Show HN: TinyFn – Your agent sucks at math (opens in new tab)

Math is the obvious example, but schema validation falls into the same bucket. If an agent outputs structured JSON, the question "does this conform to the declared schema?" should have exactly one answer. Same schema + same payload → same result, every time, regardless of runtime, language, or retry.

Once you treat that layer as deterministic infrastructure instead of model behavior, a few things get easier:

• retries stop producing inconsistent side effects • downstream systems can trust that validation actually ran • you can audit what passed structural checks independently of the model

Semantic correctness is still a separate problem (models or domain rules are needed there) but offloading the structural layer removes a lot of accidental complexity.

One example of what that deterministic validation layer looks like as a standalone API: https://docs.rapidtools.dev/openapi.yaml

So let's obscure (partially) the inadequacies of LLMs?

This way, we can skip past the obvious and move on to making really big mistakes using LLMs.

While these functions sound useful, the logic seems simple enough that you could just prompt an LLM with the documentation and get a working codebase without actually subscribing to the hosted service.

Where is the commercial value of it?