Show HN: TinyFn – Your agent sucks at math(tinyfn.io)
3 pointsby tacoooooooo3 hours ago1 comment
  • jqpabc1233 hours ago
    LLMs hallucinate math, fumble conversions, and make up validations.

    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.

    • tacoooooooo3 hours ago
      Humans are also bad at math! So we created calculators. LLMs should have the same access to tools to give them the best opportunity to succeed
      • jqpabc1232 hours ago
        LLMs have access to the same tools --- they run on a computer.

        The problem here is the basic implementation of LLMs. It is non-deterministic (i.e. probabilistic) which makes it inherently inadequate and unreliable for *a lot* of what people have come to expect from a computer.

        You can try to obscure the problem but you can't totally eliminate it without redesigning LLMs. At this time, the only real cure is to verify everything ---which nullifies a lot of the incentive to use LLMs in the first place.

        • dragonwriteran hour ago
          > LLMs have access to the same tools --- they run on a computer.

          That doesn't give them access to anything. Tool access is provided either by the harness that runs the model or by downstream software, if it is provided at all, either to specific tools or to common standard interfaces like MCP that allow the user to provide tool definitions for tools external to the harness. Otherwise LLMs have no tools at all.

          > The problem here is the basic implementation of LLMs. It is non-deterministic (i.e. probabilistic) which makes it inherently inadequate and unreliable for a lot of what people have come to expect from a computer.

          LLMs, run with the usual software, are deterministic [ignoring hardware errors ans cosmic ray bit flips, which if considered make all software non-deterministic] (having only pseudorandomness if non-zero temperature is used) but hard to predict, though because implementations can allow interference from separate queries processed in a batch, and the end user doesn't know what other typical hosted models are non-deterministic when considered from the perspective of the known input being only what is sent by one user.

          But your problem is probably actually that the result of untested combinations of configuration and input are not analytically predictable because of complexity, not that they are non-deterministic.

        • tacooooooooan hour ago
          LLMs absolutely do not have access to the same tools unless they're explicitly given access to them. Running on a computer means nothing.

          It sounds like you don't like LLMs! In that case, you may be more interested in our REST Api. All the same functions, but designed for edge computing, where dependency bloat is a real issue https://tinyfn.io/edge