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- Created aitbc/_version.py with centralized version definition - Updated aitbc/__init__.py to import __version__ from _version module - Updated constants.py to use __version__ for PACKAGE_VERSION - Replaced print() calls with logger in decorators.py, events.py, queue_manager.py, and state.py - Added logger initialization using get_logger(__name__) in config.py, decorators.py, events.py, queue_manager.py, and state.py - Added cli/commands
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2.7 KiB
AITBC Development Roadmap
Current Focus: v0.1 Release Preparation
Distribution & Binaries
- Debian stable miner binary (build workflow exists, binary built but distribution mechanism pending)
- Binary distribution via GitHub Releases (deferred until v1 release - policy: no GitHub Releases before v1)
Quality Assurance
- Cross-platform compatibility validation
- Security penetration testing
Codebase Quality & Technical Debt
CRITICAL (Short-term, 0-2 weeks)
- Replace print() statements with proper logging in aitbc/decorators.py, aitbc/events.py, aitbc/queue_manager.py, aitbc/state.py
- Fix bare except: in config.py line 79 - add logger assignment at top of file
- Coordinator-API service exports - either expand all 101 services to init.py or document lazy import pattern
HIGH (Medium-term, 2-6 weeks)
- Split massive service classes (advanced_reinforcement_learning.py 2,000 lines, certification_service.py 1,368 lines, multi_modal_fusion.py 1,324 lines)
- Consolidate CLI monolith (aitbc_cli.py 3,256 lines into existing commands/ directory structure - 19 files in aitbc_cli/commands/, 78 files in commands/)
- Improve test coverage - currently ~6% (55 test files / 21K lines for 351K-line codebase)
MEDIUM (Long-term, 1-3 months)
- Remove aitbc-core package (duplicates constants, logging, middleware from main repo - old version 0.3.0)
LOW (Nice to Have)
- Consolidate scattered documentation (100+ docs files across 40+ directories - deferred due to potential link breakage)
Upcoming Improvements
All "Upcoming Improvements" items have been completed and removed from this section.
Competitive Differentiators
Advanced Privacy & Cryptography
-
zkML + FHE Integration (Q3 2026)
- Zero-knowledge machine learning for private model inference
- Fully homomorphic encryption for private prompts and model weights
- Confidential AI computations without revealing sensitive data
-
Hybrid TEE/ZK Verification (Q4 2026)
- Combine Trusted Execution Environments with zero-knowledge proofs
- Dual-layer verification for enhanced security guarantees
- Support for Intel SGX, AMD SEV, and ARM TrustZone
Decentralized AI Economy
-
On-Chain Model Marketplace (Q3 2026)
- Smart contracts for AI model trading and licensing
- Automated royalty distribution for model creators
- Model versioning and provenance tracking on blockchain
-
Verifiable AI Agent Orchestration (Q4 2026)
- Decentralized AI agent coordination protocols
- Agent reputation and performance tracking
- Cross-agent collaboration with cryptographic guarantees
This roadmap continues to evolve as we implement new features and improvements.