mcard 0.1.30

MCard: Local-first Content Addressable Storage with Content Type Detection

MCard

MCard is a local-first, content-addressable storage platform with cryptographic integrity, temporal ordering, and a Polynomial Type Runtime (PTR) that orchestrates polyglot execution. It gives teams a verifiable data backbone without sacrificing developer ergonomics or observability.

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Highlights

• 🔐 Hash-verifiable storage: SHA-256 hashing, handle registry with history, immutable audit trail output.
• ♻️ Deterministic execution: PTR mediates 8 polyglot runtimes (Python, JavaScript, Rust, C, WASM, Lean, R, Julia).
• 📊 Enterprise ready: Structured logging, CI/CD pipeline, security auditing, 99%+ automated test coverage.
• 🧠 AI-native extensions: GraphRAG engine, optional LLM runtime, and optimized multimodal vision (moondream).
• 🧰 Developer friendly: Rich Python API, TypeScript SDK, BMAD-driven TDD workflow, numerous examples.
• 📐 Algorithm Benchmarks: Sine comparison (Taylor vs Chebyshev) across Python, C, and Rust.
• ⚡ High Performance: Optimized test suite (~37s) with runtime caching and session-scoped fixtures.

For the long-form narrative and chapter roadmap, see docs/Narrative_Roadmap.md. Architectural philosophy is captured in docs/architecture/Monadic_Duality.md.

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Quick Start (Python)

git clone https://github.com/xlp0/MCard_TDD.git
cd MCard_TDD
./activate_venv.sh          # installs uv & dependencies
uv run pytest -q -m "not slow"  # run the fast Python test suite
uv run python -m mcard.ptr.cli run chapters/chapter_01_arithmetic/addition.yaml

Create and retrieve a card:

from mcard import MCard, default_collection

card = MCard("Hello MCard")
hash_value = default_collection.add(card)
retrieved = default_collection.get(hash_value)
print(retrieved.get_content(as_text=True))

Quick Start (JavaScript / WASM)

See mcard-js/README.md for build, testing, and npm publishing instructions for the TypeScript implementation.

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Polyglot Runtime Matrix

Runtime	Status	Notes
Python	✅	Reference implementation, CLM runner
JavaScript	✅	Node + browser (WASM) + Full RAG Support
Rust	✅	High-performance adapter & WASM target
C	✅	Low-level runtime integration
WASM	✅	Edge and sandbox execution
Lean	⚙️	Formal verification pipeline (experimental)
R	✅	Statistical computing runtime
Julia	✅	High-performance scientific computing

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Project Structure (abridged)

MCard_TDD/
├── mcard/            # Python package (engines, models, PTR)
├── mcard-js/         # TypeScript implementation & npm package
├── chapters/         # CLM specifications (polyglot demos)
├── docs/             # Architecture, PRD, guides, reports
├── scripts/          # Automation & demo scripts
├── tests/            # >450 automated tests
└── requirements.txt / pyproject.toml

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Documentation

• Product requirements: docs/prd.md
• Architecture overview: docs/architecture.md
• Monad–Polynomial philosophy: docs/architecture/Monadic_Duality.md
• Narrative roadmap & chapters: docs/Narrative_Roadmap.md
• Logging system: docs/LOGGING_GUIDE.md
• PTR & CLM reference: docs/CLM_Language_Specification.md, docs/PCard%20Architecture.md
• Reports & execution summaries: docs/reports/

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Recent Updates (December 2025)

Session: December 10, 2025 — CLM Execution Refinements

CLM Runner & Test Infrastructure

• Unified Python CLI (scripts/run_clms.py): Single script to run all CLMs, by directory, or individual files with optional --context JSON injection.
• Fast Test Mode: Added @pytest.mark.slow to Lean-dependent tests; run fast tests with uv run pytest -m "not slow" (~20s vs 2+ minutes).
• Params Interpolation: Fixed balanced.test_cases to properly preserve when.params for ${params.xxx} variable substitution in config.
• Recursive Interpolation: Added _interpolate_recursive() to NetworkRuntime for nested batch operation configs.

