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6 commits

Author SHA1 Message Date
rUv
4b1fd0e286 fix(ci): Fix PostgreSQL Extension CI failures 
- Remove invalid feature flags (hybrid-search, filtered-search) that don't exist
- Replace with valid all-features flag for comprehensive testing
- Add PostgreSQL apt repository for older versions on Ubuntu 24.04
- Apply cargo fmt formatting to all crates

This fixes CI failures caused by:
- Feature flags that were planned but not implemented
- PostgreSQL 14 packages not available on Ubuntu 24.04 default repos

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 23:43:01 +00:00
rUv
9270abe86b ci: Add GitHub Actions for RuvLLM multi-platform native builds 
- Add ruvllm-native.yml workflow for building on all 5 platforms:
  - Linux x64 (ubuntu-latest)
  - Linux ARM64 (ubuntu-latest + cross-compile)
  - macOS Intel (macos-13)
  - macOS ARM (macos-14)
  - Windows x64 (windows-latest)

- Add N-API bindings (napi.rs) with full RuvLLM API:
  - SIMD inference engine
  - FastGRNN router
  - HNSW memory service
  - Embedding generator
  - SONA adaptive learning

- Create platform-specific npm packages:
  - @ruvector/ruvllm-linux-x64-gnu
  - @ruvector/ruvllm-linux-arm64-gnu
  - @ruvector/ruvllm-darwin-x64
  - @ruvector/ruvllm-darwin-arm64
  - @ruvector/ruvllm-win32-x64-msvc

- Update main @ruvector/ruvllm with all optional dependencies

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 21:59:27 +00:00
rUv
8828f60b2a feat(sona): Add HuggingFace export module and GitHub Actions for cross-platform npm builds 
- Add export module with SafeTensors, Dataset, HuggingFace Hub, and PretrainPipeline support
- Create GitHub Actions workflow for NAPI-RS cross-platform builds (Linux, macOS, Windows)
- Support 7 build targets: x64/ARM64 for Linux GNU/MUSL, macOS, Windows
- Add universal macOS binary via lipo
- Integrate ruvector-sona export into ruvLLM example with CLI tool
- Bump npm package to 0.1.3 with platform-specific optionalDependencies

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 07:10:39 +00:00
rUv
4d24844373 perf(sona): Optimize defaults based on benchmark findings 
Apply optimizations from vibecast benchmark reports:
- MicroLoRA rank-2: 5% faster than rank-1 (2,211 vs 2,100 ops/sec)
- Learning rate 0.002: +55.3% quality improvement
- Pattern clusters 100: 2.3x faster search (1.3ms vs 3.0ms)
- EWC lambda 2000: Better catastrophic forgetting prevention
- Quality threshold 0.3: Balance learning vs noise filtering

Add config presets:
- SonaConfig::max_throughput() for real-time chat
- SonaConfig::max_quality() for research/batch
- SonaConfig::edge_deployment() for mobile (<5MB)
- SonaConfig::batch_processing() for high throughput

Add OPTIMAL_BATCH_SIZE constant (32) based on benchmarks.

Bump versions: ruvector-sona 0.1.1, @ruvector/sona 0.1.2

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 06:47:58 +00:00
rUv
5bad08c6a3 feat(sona): Add SONA self-optimizing neural architecture 
Implement complete SONA system with:
- LoRA-Ultra: Adaptive low-rank adaptation for efficient fine-tuning
- Learning Loops: Instant, background, and coordinated learning modes
- EWC++: Enhanced elastic weight consolidation for continual learning
- ReasoningBank: Trajectory storage with verdict-based learning
- WASM bindings for browser deployment
- N-API bindings for Node.js integration
- Comprehensive documentation and benchmarks

New crate: crates/sona with full implementation
Integration: examples/ruvLLM with SONA module
NPM package: npm/packages/sona for JavaScript bindings

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 04:36:48 +00:00
rUv
2fb7186a38 feat: Add ruvLLM examples and enhanced postgres-cli 
Added from claude/ruvector-lfm2-llm-01YS5Tc7i64PyYCLecT9L1dN branch:
- examples/ruvLLM: Complete LLM inference system with SIMD optimization
  - Pretraining, benchmarking, and optimization system
  - Real SIMD-optimized CPU inference engine
  - Comprehensive SOTA benchmark suite
  - Attention mechanisms, memory management, router

Enhanced postgres-cli with full ruvector-postgres integration:
- Sparse vector operations (BM25, top-k, prune, conversions)
- Hyperbolic geometry (Poincare, Lorentz, Mobius operations)
- Agent routing (Tiny Dancer system)
- Vector quantization (binary, scalar, product)
- Enhanced graph and learning commands

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-03 01:26:47 +00:00