baseplate.go VS go

We chose to use govips which is a cgo wrapper around the libvips image manipulation library. The majority of new development for services in our backend is written using baseplate.go. But Go is not an ideal choice for media processing as it cannot keep up with the performance of native code. The most widely used image-processing libraries like libmagick are primarily written in C or C++. Speed was a major factor in selecting libvips in order to keep latency low on CDN cache misses for images. In our tests, libvips was 3–4 times faster than libmagick on basic image processing operations. Content-aware smart cropping was implemented by porting smartcrop.js to Go. This is the only operation implemented in pure Go.

Even Reddit has python backends https://github.com/reddit/baseplate.py based on Pyramid. They also have a go one. https://github.com/reddit/baseplate.go

there's a multitude of services in reddit's architecture. as far as i can tell, they mostly using reddit's baseplate framework (which has implementations in both python and go).

FWIW, I believe Reddit has rewritten some Python into Go because of scaling issues. I don't know of any public write ups to confirm it, but at the least there's a public repo that contains their base template.

Gazette Inference Service is a baseplate.go (Reddit’s golang web services framework) thrift service whose single responsibility is serving ML inference requests to it’s clients. It is deployed with Reddit’s modern kubernetes infrastructure.

Baseplate is implemented in Python and Go, and although they share the same main functionality, smaller features differ between the two. One such feature that was previously on the Go implementation but not Python was deadline budget propagation, which passes on the remaining timeout available from the initial client request all the way through the server and any other requests that may follow. The lack of this feature in Baseplate.py meant that many resources were being wasted by servers doing unnecessary work, despite clients no longer awaiting their response due to timeout.

A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.

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/.