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A multi-phase strategy to achieve Vertical AI Integration. From language foundations to distributed infrastructure.
Phase 1: Aurelia Foundation
Alpha compiler, Lexer, Parser, and MLIR Integration. Building the foundation of the AI-native programming language.
Key Milestones
- Implement core lexer and parser in Rust
- Define AST and intermediate representation
- Integrate with MLIR for initial code generation
- Release v0.1.0-alpha to early adopters
Phase 2: Zenith Kernel
Probabilistic scheduling and AI-Watchdog immune system. The mathematically proven secure foundation for AI-native computing.
Key Milestones
- Develop probabilistic task scheduler
- Implement AI-Watchdog for zero-day exploit detection
- Establish secure IPC mechanisms
- Initial boot on reference NPU hardware
Phase 3: SkyOS & Generative UI
Large Action Models (LAM) and Semantic File System. The first truly AI-native operating system with generative interfaces.
Key Milestones
- Integrate Large Action Models into the core OS
- Develop the Semantic File System for context-aware storage
- Create the Generative UI framework for dynamic interfaces
- Developer preview release of SkyOS
Phase 4: DeepComet Model Family
Deployment of Prime, Zenith, Code, and Mobile tiers. A complete suite of models optimized for every layer of the stack.
Key Milestones
- Train and release DeepComet-Prime (Trillion-parameter)
- Optimize DeepComet-Zenith for kernel-level operations
- Release DeepComet-Code for Aurelia synthesis
- Deploy DeepComet-Mobile for edge devices
Phase 5: SkyCloud Infrastructure
Decentralized cloud network sharing idle NPU power. Democratizing access to AI compute through peer-to-peer infrastructure.
Key Milestones
- Design peer-to-peer NPU sharing protocol
- Implement secure execution enclaves for shared workloads
- Launch SkyCloud tokenomics and incentive structure
- Global network beta testing
Major Phases
Core Technologies
Parameters (Prime)
Possibilities