stanstawalend 2026.1.0

A revolutionary natural language Python compiler — blend .sl instructions with Python code.

StanStawaLend 2026

Natural Language Python Compiler — blend .sl instructions with pure Python code and let AI do the translation.

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What is StanStawaLend?

StanStawaLend is a revolutionary programming paradigm that lets you write .sl source files containing a mix of:

• Standard Python code — runs through as-is.
• Natural-language instructions prefixed with ~ — automatically translated to Python by AI.
• Special @commands prefixed with ~ @ — trigger powerful built-in tools (debug, fix, safe, undo, test…).

You write intent. StanStawaLend writes code.

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Installation

pip install stanstawalend

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Quick Start

1. Create a .sl file

# my_program.sl

import random

~ Create a list of 10 random integers between 1 and 100
~ Sort that list in descending order
~ Print only the even numbers from the sorted list

~ @debug
~ @safe

2. Watch mode (live recompile on Ctrl+S)

stanstawalend watch my_program.sl

3. One-shot compile

stanstawalend compile my_program.sl -o output.py

4. Python API

import stanstawalend

# Live watch
stanstawalend.watch("my_program.sl")

# One-shot
stanstawalend.compile_file("my_program.sl", output="output.py")

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How It Works

my_program.sl
│
├─ Pure Python lines ──────────────────────────────► passed through as-is
│
├─ ~ Natural language instruction ─► MD5 Cache? ──► hit: reuse
│                                     └─ Token Cache? ► hit: reuse
│                                         └─ AI API ───► generate & cache
│
└─ ~ @command ─────────────────────────────────────► @command handler
                                                        (debug/fix/safe/…)

Stawa_output.py ──► subprocess run ──► error? ──► Self-Healing (up to 3×)

Incremental Compilation & Cache

Every ~ instruction is hashed (MD5). On the next save, only changed lines are re-translated. Unchanged lines are served from the local cache instantly — zero API calls.

.stanstawalend_cache/
├── line_cache.json      # MD5 hash → Python code
├── token_cache.json     # Instruction text → Python code (Token Saver)
└── history.json         # Undo stack (last 20 versions)

Self-Healing

After every compile, StanStawaLend executes Stawa_output.py in a subprocess. If it crashes, the error + original code is sent back to the AI for automatic repair — up to 3 attempts.

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@Commands Reference

Command	Description
~ @debug	Analyse code and display an optimisation report
~ @fix	Force AI to detect and repair the current code
~ @safe	Sandbox scan for dangerous system calls
~ @undo	Restore the previous compiled version
~ @explain	Plain-language explanation of the program
~ @test	Generate pytest unit tests
~ @run	Execute output and display stdout/stderr
~ @lint	Syntax validation (ast.parse)
~ @optimize	Rewrite code for maximum performance
~ @refactor	Refactor for readability (PEP-8, type hints)
~ @docs	Add Google-style docstrings
~ @types	Add PEP-484 type annotations
~ @vars	List all variables in the generated context
~ @summary	One-paragraph description of the program
~ @complexity	Time and space complexity analysis
~ @translate js	Translate code to another language
~ @benchmark	Inject timeit benchmarking
~ @profile	Run cProfile and display top 20 entries
~ @docker	Generate a Dockerfile
~ @ci	Generate a GitHub Actions workflow
~ @readme	Generate a README.md
~ @requirements	Infer and write requirements.txt
~ @redis	Add Redis caching layer
~ @json_api	Add FastAPI REST layer
…	100 commands total — see commands.py

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Example .sl File

# data_processor.sl

import pandas as pd

~ Load a CSV file named 'sales.csv' into a DataFrame called df
~ Clean the DataFrame: drop rows with null values and reset the index
~ Add a new column 'revenue' = price * quantity
~ Group by 'category' and compute the mean revenue per group
~ Print the top 5 categories by mean revenue

~ @types
~ @docs
~ @test
~ @safe

StanStawaLend will:

1. Generate clean pandas code for each ~ instruction
2. Add type hints (@types)
3. Add docstrings (@docs)
4. Write a Stawa_output_test.py pytest file
5. Scan for dangerous patterns (@safe)

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Architecture

stanstawalend/
├── __init__.py      # Public API: watch(), compile_file()
├── __main__.py      # CLI: stanstawalend watch / compile
├── core.py          # Compilation engine, AI client, Self-Healing
├── watcher.py       # Real-time watchdog file monitor
└── commands.py      # Registry of 100 @commands

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Configuration

StanStawaLend uses the Delfa AI endpoint by default:

https://delfaapiai.vercel.app/ai/copilot?message={prompt}&model=default

You can override it in your code:

from stanstawalend.core import AIClient, StawaCompiler

compiler = StawaCompiler()
compiler.ai = AIClient(endpoint="https://your-custom-endpoint.com/ai")
compiler.compile("myfile.sl")

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Requirements

• Python 3.9+
• watchdog ≥ 3.0
• requests ≥ 2.31
• colorama ≥ 0.4.6
• rich ≥ 13.0

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Author

Stanley Stawa — Creator of StanStawaLend
GitHub: @octaveluka

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License

MIT © 2026 Stanley Stawa