Don’t learn iLang.Your AI should.

iLang is a specification between humans and AI: before the AI acts, it defines what that action is authorized to become; after, code checks whether it was done right. It is not a prompting technique and not a compression format.

If your AI acts on your behalf — edits code, sends messages, spends money, operates websites — you need answers to three questions: what may it do, what counts as done, and who decides when something goes wrong. iLang writes those answers in a form machines can check.

MCP gives AI hands. Prompts tell AI what you want. Guardrails block obviously dangerous content. iLang governs the step in between: for this action, in this situation, should the AI act, confirm first, ask, hand off to a human, or stop. The answer is computed by a fixed function; the same input always gives the same result.

AI is moving from answering questions to acting for people. iLang starts from one principle: whoever sets the rules must follow them first. The model perceives, code decides, and a human decides anything that cannot be undone.

You do not need to learn iLang. Load it into your AI, or call the judgment endpoint before your AI acts. Instructions written in iLang format are already readable by AI.

Three things hold regardless of what any model says about it. The decision function is code: the same vector always gives the same mode. Every symbol of the normative text is checked on each change, and a change that leaves one without a definition does not pass. Every counterexample found is entered in a public register, the unresolved ones included.

Yes. It does not declare whether text “is AI.” It measures how far a piece of writing deviates from a human-written baseline and where: which sentences are boilerplate, where sentence structure is too uniform, where summaries repeat. The conclusion is yours to draw.

Yes. It rewrites the flagged passages one by one to read more like you wrote them: strips boilerplate, breaks up overly uniform sentence patterns, removes redundant summaries. What changed and why is listed for each edit.

Yes. The same method that detects AI-written patterns can remove them. Our detector pinpoints exactly which passages expose AI characteristics; rewriting those passages makes the text undetectable. This is adversarial robustness research, the same discipline as security researchers disclosing vulnerabilities. We publish the principles, not a one-click bypass tool. You are welcome to study the published detection results on your own.

iLang 是人和 AI 之间的一份规格：AI 动手之前，先定下这件事被允许做到什么程度；动手之后，由代码检查它做对了没有。它不是提示词技巧，也不是压缩格式。

如果你的 AI 会替你做事——改代码、发消息、花钱、操作网站——你就要回答三个问题：它可以做什么，做到什么算完成，出了错谁来拍板。iLang 把这三个问题写成机器能检查的格式。

MCP 给 AI 装上手，提示词告诉 AI 你想要什么，护栏挡住明显危险的内容。iLang 管的是中间那一步：这个具体的动作，在这个具体的情况下，是直接做、先确认、先问清楚、交给人，还是停。这个结果由一个固定的函数算出来，同样的输入永远得到同样的结果。

AI 正在从回答问题变成替人做事。iLang 从一条原则出发：定规则的人必须先守规则。模型负责感知，代码负责决定，不可逆的事由人拍板。

不用专门学。把 iLang 加载给你的 AI，或者在它动手之前调用一次判断接口就行。用 iLang 格式写给 AI 的指令，AI 本来就读得懂。

有三件事，不管哪个模型怎么说都成立：决策函数是代码，同样的输入永远得到同样的结果；规范里的每一个符号，每次改动都要过检查；发现的每一个反例都公开登记，还没解决的也照样记。

能。它不判定“是不是 AI 写的”，只告诉你这段文字和人写的基准相比偏了多少、偏在哪里：哪几句是套话、哪里句式过于整齐、哪里在重复总结。结论由你自己下。

能。它按检测出来的那几处逐条改写，让文字更像你自己写的：去掉套话，打散过于整齐的句式，删掉重复的总结。改了哪里、为什么改，都会列出来。

能。检测 AI 味的原理，反过来就是去掉 AI 味的方法。我们的检测能定位到具体哪几处暴露了 AI 特征，改掉这些特征，检测器就认不出来了。这属于对抗鲁棒性研究，和安全研究员公开漏洞是同一个性质。我们公开原理，不做自动改写的工具。各位可以基于公开的检测结果自行研究。