Skip to content

Latest commit

 

History

History
54 lines (45 loc) · 2.19 KB

File metadata and controls

54 lines (45 loc) · 2.19 KB

AETHER Development Roadmap

This document outlines the version milestones and research directions for the AETHER programming language.

Milestone 1: Version 0.1 (Current Status)

  • Status: Completed
  • Focus: Build foundational parsing and JIT simulation frameworks.
  • Deliverables:
    • Lexical analyzer and parser for intent declarations and expressions.
    • Command-line toolchain (init, build, test, benchmark commands).
    • Production-ready crdt library implementing state-based semilattices (GCounter, GSet, PNCounter, ORSet).
    • Standing unit test suite.

Milestone 2: Version 0.2 (Next Phase)

  • Status: Planned
  • Focus: Type verification and developer tools.
  • Deliverables:
    • Full static type checker for variable mutations and schemas.
    • Multi-file module import system.
    • Detailed compile-time error reporting with line and column spans.

Milestone 3: Version 0.3

  • Status: Planned
  • Focus: Developer Experience (DX) tooling.
  • Deliverables:
    • Integrated package installer and solver.
    • Automatic code formatter.
    • Language Server Protocol (LSP) implementation for IDE completions.

Milestone 4: Version 0.4

  • Status: Planned
  • Focus: Real Execution Backends.
  • Deliverables:
    • LLVM compiler backend integration.
    • Native machine code generation (executables for Windows, macOS, Linux).
    • In-memory execution profiling.

Milestone 5: Version 1.0

  • Status: Long-Term Goal
  • Focus: Production Release.
  • Deliverables:
    • Standardized stable language specification.
    • Complete, optimized standard library coverage.
    • Production-ready compiler toolchain.

Long-Term Research Directions

These areas represent conceptual explorations for future computing architectures:

  • Quantum Hardware Integration: Translating simulated quantum primitive gate sequences to physical QPU instruction standards (such as OpenQASM).
  • Physical Swarm Runtime: Running CRDT state-based convergence across real multi-node network clusters.
  • Brain-Computer Interface: Mapping cognitive signal streams (EEG inputs) to language primitives under a formal hardware abstraction layer.
  • Autonomous Self-Healing: Real JIT recovery of fault states using dynamic runtime patch generation.