- Deploy TURN server (coturn) on GCE at 34.72.154.225:3478
- Update webrtc.js with self-hosted TURN as primary
- Add Edge-Net STUN server as primary option
- Update signaling to use Cloud Run Genesis as default
- Deploy dashboard to Cloud Run at edge-net-dashboard
- Bump to v0.4.5
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- Update NativeStats interface to match actual native module exports
- Map training_steps to patternsLearned for API compatibility
- Fix stats() returning undefined values
- Bump version to 0.2.4
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## Task Execution System (NEW)
- Created task-execution-handler.js with TaskExecutionHandler class
- Tasks broadcast via signals are now ACTUALLY executed by receiving nodes
- Results sent back via 'task-result' signaling
- TaskValidator for security (signature verification, capability matching)
- DistributedTaskNetwork for easy integration
## Genesis Node Deployment (FIXED)
- Fixed port sharing for Cloud Run (WebSocket + HTTP on same port)
- Deployed to: https://edge-net-genesis-875130704813.us-central1.run.app
- WebSocket verified working
- Health endpoints working
## Multi-Tenancy (VERIFIED)
- 3+ nodes discover each other
- Task broadcast works
- Signaling (offer/answer/ICE) works
## Tests Added
- tests/multitenancy-test.js
- tests/task-execution-test.js
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- Add networks.js with NetworkGenesis, NetworkRegistry, and MultiNetworkManager
- Support for public, private (invite-only), and consortium networks
- Each network has its own genesis block, QDAG ledger, and peer registry
- Network IDs derived from genesis hash for tamper-evident identity
- Invite code generation for private networks with base64url encoding
New CLI options:
--networks List all known networks
--discover Discover available networks
--create-network Create a new network with custom name/type
--network-type Set network type (public/private/consortium)
--switch Switch active network for contributions
--invite Provide invite code for private networks
Security features:
- Network isolation with separate storage per network
- Cryptographic network identity from genesis hash
- Invite codes for access control on private networks
- Ed25519 signatures for network announcements
Well-known networks:
- mainnet: Primary public compute network
- testnet: Testing and development network
- Add network.js with peer discovery, QDAG contribution ledger, and
contribution verification protocol
- Add join.html for browser-based network joining with Web Crypto API
- Update join.js with NetworkManager integration for QDAG recording
- Add --peers and --network commands for network status viewing
- Update package.json with new files and scripts
The QDAG (Quantum DAG) ledger provides:
- Contribution recording with parent selection for DAG structure
- Weight-based confirmation (3 confirmations for finality)
- Peer-to-peer synchronization support (simulated in local mode)
- Contributor statistics and network-wide metrics
The browser join page provides:
- WASM-based Pi-Key identity generation
- PBKDF2 + AES-256-GCM encrypted identity backup/restore
- Real-time contribution tracking and credit display
- localStorage persistence for cross-session identity
- Implement PersistentIdentity class for months/years persistence
- Store identities in ~/.ruvector/identities with encrypted backup
- Track contribution history in ~/.ruvector/contributions
- Add --list command to show all stored identities
- Add --history command to show contribution milestones
- Auto-restore identities across sessions
- Track "return after absence" milestones (>30 days)
- Session tracking with timestamps
- Add multi-contributor-test.js for network simulation
- All contributions preserved indefinitely
- Add join.js CLI for joining EdgeNet with public key identity
- Support generating new Pi-Key identities with Ed25519 signing
- Enable encrypted identity export/import (Argon2id + AES-256-GCM)
- Add multi-contributor demonstration and cross-verification
- Update main CLI to include join command
- Fix test file syntax errors and assertion bounds
- All 186 Rust tests pass, WASM module fully functional
Security Fixes:
- CRITICAL: Add zeroize on drop for FinancialProver to prevent memory extraction
- HIGH: Fix WASM type import (ProdVerificationResult -> VerificationResult)
- MEDIUM: Add input validation for zero rent/multiplier/budget values
- Use checked_mul instead of saturating_mul for overflow detection
Performance Optimizations:
- Reduce generator memory from 16 MB to 8 MB (1-party vs 16-party)
- Add zeroize dependency (1.8) for secure memory clearing
Documentation:
- Add comprehensive ZK performance analysis docs
- Add benchmark suite for criterion testing
- Add optimization quick reference and examples
All 7 production ZK tests pass.
Security Fixes:
- Remove blinding factor from Commitment struct (was leaking secrets)
- Add per-installation unique salt for key derivation (was hardcoded)
- Add prominent security warnings to zkproofs.rs (demo-only crypto)
- Document that ZK implementation is for API demonstration only
Performance Fixes:
- Fix memory leak: category_embeddings now uses HashMap instead of Vec
- Add LRU-style eviction at 10k embeddings capacity
- Prevents unbounded memory growth that would crash browser
Code Quality:
- Add max_embeddings configuration option
- Better documentation for data structures
- Add security audit report and optimization guides
⚠️ IMPORTANT: The ZK proof cryptography is simplified for demonstration.
For production use, replace with bulletproofs, curve25519-dalek, merlin crates.
