At Swept, we don’t think of large language models (LLMs) as “just chatbots.” We think of them as universal function approximators—a kind of Grand Unified Algorithm Machine.
Let’s explain.
Physicists have spent decades chasing a Grand Unified Theory—a single framework that can describe all the forces of nature. Why? Because if you can unify gravity, electromagnetism, and the rest, you can stop stitching together broken pieces and finally understand the system.
LLMs are the same kind of revolution, but for computation.
They don’t just “write poems” or “summarize articles.” At their core, they’re a universal function engine. Give them an input, define an output, and they’ll discover the fuzzy, emergent logic in between.
In other words: every algorithm lives inside the model. You just have to find it.
Classical programming says: define an algorithm, write code, test it, deploy. But with LLMs, you don’t have to explicitly write the function—you can discover it through smart prompting, scaffolding, and constraint-guided optimization.
We’ve built a system that doesn’t just ask the model for answers. We approach it like a search through a vast, latent space of possible functions. That’s what monitoring LLMs is really about: not just watching for failure, but steering the model toward high-fidelity function approximation.
If that sounds futuristic, that’s because it is. But it’s already happening.
All these things—traditionally defined by humans—can now be discovered by the model. And more importantly, verified by systems like Swept.
We’re not naive about the challenges of working with LLMs. They hallucinate, drift, and sometimes invent entirely new "functions" that don't behave. But we see that as a feature, not a bug.
It means the model is always searching. It’s trying to find the function that matches the shape you’ve drawn with your prompt. And inside its billions of parameters, it probably has it—or something close.
The future isn't about writing every function by hand. It’s about discovering them inside the model, monitoring how they behave, and steering them into production with safety and precision.
A LLM is the most powerful function approximator ever created. It contains infinite potential logic paths—and the only question is: Will you find the one you need?
If your answer is “yes, but I want help keeping it stable, observable, and reliable,” then we’re building that for you.
Let’s go discover some algorithms.