sui-lang 0.2.0

A programming language optimized for LLM code generation

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
pip install sui-lang

# 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

Interpreter

# Run sample (shows demo)
sui

# Run file
sui examples/fibonacci.sui

# Run with arguments
sui examples/fib_args.sui 15

# Validate
sui --validate examples/fibonacci.sui

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

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

Instr	Format	Description
=	= var value	Assignment
+	+ result a b	Addition
-	- result a b	Subtraction
*	* result a b	Multiplication
/	/ result a b	Division
%	% result a b	Modulo
<	< result a b	Less than (0/1)
>	> result a b	Greater than (0/1)
~	~ result a b	Equality (0/1)
!	! result a	NOT
&	& result a b	AND
|	| result a b	OR
?	? cond label	Conditional jump
@	@ label	Unconditional jump
:	: label	Label definition
#	# id argc {	Function definition start
}	}	Function definition end
$	$ result func args...	Function call
^	^ value	Return
[	[ var size	Array create
]	] result arr idx	Array read
{	{ arr idx value	Array write
.	. value	Output
,	, var	Input
P	P result "func" args...	Python FFI

Variables

Format	Meaning
v0, v1, ...	Local variables
g0, g1, ...	Global variables
a0, a1, ...	Function arguments
g100	argc (command-line argument count)
g101, g102, ...	argv (command-line arguments)

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
├── 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

LLM Weakness	Sui's Solution
Bracket mismatch	Only {} for functions
Long-range dependencies	Each line is independent
Variable name typos	Only sequential numbers (v0, v1...)
Complex nesting	No nesting, decompose to temp variables

vs Assembly

Aspect	Assembly	Sui
Instruction count	Hundreds to thousands	~20
Registers	8-32	Unlimited
Function calls	Complex (ABI)	Simple

vs Python

Aspect	Python	Sui
Token count	High	Low
Syntax complexity	High	Low
Line-by-line errors	Hard	Easy

Roadmap

• Transpiler (Python output)
• Transpiler (Python input, for humans)
• Transpiler (JavaScript output)
• Type annotations (optional)
• LLVM IR output
• WebAssembly output

License

MIT License