Implements ZK proofs that allow users to prove financial statements without
revealing actual numbers. Key features:
- Bulletproofs-style range proofs (no trusted setup required)
- Pedersen commitments to hide actual values
- Proof types: income, affordability, savings, overdraft, debt ratio
- Complete rental application proof bundle
- All proof generation runs in browser WASM
Components:
- examples/edge/src/plaid/zkproofs.rs: Core ZK proof system
- examples/edge/src/plaid/zk_wasm.rs: WASM bindings for browser
- examples/edge/pkg/zk-financial-proofs.ts: TypeScript API
- examples/edge/pkg/zk-demo.html: Interactive demo
Use cases:
- Rental applications: Prove income ≥ 3× rent without revealing salary
- Loan pre-qualification: Prove DTI ratio without revealing debts
- Employment verification: Prove minimum salary without exact pay
- Account stability: Prove no overdrafts without transaction history
Privacy guarantee: Verifier mathematically CANNOT extract actual numbers
from the proof - only learns whether statement is true or false.
Implements a privacy-preserving financial learning system that runs entirely
in the browser using WebAssembly. Key features:
- PlaidLocalLearner: Browser-local ML engine with IndexedDB persistence
- Q-learning for budget optimization and spending recommendations
- HNSW vector index for semantic transaction categorization
- Spiking neural network for temporal pattern recognition
- Anomaly detection for unusual transaction flagging
- Zero data exfiltration - all learning stays client-side
Components:
- examples/edge/src/plaid/mod.rs: Core Rust learning algorithms
- examples/edge/src/plaid/wasm.rs: WASM bindings for browser
- examples/edge/pkg/plaid-local-learner.ts: TypeScript API wrapper
- examples/edge/pkg/plaid-demo.html: Interactive demo page
- examples/edge/docs/plaid-local-learning.md: Comprehensive documentation
Privacy guarantees:
- Financial data never leaves the browser
- Optional AES-256-GCM encryption for IndexedDB storage
- User can delete all data instantly
- No analytics, telemetry, or tracking
- Add EdgeNet service with real WASM module initialization from CDN
- Add PiKey cryptographic identity store with Ed25519 signatures
- Add IndexedDB persistence for credits, tasks, and settings
- Add ConsentWidget for CPU/GPU contribution with settings modal
- Add IdentityPanel for crypto identity management
- Add DocumentationPanel with comprehensive user guide
- Add SpecializedNetworks component for network communities
- Deploy Edge-Net Genesis Relay to Google Cloud Run with security:
- Origin validation (CORS whitelist)
- Rate limiting (100 msgs/min per node)
- Message size limits (64KB)
- Connection timeout (30s heartbeat)
- Max 5 connections per IP
- Update Header with Edge-Net branding
- Update Sidebar with Docs tab
- Update networkStore to use real WASM stats
- Configure dashboard to connect to Genesis relay
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- Build dual WASM targets (web + nodejs) for universal compatibility
- Add Node.js polyfills for web APIs (crypto, performance, window, document)
- Create universal entry point with auto-detection of environment
- Update CLI with comprehensive benchmark, demo, and info commands
- Fix ESM/CJS compatibility with .cjs extension for Node.js module
- Package includes both browser and Node.js WASM binaries
Published to npm as @ruvector/edge-net v0.1.1
Package: 885.4 kB compressed, 3.2 MB unpacked
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- Implement comprehensive tests for adaptive learning mechanisms including MicroLoRA and SONA in learning_tests.rs.
- Introduce tests for bio-inspired neural components such as HDC, BTSP, and Spiking Neural Networks in nervous_system_tests.rs.
- Create common utilities for random vector generation, vector assertions, and softmax calculations in mod.rs.
- Ensure all tests validate expected behaviors and maintain numerical stability.
Four attention mechanisms answering fundamental questions:
- Neural Attention: What words/tokens matter?
- DAG Attention: What computational steps matter?
- Graph Attention: What relationships matter?
- State Space: What history still matters?
Includes:
- dag_attention.rs: Critical path analysis, topological ordering
- attention_unified.rs: Unified interface composing all 4 types
- Updated mod.rs architecture diagram
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- Enable capabilities module with pub export
- Add compute/ module with SIMD, WebGPU, WebGL backends
- Add ai/ module with attention, router, federated learning, LoRA
- Streamline WASM API for Time Crystal, NAO, MicroLoRA, HDC, WTA, BTSP
- Add Global Workspace and Morphogenetic network support
- Add learning scenarios for error recovery and file sequences
- Add swarm collective intelligence and consensus modules
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Critical security fixes before production deployment:
1. Argon2id PBKDF in pikey/mod.rs (replaces SHA-256)
- Memory-hard KDF with 64MB memory, 3 iterations
- Version 0x02 format with salt, backward compatible with v1
- Secure zeroization of key material
2. Ed25519 signature verification in rac/mod.rs
- Real cryptographic verification for authority resolutions
- ScopedAuthority::sign_resolution() helper for signing
- Canonical message format for verification
3. Password-protected key export in identity/mod.rs
- export_secret_key now requires 8+ character password
- AES-256-GCM encryption with Argon2id-derived key
- import_secret_key for secure recovery
Dependencies added:
- argon2 v0.5 (memory-hard KDF)
- zeroize v1.7 (secure memory cleanup)
Test coverage:
- 125 tests passing (40 lib + 85 integration)
- Updated adversarial tests with real Ed25519 signatures
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## Learning Module (src/learning/mod.rs)
- ReasoningBank for pattern storage with similarity lookup and pruning
- TrajectoryTracker ring buffer for task execution tracking
- Spike-driven attention for 87x energy efficiency (based on Yao et al.)
