Resolved conflicts in examples/edge/README.md by keeping Web Workers
documentation additions from feature branch.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New CLI Commands:
- batch-learn: Process multiple learning experiences at once
- subscribe: Stream real-time learning updates (polling)
- watch: Auto-learn from file changes in real-time
New MCP Tools (49 total):
- hooks_batch_learn: Batch process experiences array
- hooks_subscribe_snapshot: Get state deltas for subscriptions
- hooks_watch_status: Get current watch/learning status
Features:
- Batch learning processes multiple experiences efficiently
- Subscribe streams events: learn, route, memory, compress
- Watch monitors file changes and auto-learns patterns
- Co-edit detection in watch mode (files edited within 1 min)
Published as v0.1.71
- Add null checks in getQ() method for patterns object
- Add null checks in updateQ() and learn() methods
- Improve load() to merge file data with defaults
- Ensures all required fields exist even with partial data
- Fixes 'Cannot read properties of undefined' errors
Published as v0.1.70
- Check this.data exists before accessing properties
- Initialize trajectories array if missing
- Add try/catch around engine and save operations
- Null-check trajectory objects in filter
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add defensive check for this.data.stats
- Initialize with defaults if missing
- Fixes "Cannot read properties of undefined (reading 'last_session')"
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New features in this release:
- 13 new CLI hooks commands (AST, Diff, Coverage, Graph, Security, RAG)
- 16 new MCP tools for Claude Code integration
- Enhanced hooks init with v2.0 templates
- 9-phase pretrain with attention/GNN capabilities
- Comprehensive CLAUDE.md documentation generation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added SIMD badge and documentation
- Added ParallelEmbedder API reference and usage examples
- Updated performance benchmarks with parallel vs sequential comparison
- Added browser compatibility table
- Added changelog section
- Added batch processing use case example
- Updated build instructions with SIMD flags
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- ParallelEmbedder class using Node.js worker_threads
- Distributes batches across multiple CPU cores
- Benchmark results: 3.6-3.8x speedup on batch processing
- Per-text latency drops from ~390ms to ~103ms with 4 workers
- Published v0.1.2 to npm and crates.io
Usage:
import { ParallelEmbedder } from 'ruvector-onnx-embeddings-wasm/parallel';
const embedder = new ParallelEmbedder({ numWorkers: 4 });
await embedder.init();
const embeddings = await embedder.embedBatch(texts);
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Enable WASM SIMD128 instructions for vectorized operations
- Update simd_available() to properly detect SIMD at compile time
- SIMD build is 180KB smaller than non-SIMD (more compact instructions)
- Published v0.1.1 to both npm and crates.io
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added npm and crates.io version badges
- WebAssembly and MIT license badges
- Quick start examples for Browser, Node.js, and Cloudflare Workers
- Complete API reference for WasmEmbedder, WasmEmbedderConfig
- Model comparison table with 6 HuggingFace models
- Performance benchmarks and use case examples
Published to npm as ruvector-onnx-embeddings-wasm@0.1.0
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Fixes PreToolUse:Read hook error by supporting --silent flag
for Read/Glob/Task hooks that should not produce output.
Published ruvector@0.1.54
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New optional companion package using Tract for inference:
- Runs in browsers, Cloudflare Workers, Deno, edge environments
- Same API as native crate
- JavaScript bindings via wasm-bindgen
- Supports all pooling strategies (Mean, Cls, Max, etc.)
Uses Tract instead of ONNX Runtime for WASM compatibility.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Changed invalid category "machine-learning" to "algorithms".
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The download logic would immediately fail if model.onnx wasn't at the
repo root, never trying the onnx/ subfolder where most sentence-transformer
models store their ONNX files.
Now tries both locations:
1. Root: {repo}/model.onnx
2. Subfolder: {repo}/onnx/model.onnx
Also applies fallback logic to auxiliary files (tokenizer.json, config.json).
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added hooks feature summary near top of README.md
- Created comprehensive HOOKS.md documentation
- Links to detailed docs for pretrain, build-agents, verify, etc.
