esqueleto VS go

Writing Haskell programs that rely on third-party packages is still an issue when it’s a not actively maintained package. They get out of date with the base library (Haskell’s standard library), and you might see yourself in a situation where you need to downgrade to an older version. This is not exclusive to Haskell, but it happens more often than I’d like to assume. However, if you only rely on known well-maintained libraries/frameworks such as Aeson, Squeleto, Yesod, and Parsec, to name a few, it’s unlikely you will face troubles at all, you just need to be more mindful of what you add as a dependency. There’s stackage.org now, a repository that works with Stack, providing a set of packages that are proven to work well together and help us to have reproducible builds in a more manageable way—not the solution for all the cases but it’s good to have it as an option.

However, we can use Esqueleto (”a bare bones, type-safe EDSL for SQL queries”) with Persistent's serialization to write type-safe SQL queries. It’s unlikely that you want to use Persistent by itself with SQL, so let’s use and review them together.

Postgresql-simple is a great library, it makes a nice use of overloaded strings to do the job. Some other nice libraries to keep an eye on are opaleye (postgres specific, which is equally nice but could be a bit difficult to get why the types are so big) and a combination of persistent (not DB specific! can work on postgres, sqlite, but also noSQL DBs like mongo, it's still easy to learn but you lose some things, such as joins due to the power of being agnostic) + esqueleto for type safe joins (be sure to look up the experimental package, it's a more comfortable syntax that will soon become the default one).

Using esqueleto in one of my haskell projects was a huge time sink and a major barrier to entry for colleagues.

In Haskell: https://hackage.haskell.org/package/esqueleto

Either it analyzes the given SQL to determine the in/out types of each SQL query, or it calls the database describe feature at compile-time.

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

When Go first shipped, it was already well-documented that the only stable ABI on some platforms was via dynamic libraries (such as libc) provided by said platforms. Go knowingly and deliberately ignored this on the assumption that they can get away with it. And then this happened:

https://github.com/golang/go/issues/16606

If that's not "getting burned", I don't know what is. "Trying to provide a nice feature" is an excuse, and it can be argued that it is a valid one, but nevertheless they knew that they were using an unstable ABI that could be pulled out from under them at any moment, and decided that it's worth the risk. I don't see what that has to do with "not being as broadly compatible as they had hoped", since it was all known well in advance.