- Multi-head attention for distributed task routing
- NetworkLearning unified interface for edge nodes
## RAC Module (src/rac/mod.rs) - Adversarial Coherence Thesis
Implements the 12 axioms for browser-scale adversarial truth maintenance:
1. Connectivity is not truth
2. Everything is an event
3. No destructive edits (deprecation only)
4. Every claim is scoped
5. Semantics drift is expected
6. Disagreement is signal
7. Authority is scoped, not global
8. Witnesses matter
9. Quarantine is mandatory
10. All decisions are replayable
11. Equivocation is detectable
12. Local learning is allowed
Core components:
- Append-only Merkle event log for tamper-evident history
- CoherenceEngine for conflict detection and resolution
- QuarantineManager for contested claims
- Authority policy and verifier traits
- Decision traces for audit and replay
## Integration
- Learning and RAC integrated into EdgeNetNode
- 28 tests pass (13 new tests for learning/RAC)
References:
- FLP Impossibility (MIT CSAIL)
- PBFT Byzantine Fault Tolerance
- CRDTs (Lip6)
- RFC 6962 Certificate Transparency
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Updated README to focus on research and simulation aspects:
- Renamed to 'Artificial Life Simulation'
- Cells instead of nodes terminology
- Energy system instead of cryptocurrency
- Clear disclaimer: NOT a financial product
- Research goals and applications
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- Add Concurrency section with 4 worker modes:
- Main Thread (no workers, simple)
- Worker Pool (auto-scaling, recommended)
- Dedicated (one worker per task type)
- Shared Worker (cross-tab coordination)
- Add comprehensive worker templates with code examples
- Update stats to show worker count
- Include WorkerPool class with batch operations
- Add SharedWorker cross-tab example
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- Add comprehensive Web Workers section explaining UI responsiveness
- Include WorkerPool usage examples with practical code
- Add feature table explaining auto-scaling, load balancing, timeouts
- Add "when to use workers" guidance table
- Update Table of Contents with Consensus Modes and Web Workers
- Add WorkerPool to API Reference section
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- Add npm package section at top with install commands
- Link to pkg/README.md for JavaScript documentation
- Clarify Raft vs Gossip+CRDT consensus modes
- Add Web Worker pool to distributed systems features
- Update WASM badge to show 364KB size
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- Include worker.js and worker-pool.js in package
- Add exports for @ruvector/edge/worker and @ruvector/edge/worker-pool
- Document WorkerPool API with examples
- Features: round-robin distribution, batch splitting, load balancing
- Keeps UI responsive during heavy WASM operations
- Bump to v0.1.9
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Raft assumes stable membership and trusted nodes - not suitable for
wild browser swarms. Updated docs to:
- Position Raft for "trusted cohorts" (teams, enterprise, private relays)
- Add Gossip + CRDT for "open swarms" (public, high-churn, adversarial)
- Explain when to use each mode with code examples
- Update capability tables to reflect both consensus strategies
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- Update heading to "Free Self-Learning AI Swarms at the Edge"
- Emphasize self-optimizing agents that get smarter over time
- Mention LoRA fine-tuning, EWC++ continual learning, ReasoningBank
- Bump to v0.1.7
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- Add comprehensive platform diagram showing edge vs edge-full
- List all capabilities of edge-full modules
- Add optional/peer dependency on @ruvector/edge-full
- Show usage patterns for both packages together
- Bump to v0.1.6
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- 5-step tutorial: Identity, Crypto, Vector Search, Routing, Consensus
- P2P transport options: WebRTC, GUN.js, IPFS/libp2p, Nostr
- Full code examples for each transport integration
- Architecture diagrams showing edge-first design
- Transport comparison table with recommendations
- Complete API reference for all 9 WASM exports
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- Emphasize zero-cost edge-first architecture
- Add economics comparison (cloud vs edge)
- Document all WASM APIs with examples
- Add use cases and architecture diagram
- Compare with cloud alternatives (OpenAI, LangChain, AutoGPT)
- Include agentic-flow integration example
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- Built WASM bindings with wasm-pack for browser/Node.js usage
- Exports: WasmIdentity, WasmCrypto, WasmHnswIndex, WasmSemanticMatcher,
WasmRaftNode, WasmHybridKeyPair, WasmSpikingNetwork, WasmQuantizer,
WasmAdaptiveCompressor
- 364KB optimized WASM binary with full TypeScript types
- Published to npm as @ruvector/edge@0.1.0
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