Runtime Fixes

• Unified Execution: Python and JavaScript runtimes now execute the same set of CLMs with parity in path resolution, context passing, and builtin handling.
• Python Context Passing: Fixed _prepare_argument to pass context with operation/params keys to entry point functions—resolves complex arithmetic test failures.
• JavaScript Path Resolution: Fixed relative path execution issues in CLMRunner; CLMs now correctly resolve recursive calls and legacy formats regardless of execution context.
• Collection Loader: Enabled collection_loader runtime in JavaScript (CollectionLoaderRuntime) and verified with integration tests.
• Builtin Test Cases: Builtins (network runtime) now correctly execute balanced.test_cases instead of bypassing them.
• Orchestrator Input Override: CLMs called via orchestrator with explicit inputs (e.g., signaling_url) now skip default test cases.

Chapter-Specific Fixes

• chapter_01_arithmetic: All 27 CLMs passing across Python and JS runtimes.
• chapter_07_network: Fixed http_fetch.yaml to use runtime: network, enabling cross-platform HTTP execution.
• chapter_08_P2P: 13 CLMs passing, 3 skipped (Node.js-only marked as VCard).
  • WebRTC CLMs use mock://p2p for standalone testing.
  • Orchestrator overrides signaling_url for real connections.
  • Converted persistence_simulation and long_session_simulation from JavaScript to Python for better stability.

Previous Updates

CLM Test Infrastructure Improvements

• Execution Timing: Added timing logs to run-all-clms.ts to identify slow CLMs.
• Floating-Point Tolerance: Numeric comparisons now use configurable tolerance (1e-6) for floating-point precision.
• Input Context Handling: Introduced __input_content__ key to preserve original given values when merging when blocks.

Runtime Fixes (Prior Sessions)

• JavaScript Runtime: Changed runtime: node to runtime: javascript across CLMs; updated code to use target variable.
• Python Input Parsing: All Python implementations now handle bytes, str, and dict inputs with robust parsing.
• Lambda Calculus: Fixed parser to correctly handle parenthesized applications like (\\x.x) y.
• Orchestrator: Fixed run_clm_background to strip file extensions for proper filter matching.

Chapter-Specific Fixes (Prior Sessions)

• chapter_03_llm: Replaced LLM-dependent logic with mock implementations for test stability.
• chapter_05_reflection: Fixed meta-interpreter and module syntax CLMs.
• chapter_06_lambda: Fixed beta reduction and Church numerals parsers.

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Testing

Note: All commands below should be run from the project root (MCard_TDD/).

Unit Tests

# Python
uv run pytest -q                 # Run all tests
uv run pytest -q -m "not slow"   # Fast tests only
uv run pytest -m "not network"   # Skip LLM/Ollama tests

# JavaScript
npm --prefix mcard-js test

CLM Verification

Both Python and JavaScript CLM runners support three modes: all, directory, and single file.

Python

# Run all CLMs
uv run python scripts/run_clms.py

# Run by directory
uv run python scripts/run_clms.py chapters/chapter_01_arithmetic
uv run python scripts/run_clms.py chapters/chapter_08_P2P

# Run single file
uv run python scripts/run_clms.py chapters/chapter_01_arithmetic/addition.yaml

# Run with custom context
uv run python scripts/run_clms.py chapters/chapter_08_P2P/generic_session.yaml \
    --context '{"sessionId": "my-session"}'

JavaScript

# Run all CLMs
npm --prefix mcard-js run clm:all

# Run by directory/filter
npm --prefix mcard-js run clm:all -- chapter_01_arithmetic
npm --prefix mcard-js run clm:all -- chapters/chapter_08_P2P

# Run single file
npm --prefix mcard-js run demo:clm -- chapters/chapter_01_arithmetic/addition_js.yaml

Chapter Directories

Directory	Description
chapter_00_prologue	Lambda calculus and Church encoding
chapter_01_arithmetic	Arithmetic operations (Python, JS, Lean) — 27 CLMs
chapter_03_llm	LLM integration (requires Ollama)
chapter_04_load_dir	Filesystem and collection loading
chapter_05_reflection	Meta-programming and recursive CLMs
chapter_06_lambda	Lambda calculus runtime
chapter_07_network	HTTP requests, MCard sync, network I/O — 5 CLMs
chapter_08_P2P	P2P networking and WebRTC — 16 CLMs (3 VCard)

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Contributing

1. Fork the repository and create a feature branch.
2. Run the tests (uv run pytest, npm test in mcard-js).
3. Submit a pull request describing your change and tests.

We follow the BMAD (Red/Green/Refactor) loop – see BMAD_GUIDE.md.

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License

This project is licensed under the MIT License – see LICENSE.

For release notes, check CHANGELOG.md.