- Introduced `ibc_example.rs` demonstrating Inter-Blockchain Communication operations including cross-chain transfers, channel management, packet handling, and relayer operations.
- Introduced `zk_example.rs` showcasing Zero-Knowledge proof operations such as circuit compilation, proof generation and verification, and on-chain verification with multiple proving systems.
Privacy SDK features:
- Confidential transactions with Pedersen commitments
- Bulletproof range proofs for value validation
- Ring signatures for anonymous signing with key images
- Stealth addresses for unlinkable payments
- Blinding factor generation and value operations
Contract SDK features:
- Smart contract deployment (standard and CREATE2)
- Call (view/pure) and Send (state-changing) operations
- Event log filtering, subscription, and decoding
- ABI encoding/decoding utilities
- Gas estimation and contract verification
- Multicall for batched operations
- Storage slot reading
Languages implemented:
- JavaScript/TypeScript
- Python (async with httpx)
- Go
- Rust (async with reqwest/tokio)
- Java (async with OkHttp)
- Kotlin (coroutines with Ktor)
- Swift (async/await with URLSession)
- Flutter/Dart
- C (header-only interface)
- C++ (header-only with std::future)
- C#/.NET (async with HttpClient)
- Ruby (Faraday HTTP client)
All SDKs follow consistent patterns:
- Configuration with API key, endpoint, timeout, retries
- Custom exception types with error codes
- Retry logic with exponential backoff
- Health check endpoints
- Closed state management
Add Economics, Governance, and Mining SDKs for:
- Java: Full SDK with CompletableFuture async operations
- Kotlin: Coroutine-based SDK with suspend functions
- Swift: Modern Swift SDK with async/await
- Flutter/Dart: Complete Dart SDK with Future-based API
- C: Header files and implementations with opaque handles
- C++: Modern C++17 with std::future and PIMPL pattern
- C#: Records, async/await Tasks, and IDisposable
- Ruby: Struct-based types with Faraday HTTP client
Also includes minor Dart lint fixes (const exceptions).
Phase 3 SDK expansion - Flutter/Dart implementations:
- Database SDK: Multi-model database with Key-Value, Document,
Vector, and Time Series stores
- Hosting SDK: Decentralized web hosting with domain management,
DNS, deployments, and SSL provisioning
- Bridge SDK: Cross-chain asset transfers with lock-mint and
burn-unlock patterns
All SDKs follow consistent patterns with:
- Async/await API using Futures
- Proper error handling with typed exceptions
- Configurable retry logic and timeouts
- Full type safety with Dart's type system
- Implement SynorStorage class for decentralized storage operations including upload, download, pinning, and CAR file management.
- Create supporting types and models for storage operations such as UploadOptions, Pin, and StorageConfig.
- Implement SynorWallet class for wallet operations including wallet creation, address generation, transaction signing, and balance queries.
- Create supporting types and models for wallet operations such as Wallet, Address, and Transaction.
- Introduce error handling for both storage and wallet operations.
- Created package_graph.json to define project structure and dependencies for synor_compute.
- Added version file to specify the Dart SDK version.
- Generated pubspec.lock to lock dependencies and their versions for the project.
Adds comprehensive model management and training capabilities:
synor-compute (Rust):
- ModelRegistry with pre-registered popular models
- LLMs: Llama 3/3.1, Mistral, Mixtral, Qwen, DeepSeek, Phi, CodeLlama
- Embedding: BGE, E5
- Image: Stable Diffusion XL, FLUX.1
- Speech: Whisper
- Multi-modal: LLaVA
- ModelInfo with parameters, format, precision, context length
- Custom model upload and registration
- Model search by name/category
Flutter SDK:
- Model registry APIs: listModels, getModel, searchModels
- Custom model upload with multipart upload
- Training APIs: train(), fineTune(), trainStream()
- TrainingOptions: framework, epochs, batch_size, learning_rate
- TrainingProgress for real-time updates
- ModelUploadOptions and ModelUploadResult
Example code for:
- Listing available models by category
- Fine-tuning pre-trained models
- Uploading custom Python/ONNX models
- Streaming training progress
This enables users to:
1. Use pre-registered models like 'llama-3-70b'
2. Upload their own custom models
3. Fine-tune models on custom datasets
4. Track training progress in real-time
