Hugo VS go

At one point though I realized there is a scaling problem with my build minutes. I knew that golang has considerably faster builds and in my case the easy fix is swapping over to Hugo.

This blog is running on Hugo. It had previously been running on Jekyll. Both these SSGs ship with the ability to create excerpts from your markdown content in 1 line or thereabouts.

Hugo

Hugo is a popular static site generator specifically designed to create websites and documentation lightning-fast. Its minimalist approach, emphasis on speed, and ease of use have made it popular among developers, technical writers, and anybody looking to construct high-quality websites without the complexity of typical CMS platforms.

As per many other comments, it sounds like a static site generator like Hugo (https://gohugo.io/) or Jekyll (https://jekyllrb.com/), hosted on GitHub Pages (https://pages.github.com/) or GitLab Pages (https://about.gitlab.com/stages-devops-lifecycle/pages/), would be a good match. If you set up GitHub Actions or GitLab CI/CD to do the build and deploy (see e.g. https://gohugo.io/hosting-and-deployment/hosting-on-github/), your normal workflow will simply be to edit markdown and do a git push to make your changes live. There are a number of pre-built themes (e.g. https://themes.gohugo.io/) you can use, and these are realtively straightforward to tweak to your requirements.

Create the technical documentation of your project You can use any of the following options: * A wiki, like the ArchWiki that uses MediaWiki * Read the Docs, used by projects like Setuptools. Check Awesome Read the Docs for more examples. * Create a website * Create a blog, like the documentation of Blowfish, a theme for Hugo.

Doing this made me appreciate existing SSGs like Hugo and Next.js even more👏👏

I suggest hugo: https://gohugo.io/

Generates a completely static website from MD (and other formats) files; also handles themes (including a lot of them rendering well on mobile), and different types of content - posts, articles, etc. - depending on the theme.

It's open source and, being completely static, cheap as fuck to self host.

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/.