alt Hacker NewsI've been saying this for maybe nine months vis-à-vis my consulting work keeps proving it.
Go is an excellent language for LLM code generation. There exists a large stable training corpus, one way to write it, one build system, one formatter, static typing, CSP concurrency that doesn't have C++ footguns.
The language hasn't had a breaking version in over a decade. There's minimal framework churn. When I advise teams to adopt agentic coding workflows at my consultancy [0], Go delivers highly consistent results via Claude and Codex regularly and more often than working with clients using TypeScript and/or Python.
When LLMs have to navigate Python and TypeScript there is a massive combinatorial space of frameworks, typing approaches, and utility libraries.
Too much optionality in the training distribution. The output is high entropy and doesn't converge. Python only dominated early AI coding because ML researchers write Python and trained on Python first. It was path dependence, not merit.\
The thing nobody wants to say is that the reason serious programmers historically hated Go is exactly why LLMs are great at it: There's a ceiling on abstraction.
Go has many many failings (e.g. it took over a decade to get generics). But LLMs don't care about expressiveness, they care about predictability. Go 1.26 just shipped a completely rewritten go fix built on the analysis framework that does AST-level refactoring automatically. That's huge for agentic coding because it keeps codebases modern without needing the latest language features in training data or wasting tokens looking up new signatures.
I spent four years building production public key infrastructure in Golang before LLMs [1]. After working coding agents like everyone else and domain-switching for clients - I've become more of a Go advocate because the language finally delivers on its promise. Engineers have a harder time complaining about the verbose and boilerplate syntax when an LLM does it correctly every single time.
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> I spent four years building production public key infrastructure in Golang before LLMs
Do you think you might perhaps have a bias in the same way that my 9+ years of Typescript usage and advocacy would cause me to have a bias or a material interest?
There is nothing non-trivial you can make that involves the web that is better with Go than Typescript. I look at your personal page and I see that you're already struggling to manage state and css and navigation, or that those things aren't interesting to you.
This tells me you have limited web experience, just as I have limited experience making build scripts at Google and you would probably find my server-side concurrency fairly crude.
Still, you lump Python and Typescript together as "equally frustrating for LLMs" tells me you are not speaking out of direct experience. But the lumping in of Typescript and Python feels really, empirically wrong to me as someone with a foot in both those worlds.
> When LLMs have to navigate Python and TypeScript there is a massive combinatorial space of frameworks, typing approaches, and utility libraries.
I'm right there with you with Python! Lumping in static and dynamic languages is not correct here. Most Python code is from a fragmented ecosystem that took 10+ years to migrate from 2 to 3 and often there is no indication in the corpus even what major version it is and typing caught on very slowly. That's going to be a major problem for a long time, whereas no recent LLM has never ever ever confused .js for .ts or suddenly started writing Node .v12 and angular into a Node 22 and vue project.
I'm happy to throw down the gauntlet if you ever want to have a friendly go vs typescript vibe-code off that spans a reasonably sophisticated full-stack project over three or four hours of live coding.
If you feel like I'm a mean person and attacking you for wanting proof that Typescript is not at parity or superior to Go in terms of LLM legibility, I still would really like you to consider how you can demonstrate your virtuosity and value judgements best.
This has been studied, Kotlin/C#/Elixir beat it handily.
> Python only dominated early AI coding because ML researchers write Python and trained on Python first. It was path dependence, not merit.
Python doesn’t need dependence to prove its merit. There’s a reason why it is one the major programming languages and was top 1 for a while.
A lot of those pros apply to c# as well. Which claude and gemeni both do very well with.
I agree with most of this. You can learn Go syntax in an afternoon. Idiomatic Go takes a little longer but it just doesn't have the complexity footguns C++ does. But you can shoot yourself in the foot somewhat in Go. I'm talking about buffered channels, which are a mistake most of the time and a premature optimization almost all of the time, and you can explode your program with goroutines. But honestly it's not bad.
Python is an interesting one because it's not always obvious the program is wrong or will fail, thanks to dynamic typing.
But another problem is the Python philosophy since 3.0. Where once backwards compatibility was treated as almost sacrosanct, 3.0+ does not. 2.7 persisted for so long for this reason. But minor releases in 3.x make breaking changes and it's wild to me.
I just wish Go had cooperative async/await rather than channels because (IMHO) cooperative async/await is a vastly superior abstraction to unbuffered channels in particular.
> But LLMs don't care about expressiveness, they care about predictability.
I think this is true, but it misses a very key point. Go does an impressively bad job at designing APIs that are difficult to misuse, so LLMs will misuse them and will require also writing unit tests to walk through it, just to validate it used the libraries correctly. This isn't always possible (or is awkward/cumbersome) for certain scenarios like database querues.
All of the reasons people argue Go is good for LLMs are more true for Rust. You and the LLM can design libraries to be difficult to misuse, and then get instant feedback from the compiler to the LLM about what it did wrong, and often with suggestions about how it should fix them! This also makes RL deriving from compiler feedback more effective.
This allows the LLMs to reason more abstractly at larger scales, since the abstractions are less leaky (unlike in Go). The ceiling on abstraction screws you here, since troubleshooting requires more deep diving. It's the same reason Go projects become difficult for humans at large scales, too.
Most of these reasons apply to Java as much, if not more.
It's an even more popular language with even more training data and also has a better type system so more validation on LLM output, etc.