NullAway VS go

Fortunately, Uber made tooling for languages with broken type systems

* https://github.com/uber/NullAway

* https://github.com/uber-go/nilaway

Some patterns arise from language design

* You can't express `T` where `null` is forbidden in the type system so you get NullPointerException everywhere and defensive null checks.

* You express a sum type as a product type because your language does not have sum types .

* Your language doesn't have first class multiple return values (or tuples) so you return extra parameters via out parameters or thread local variables such as `errno`.

* Your language doesn't have exceptions (or algebraic effects) and can't do IO so you have monad transformers.

* Your language doesn't have set-theoretic types so you need hacks like `thiserror` .

* Your language doesn't have stackful coroutines or can't infer async IO for you so you have `async/await` spam or callback hell or "mono's".

* Your language doesn't have exhaustive checks (or pattern matching) so you need a fallthrough case check on switch statements .

* Your language doesn't have algebraic effects, so you need to pass context everywhere.

I know someone will reply about Java's null annotation checking options, so here is one of them: https://github.com/uber/NullAway .

If you must use Java, use Uber's Nullaway which gives null safety via Errorprone.

Does anyone have experience using this at Meta who can compare to https://github.com/uber/NullAway ?

A special kind of validation is enforcing that record fields are not null. (Un)fortunately, records do not have any special behavior regarding nullability. You can use tools like NullAway or Error Prone to prevent null in your code in general, or you can add checks to your records:

What do you think of https://github.com/uber/NullAway

Take a look at NullAway, a plugin for Error Prone.

PMD, Spotbugs, Nullaway: Java linting/static analysis (https://pmd.github.io, https://spotbugs.github.io, https://github.com/uber/NullAway)

I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles

The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.

The Go Programming Language