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
- 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.
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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