Sui (粋) - A Programming Language for LLMs

A line-based programming language optimized for accurate LLM code generation

日本語版 README

Overview

Sui (粋) is a programming language named after the Japanese aesthetic concept meaning "refined" and "elimination of excess." It is designed with LLM (Large Language Model) code generation accuracy as the top priority.

Design Principles

1. Line Independence - Each line is completely self-contained
2. Minimal Bracket Matching - Nesting only for function blocks {}
3. Single-Character Instructions - Maximum token efficiency
4. Sequential Variables - No meaningful names needed (v0, v1, g0, a0)
5. Explicit Control Flow - Labels and jumps

Installation

# PyPI (basic)
pip install sui-lang

# PyPI with WebAssembly support
pip install sui-lang[wasm]

# Homebrew (macOS/Linux)
brew tap TakatoHonda/sui
brew install sui-lang

# From source
git clone https://github.com/TakatoHonda/sui-lang.git
cd sui-lang

Quick Start

Interactive Mode (REPL)

# Start REPL
sui

# Example session
>>> = v0 10
>>> + v1 v0 5
>>> . v1
>>>
15
>>> .exit

Commands: .exit / .quit (exit), .reset (reset state)

Interpreter

# Run file
sui examples/fibonacci.sui

# Run with arguments
sui examples/fib_args.sui 15

# Validate
sui --validate examples/fibonacci.sui

# Show help
sui --help

Transpiler (Sui → Python)

# Show converted code
sui2py examples/fibonacci.sui

# Output to file
sui2py examples/fibonacci.sui -o fib.py

# Convert and execute
sui2py examples/fib_args.sui --run 15

Transpiler (Python → Sui) for humans

# Show converted code
py2sui your_code.py

# Output to file
py2sui your_code.py -o output.sui

WebAssembly

# Transpile to WebAssembly Text Format (WAT)
sui2wat examples/fibonacci.sui
sui2wat examples/fibonacci.sui -o fib.wat

# Execute directly via WebAssembly (requires: pip install sui-lang[wasm])
suiwasm examples/fibonacci.sui

Running without Installation (from source)

# Using python directly
python sui.py examples/fibonacci.sui
python sui2py.py examples/fibonacci.sui
python py2sui.py your_code.py

Syntax

Instructions

Variables

Examples

Fibonacci

# 0 1 {
< v0 a0 2
! v1 v0
? v1 1
^ a0
: 1
- v2 a0 1
$ v3 0 v2
- v4 a0 2
$ v5 0 v4
+ v6 v3 v5
^ v6
}
= g0 10
$ g1 0 g0
. g1

Output: 55

Python FFI

; Math functions
P g0 "math.sqrt" 16
. g0

; Random number
P g1 "random.randint" 1 100
. g1

; Type conversion
P g2 "int" "123"
+ g3 g2 1
. g3

Output: 4.0, random number, 124

FizzBuzz

= v0 1
: 0
> v1 v0 100
? v1 9
% v2 v0 15
~ v3 v2 0
? v3 1
% v4 v0 3
~ v5 v4 0
? v5 2
% v6 v0 5
~ v7 v6 0
? v7 3
. v0
@ 4
: 1
. "FizzBuzz"
@ 4
: 2
. "Fizz"
@ 4
: 3
. "Buzz"
@ 4
: 4
+ v0 v0 1
@ 0
: 9

File Structure

sui/
├── README.md           # This file (English)
├── README_ja.md        # Japanese README
├── LICENSE             # MIT License
├── sui.py              # Interpreter
├── sui2py.py           # Sui → Python transpiler
├── sui2wat.py          # Sui → WebAssembly Text Format transpiler
├── suiwasm.py          # WebAssembly runtime (execute via wasmtime)
├── py2sui.py           # Python → Sui transpiler (for humans)
├── examples/
│   ├── fibonacci.sui
│   ├── fib_args.sui
│   ├── fizzbuzz.sui
│   ├── list_sum.sui
│   ├── args_demo.sui
│   └── ffi_demo.sui
└── prompts/
    ├── system_prompt.md  # LLM system prompts
    └── examples.md       # Generation examples

LLM Integration

Sui is designed for LLM code generation. Use the prompts in prompts/ directory:

1. Copy the system prompt from prompts/system_prompt.md
2. Paste it into ChatGPT / Claude / Gemini / etc.
3. Ask to generate Sui code for your task
4. Run with sui your_code.sui

See prompts/examples.md for prompt templates and expected outputs.

Why Sui?

Name Origin

Sui (粋) - A Japanese word meaning "refined," "sophisticated," or "the essence." It represents the aesthetic of eliminating excess and keeping only what is essential.

Avoiding LLM Weaknesses

vs Assembly

vs Python

Roadmap

• Transpiler (Python output)
• Transpiler (Python input, for humans)
• Interactive mode (REPL)
• WebAssembly output (WAT transpiler + runtime)
• Transpiler (JavaScript output)
• Type annotations (optional)
• LLVM IR output

License

MIT License