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This comprehensive implementation includes: ## Core Components - router-core: High-performance Rust vector database library * HNSW indexing for O(log n) search complexity * SIMD-optimized distance calculations (L2, Cosine, Dot, Manhattan) * Multiple quantization techniques (Scalar, Product, Binary) * Storage layer with redb and memory-mapped files * Full AgenticDB API compatibility - router-ffi: NAPI-RS Node.js bindings * Zero-copy buffer operations with Float32Array * Async/await support with Tokio * TypeScript type definitions auto-generated - router-wasm: WebAssembly target * Browser-compatible WASM bindings * WASI support for filesystem access - router-cli: Command-line interface * Database creation and management * Benchmarking and performance testing * Interactive queries ## Features Implemented - Sub-millisecond vector search with HNSW - 4-32x memory compression via quantization - Multi-platform support (Node.js, Browser, Native) - AgenticDB API compatibility - Comprehensive test suite - Criterion.rs benchmarks ## Build System - Cargo workspace configuration - Release builds with LTO optimization - NPM package setup for multi-platform binaries ## Claude Flow Integration - Initialized swarm system with collective memory - Hive Mind system for distributed cognition - ReasoningBank for AI-powered memory - Complete command structure for workflow automation Built to specification from Tiny Dancer technical requirements and Ruvector architectural plan.
- Fix unused variable warnings in CLI - Add documentation to error fields - Use Clippy suggestions: * Replace manual modulo with .is_multiple_of() * Use .div_ceil() instead of manual ceiling division * Derive Default instead of manual implementation - Remove unused imports - Clean build with zero warnings (except profile location notices) All changes improve code quality and follow Rust best practices.
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…SWRaPAJ8VoVFagt8spp6 Set up Claude Flow swarm initialization
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Apr 24, 2026
Memory audit finding #4: encode_query_packed previously did q.to_vec() // alloc #1 (unit buffer) self.rotation.apply(&unit) // alloc #2 (rotated buffer) vec![0u64; n_words] // alloc #3 (returned packed words) per query. 3 heap allocations per search, firing at ~10k QPS, caused measurable allocator contention under concurrent load. Fix: thread_local scratch holds (unit_buf, rotated_buf) across queries on the same thread. RandomRotation gains an apply_into(&[f32], &mut [f32]) variant that writes into the scratch rather than allocating. Only the returned Vec<u64> is freshly allocated (the caller needs ownership). Net: 3 → 1 allocation per query on the hot path. New RandomRotation::apply_into is the building block for future in-place paths; apply() is now a thin wrapper around it. Measured QPS lift at n=100k (stacked with earlier iter-2/3 security + flatten): single-thread QPS: 2,975 → 3,137 (+5%) concurrent 1-shard: 23,681 → 24,255 (+2%) The uplift is smaller than the profiler's 30–50% estimate because at n=100k the scan dominates query encoding. On smaller collections (n=5k) where encoding is a larger fraction the relative win is similar. Allocator contention dominates only at much higher QPS. 23 rabitq tests passing. Clippy -D warnings clean. Co-Authored-By: claude-flow <ruv@ruv.net>
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This pull request introduces comprehensive documentation for Claude Flow CLI commands, grouped by major operational themes: agents, analysis, automation, GitHub integration, and hive-mind coordination. Each command group now has a dedicated README and detailed sub-command guides, covering usage, options, examples, workflows, and best practices. This significantly improves discoverability and usability for developers working with Claude Flow.
Agents Command Documentation:
.claude/commands/agents/README.mdwith links to agent command guides, including types, capabilities, coordination, and spawning.agent-types.md), capabilities matrix (agent-capabilities.md), coordination patterns (agent-coordination.md), and agent spawning using Claude Code's Task tool (agent-spawning.md). [1] [2] [3] [4]Analysis Command Documentation:
.claude/commands/analysis/README.mdand detailed guides for bottleneck detection (bottleneck-detect.md), token usage analysis (token-usage.md), and performance reporting (performance-report.md). [1] [2] [3] [4]Automation Command Documentation:
.claude/commands/automation/README.mdand guides for auto agent spawning (auto-agent.md), smart spawn (smart-spawn.md), and workflow selection (workflow-select.md). [1] [2] [3] [4]GitHub Integration Documentation:
.claude/commands/github/README.mdand guides for GitHub swarm management (github-swarm.md), repository analysis (repo-analyze.md), PR enhancement (pr-enhance.md), issue triage (issue-triage.md), and code review (code-review.md). [1] [2] [3] [4] [5] [6]Hive-mind Coordination Documentation:
.claude/commands/hive-mind/README.mdwith links to hive-mind operation guides, including initialization, spawning, status, consensus, memory, metrics, and wizard tools.