Your four points are correct:
1. Symbol mapping: Yes - ƒ → def, ∀ → for, λ → lambda, π → print, etc. Custom mappings are configurable.
2. Multi-layer caching: Confirmed - source content cache, transpiled Python cache, module import specs, and optimized bytecode with batch writes.
3. PhiRust acceleration: Clarification - it's a Rust-based transpiler that handles the symbol-to-Python conversion for performance, not converting Python to Rust. When files exceed 300KB, the system delegates transpilation to the Rust binary instead of using Python regex processing.
4. HTTP API: Yes - provides endpoints for transpilation, symbol mapping queries, and engine info to enable IDE integration.
The technical decision to use string replacement over AST manipulation came down to measured performance differences.
The benchmarks show 3,000,000+ chars/sec throughput on extreme stress tests and 1,200,000+ chars/sec on typical workloads. Where AST parsing, transformation, and regeneration introduces overhead that makes real-time symbol conversion impractical for large codebases.
The string replacement preserves exact formatting, comments, and whitespace while maintaining compatibility with any Python syntax. Including future language features that AST parsers might not support yet. Each symbol maps directly to its Python equivalent without intermediate representations that can introduce transformation errors.
The cache system includes integrity validation to detect corrupted cache entries and automatic cleanup of temporary files. Cache invalidation occurs when source files change, preventing stale transpilation results. Batch write operations with atomic file replacement ensure cache consistency under concurrent access.
The runtime serves cognitive improvements for domain-specific development. Mathematical algorithms become more readable when written with actual mathematical notation rather than verbose keywords. It can help in game development, where certain functions can benefit from different naming (eg.: def → skill, def → special, def → equipment).
The gradual adoption path matters for production environments. Teams can introduce custom syntax incrementally without rewriting existing codebases since the transpiled output remains standard Python. The multi-layer caching system ensures that symbol conversion overhead doesn't impact execution performance.
Domain-specific languages for mathematics, finance, education, or any field where visual clarity improves comprehension. The system maintains full Python compatibility while enabling cognitive improvements through customizable syntax.