Type-safe transformation powered by inference.
ArtificialCast is a lightweight, type-safe casting and transformation utility powered by large language models. It allows seamless conversion between strongly typed objects using only type metadata, JSON schema inference, and prompt-driven reasoning.
Imagine a world where
Convert.ChangeType()could transform entire object graphs, infer missing values, and adapt between unrelated types – without manual mapping or boilerplate.ArtificialCast makes that possible.
Features
- Zero config – Just define your types.
- Bidirectional casting – Cast any type to any other.
- Schema-aware inference – Auto-generates JSON Schema for the target type.
- LLM-powered transformation – Uses AI to “fill in the blanks” between input and output.
- Testable & deterministic-ish – Works beautifully until it doesn’t.
…
As beautiful as it is disgusting, this C# is fully-functional and works exactly as described… and yet you really, really should never use it (which its author will tell you, too).
Casting is the process of transforming a variable of one type into one of another. So for example you might cast the number 3 into a string and get
"3" (though of course this isn’t the only possible result: "00000011" might also be a valid representation, depending on the circumstances1).
Casting between complex types defined by developers is harder and requires some work. Suppose you have a User model with attributes like “username”, “full name”, “hashed password”, “email address” etc., and you want to convert your users into instances of a new model called Customer. Some of the attributes will be the same, some will be absent, and some will be… different (e.g. perhaps a Customer has a “first name” and “last name” instead of a “full name”, and it’s probably implemented wrong to boot).
The correct approach is to implement a way to cast one as the other.
The very-definitely incorrect approach is to have an LLM convert the data for you. And that’s what this library provides.
…
ArtificialCast is a demonstration of what happens when overhyped AI ideas are implemented exactly as proposed – with no shortcuts, no mocking, and no jokes.
It is fully functional. It passes tests. It integrates into modern .NET workflows. And it is fundamentally unsafe.
This project exists because:
- AI-generated “logic” is rapidly being treated as production-ready.
- Investors are funding AI frameworks that operate entirely on structure and prompts.
- Developers deserve to see what happens when you follow that philosophy to its logical conclusion.
ArtificialCast is the result.
It works. Until it doesn’t. And when it doesn’t, it fails in ways that look like success. That’s the danger.
…
I’ve played with AI in code a few times. There are some tasks it’s very good at, like summarising and explaining (when the developer before you didn’t leave a sufficiency of quality comments). There are some tasks it can be okay at, with appropriate framing and support: like knowing its way around unfamiliar-to-you but well-documented APIs2.
But if you ask an AI to implement an entire product or even just a significant feature from scratch, unsupervised, you’re at risk of rapidly hitting the realm of Heisenbugs, security vulnerabilities, and enormous redundancies.
This facetious example – of using AI as a universal typecasting engine – helps hammer that point home, and I love it.
Footnotes
1 How to cast basic types isn’t entirely standardised: PHP infamously casts the string "0" as false when it’s coerced into a
boolean, which virtually no other programming language does, for example.
2 The other week, I had a GenAI help me write some code that writes to a Google Sheets document, because I was fuzzy on the API and knew the AI would pick it up faster than me while I wrote the code “around” it.