- Rename npm package from ruvector-math-wasm to @ruvector/math-wasm
- Update README with correct scoped package name
- Update workflow to publish with scoped name
- Add scripts/test-wasm.mjs for WASM package testing
- Consistent with @ruvector/attention-* naming convention
Published:
- @ruvector/math-wasm@0.1.31 on npm
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
## Summary
- Add PowerInfer-style sparse inference engine with precision lanes
- Add memory module with QuantizedWeights and NeuronCache
- Fix compilation and test issues
- Demonstrated 2.9-8.7x speedup at typical sparsity levels
- Published to crates.io as ruvector-sparse-inference v0.1.30
## Key Features
- Low-rank predictor using P·Q matrix factorization for fast neuron selection
- Sparse FFN kernels that only compute active neurons
- SIMD optimization for AVX2, SSE4.1, NEON, and WASM SIMD
- GGUF parser with full quantization support (Q4_0 through Q6_K)
- Precision lanes (3/5/7-bit layered quantization)
- π integration for low-precision systems
🤖 Generated with [Claude Code](https://claude.com/claude-code)
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
