ruvnet

Tamper-Evident Witness Logs: State of the Art and Where RVM Stands

Update (2026-06-12, same day): Recommendations R1, R2, R4, and R6 from Part 6 are now implemented, tested, and merged (PR #558): chained seal digests with keyless whole-history verification (verify_seal_chain_binding), the new rvm-checkpoint crate emitting C2SP tlog-checkpoint / signed-note output (the Go sumdb/note reference vector reproduces byte-identically), per-segment forward-secure key ratcheting with in-critical-section key erasure, and CoveragePolicy::Strict backpressure invariants wired into the security gate ("no witness, no mutation" fails closed). Combined suite: 926 tests, 0 failures; the append hot path is unchanged (all new state is seal-time-only; control-normalized bench). The post-compromise property now has a test: an attacker holding the current chain key who rewrites sealed history and recomputes the entire downstream MAC chain is detected via ratchet

ruvnet

RuView Soul Signature — passive WiFi biometric identity from hardware (no camera, no wearable). Repo: https://github.com/ruvnet/RuView · Spec: docs/research/soul/

RuView Soul Signature — passive WiFi biometric identity from hardware (no camera, no wearable). Repo: https://github.com/ruvnet/RuView · Spec: docs/research/soul/

Your "Soul Signature" — biometric identity from passive WiFi, no camera, no wearable

Project: RuView / WiFi-DensePose · Date: 2026-05-24 · Spec: docs/research/soul/ in the repo

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ruvnet

ruvllm_sparse_attention: Subquadratic Sparse Attention for Edge LLM Inference

What This Is (Simple Version)

AI language models are slow on small computers — not because of the model weights, but because of attention: the mechanism that lets every word look at every other word in the text. When you double the text length, attention gets four times harder, not twice.

ruvllm_sparse_attention fixes this by teaching the model to be selective. Instead of every word looking at every other word, it looks at:

• The words closest to it (recent context)
• A few anchor words at the start (global signals)

ruvnet

Architectural Comparison: Claude Flow V3 vs Claude Code TeammateTool

Date: 2026-01-25 Analysis: Side-by-side comparison of Claude Flow V3 swarm architecture (developed by rUv) and Claude Code's TeammateTool (discovered in v2.1.19)

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Executive Summary

A detailed analysis reveals striking architectural similarities between Claude Flow V3's swarm system and Claude Code's TeammateTool. The terminology differs, but the core concepts, data structures, and workflows are nearly identical.

kieranklaassen

Claude Code TeammateTool - Source Code Analysis

This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.

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Executive Summary

TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).

ruvnet

RuVector MinCut

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

ruvnet

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.

ruvnet

🚀 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

ruvnet

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

ruvnet

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.