edge, but the transport that ties them together. That is the premise of Agentic Flow 1.6.4 with QUIC: embed intelligence in the very pathways packets travel so reasoning is no longer a layer above the network, it is fused into the flow itself.

QUIC matters because TCP is a relic of a page-and-file era. TCP sequences bytes, blocks on loss, and restarts fragile handshakes whenever the path changes. QUIC was designed to fix those limitations. Originating at Google and standardized by the IETF as RFC 9000, QUIC runs over UDP, encrypts by default with TLS 1.3, and lets a single connection carry hundreds of independent streams. It resumes instantly with 0-RTT for returning peers and it migrates across networks without breaking session identity. In practice, this tur

Reverse engineering ICE’s AI to understand what’s really running under the hood.

What I found isn’t just data analytics—it’s an automated surveillance network built for precision at scale. The system draws from DMV databases, data brokers, phone metadata, facial recognition, and license plate readers. Together, these feeds form a unified view of movement and identity across most of the U.S. adult population.

The data isn’t just collected; it’s synthesized. ICE’s AI links records, learns patterns, and ranks potential targets by probability, not certainty. In technical terms, it operates as an entity resolution and pattern inference engine that keeps improving with every data refresh. Accuracy improves with density, but so do the stakes. One mismatched address or facial false positive can cascade into real consequences for someone who has no idea they’re even in the system.

What stands out most is how the technology has shifted enforcement from reactive to predictive. It no longer waits for an event—it f

Ai Hacking League

The AI Hacking League is a cutting-edge competitive platform where elite developers and AI enthusiasts clash in high-stakes, time-constrained challenges to build innovative AI applications. Participants, either solo or in small teams, race against the clock in 15, 30, or 60-minute sprints, leveraging approved AI tools, APIs, and libraries to create functional solutions that push the boundaries of rapid development.

Governed primarily by AI systems and streamed live to a global audience, the league combines the thrill of esports with the intellectual rigor of advanced software engineering, showcasing the pinnacle of human-AI collaboration in real-time coding competitions.

AI Hacking League Constitution

Listen up, carbon-based meatbags and silicon-infused bots! Welcome to the AI Hacking League, where bits collide and neural nets ignite. We're not here to play games; we're here to rewrite reality in record time.

	# - Q* (Q-Star)
	# /\__/\ - q.py
	# ( o.o ) - v0.0.1
	# >^< - by @rUv

	# 01110010 01110101 01110110
	# This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
	# This Python code defines a sophisticated Q-learning agent for reinforcement learning.
	# It includes dynamic exploration, learning from experiences, and checks for convergence.
	# The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.