While large model-generated code still faces issues such as syntax errors, messy naming, and context dependency, Japanese data scientist Takato Honda introduced an innovative solution: a new programming language called **Sui** (粋). The name is derived from the traditional Japanese aesthetic "Sui," meaning extreme refinement and elimination of redundancy. The language itself embodies this philosophy: it completely eliminates syntax errors in structure, uses numbers instead of names for variables, and ensures that each line of code is entirely independent. The goal is to achieve "100% accuracy" when large language models (LLMs) generate code.

Sui's design principles directly address the core pain points of current AI programming:

- Zero syntax error rate: the language structure is highly standardized with no ambiguous grammar, ensuring any valid Sui code can be parsed without errors;

- Zero spelling errors: variables do not use identifiers like "userName" or "result," which are prone to errors, but instead use numerical labels (e.g., v0, v1), completely eliminating issues with inconsistent naming;

- Line-level independence: each line is a self-contained instruction, not dependent on context indentation or scope, greatly reducing the risk of logical breaks when LLMs generate code;

- Pure logic language: Sui only handles computational logic and is not tied to UI frameworks; front-end can be combined arbitrarily using React, Vue, Hono.js, or native JavaScript;

- Maximum token efficiency: the syntax is highly compressed, and once mastered by an LLM, its generation efficiency and reliability will far exceed general-purpose languages like Python and JavaScript.

Users can install Sui via PyPI, Homebrew, or source code. Its interactive environment (REPL) is simple to operate, for example:

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However, it is worth noting that according to the latest project updates, **Sui has been marked as "legacy"**, and development focus has shifted to its successor **Isu**—a structured pseudocode. Isu is designed for **deterministic parsing** and **step-level repair**, and can be read and written by LLMs, then converted into a standardized AST called **IIR** (Isu Intermediate Representation), supporting future compilation to backends such as Python, Wasm, and LLVM IR.

Despite this, Sui's concept remains thought-provoking: it does not aim to become a general-purpose programming language, but rather a "middle logical expression layer" tailored for LLMs—ensuring 100% correctness in logic first, then automatically translating it into human-friendly languages. This "AI-first, human-second" design paradigm may be the key direction for the next generation of AI programming infrastructure.

At the critical point where AI is moving from "assisted programming" to "autonomous programming," the attempts of Sui and Isu are not only technical experiments but also a redefinition of the "essence of code": when machines become the main code producers, should we redesign the language for them? Takato Honda's answer lies in that concise ". v1".

Open source address: https://github.com/TakatoHonda/sui-lang