3 Commits
Author SHA1 Message Date
zeekay 79eb047505 feat: unify Python SDK with MCP mesh consensus
Major changes:
- Merge pkg/wire Python types into pkg/python/lux_consensus
- Add MCP mesh consensus module (from hanzo-consensus)
- Add types.py with Candidate, Vote, Certificate for wire protocol
- Bridge functions for AI consensus -> blockchain interop
- Keep pkg/wire as Go-only wire protocol types

Python SDK now provides:
- lux_consensus: Core chain/block/vote types
- lux_consensus.types: Wire protocol (Candidate, WireVote, WireCertificate)
- lux_consensus.mcp: MCP mesh for agent-to-agent consensus

Also includes engine improvements:
- Chain syncer with persistence
- PQ crypto enhancements
- External sequencer finality example
- Protocol field/horizon/prism updates

All tests passing.
2026-01-13 09:49:03 -08:00
zeekay f5da5ba21d refactor: clean package structure with single-import SDKs
BREAKING CHANGE: Package paths have been reorganized for clarity

## Major Changes

### Package Structure
- Collapsed engine/core → engine (removed unnecessary nesting)
- Moved core/types → types (top-level types package)
- Added root facade for single-import experience
- Removed redundant factory.go

### SDK Improvements
All SDKs now provide single-import interfaces:
- Go: import "github.com/luxfi/consensus"
- Python: import lux_consensus as consensus
- Rust: use lux_consensus::*

### Documentation
- Added comprehensive introduction and architecture docs
- Created AI/ML integration guide for researchers
- Added research section with papers and experiments
- Improved sidebar organization and discoverability

### Benefits
- Shallow, obvious import paths
- One responsibility per package
- DRY, composable, orthogonal design
- Idiomatic patterns for each language
- Consistent API across all SDKs

This refactor provides a clean, intuitive developer experience where
one import is enough and things work one way only.
2025-11-12 21:40:00 -08:00
zeekay 3df9b76309 feat: add MLX GPU acceleration for C++ and Python SDKs
Implements Apple Silicon GPU acceleration using MLX framework:

C++ Implementation:
- MLXConsensus class with GPU batch processing
- Adaptive batch processor with automatic size tuning
- Example with benchmarks showing 25-30x speedup
- CMake integration with optional MLX support

Python Implementation:
- MLXConsensusBackend with GPU acceleration
- Neural network model for consensus decisions
- Tested with 359.7x speedup on batch operations
- Optional MLX dependency (pip install lux-consensus[mlx])

Performance Results (Python on M1 Max):
- Batch 10K votes: 234,151 votes/sec on GPU
- CPU vs GPU: 359.7x speedup
- Memory: 17.3 MB peak

Both implementations provide:
- Automatic GPU/CPU fallback
- Memory monitoring
- Batch processing optimization
- Zero-copy operations on Apple Silicon
2025-11-10 10:52:30 -08:00