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Dim Language Specification Implementation Plan

Design a statically-compiled, high-performance language that unifies Python ergonomics with C/C++ performance, featuring first-class AI and ML support.

Implementation Status: Phase 2 complete — lexer, parser, type checker, MIR, borrow checker, LLVM codegen all implemented in Python. 35 tests passing.

Proposed Design Sections

1. Language Core & Syntax

  • Indentation-based, minimal punctuation.
  • Static typing with inference.
  • Algebraic Data Types (ADTs), Pattern Matching.
  • Traits/Interfaces.

2. Compiler Architecture

  • Pipeline: Lexer → Parser → AST → Semantic Analysis → MIR (Mid-level IR) → MLIR/LLVM IR.
  • Multi-target: Native, WASM, JavaScript.

3. Memory & Execution Model

  • Hybrid: Determinism (Ownership) + Region-based + Optional GC.
  • Memory safety by default, unsafe blocks for systems work.
  • Zero-cost abstractions.

4. Concurrency & Asynchrony

  • Structured concurrency, async/await.
  • Actors and green threads.
  • Backend-specific primitives (epoll, kqueue, JS event loop).

5. AI/LLM & ML First-Class Citizens

  • Native prompt and model types.
  • Built-in tensor types and automatic differentiation.
  • GPU acceleration (CUDA/ROCm) as a standard feature.

6. Security & Systems

  • Constant-time crypto primitives.
  • Binary parsing and symbolic execution modules.
  • Sandbox and audit modes.

7. Interop & Tooling

  • Stable C ABI.
  • Seamless JS/TS FFI.
  • Package manager, linter, formatter, and ID-integrated debugging.

Verification Plan

Design Review

  • Self-consistency check of the grammar and type system.
  • Feasibility analysis of the hybrid memory model.
  • Mapping AI/ML constructs to LLVM/MLIR.

Specification Validation

  • Create "Hello, World" and "AI Agent" code snippets in Dim to verify syntax ergonomic.
  • Draft small IR examples to demonstrate compiler transformations.