Interesting. I've only dipped my toe in the AI waters but my initial experience with a Go project wasn't good.

I tried out the latest Claude model last weekend. As a test I asked it to identify areas for performance improvement in one of my projects. One of the areas looked significant and truth be told, was an area I expected to see in the list.

I asked it to implement the fix. It was a dozen or so lines and I could see straightaway that it had introduced a race condition. I tested it and sure enough, there was a race condition.

I told it about the problem and it suggested a further fix that didn't solve the race condition at all. In fact, the second fix only tried to hide the problem.

I don't doubt you can use these tools well, but it's far too easy to use them poorly. There are no guard rails. I also believe that they are marketed without any care that they can be used poorly.

Whether Go is a better language for agentic programming or not, I don't know. But it may be to do with what the language is being used for. My example was a desktop GUI application and there'll be far fewer examples of those types of application written in Go.

I don't believe the "corpus" argument that much.

I have been extending the Elm language with Effect semantics (ala ZIO/Rio/Effect-ts) for a new langauge called Eelm (extended-Elm or effectful-elm) and both Haskell (the language that the Elm compiler is written in) and Eelm (the target language, now we some new fancy capabilities) shouldn't have a particularly relevant corpus of code.

Yet, my experiments show that Opus 4.6 is terrific at understanding and authoring both Haskell and Eelm.

Why? I think it stems from the properties of these languages themselves: no mutability makes it reason to think about, fully statically typed, excellent compiler and diagnostics. On top of that the syntax is rather small.

Go’s design philosophy actually aligns with AI’s current limitations very well.

AI has trouble with deep complexity, go is simple by design. With usually only one or two correct paths instruction wise. Architecturally you can design your src however but there’s a pretty well established standard.

One of the things that makes it work so well with agents is two facts. Go is a language that is focused on simplicity and also the gofmt and go coding style makes that almost all go code looks familiar, because everyone write the code with a very consistent style. That two things makes the experience pleasant and the work for the llm easier.

I have had good experience with Go, but I've also had good results with TypeScript. Compile-time checks are very important to getting good results. I don't think the simplicity of the language matters as much as the LLM being generally aware of the language via training data and being able to validate the output via compilation.

Yeah in my experience Claude is significantly better at writing go than other languages I’ve tried (Python, typescript)

I wonder how is the experience writing Rust or Zig with LLMs. I suspect zig might not have enough training data and rust might struggle with compile times and extra context required for borrow checker.

I'm having similarly good results with go and agents. Another good language for it is flutter/dart in my experience.

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