Metalsmith VS go

Metalsmith — the best customizable SSG

I use Metalsmith. Been happy with it. I build my site into a self-contained nginx docker image.

const Metalsmith = require('metalsmith') const markdown = require('@metalsmith/markdown') const layouts = require('metalsmith-layouts') const permalinks = require('@metalsmith/permalinks') const collections = require('metalsmith-collections') Metalsmith(__dirname) .metadata({ sitename: 'Website Name', description: "Website description.", generator: 'Metalsmith', url: 'https://metalsmith.io/' }) .source('./src') .destination('./build') // .clean(true) .use( collections({ projects: 'pages/*.md', reverse: true, refer: true }) ) .use(markdown()) .use(permalinks()) .use( layouts({ engineOptions: { helpers: { formattedDate: function (date) { return new Date(date).toLocaleDateString() } } } }) ) .build(function (err, files) { if (err) throw err })

I also started to move to hugo, but they didn't merge the pr [2] which would have helped in the transition. :(

The look is still similar to what it was in the beginning, in terms of colors at least.

[1] https://github.com/metalsmith/metalsmith

Metalsmith

A static site generator I've been enjoying lately (and using for my blog) is Metalsmith: https://metalsmith.io/

It feel like it's the best of both worlds, because it's simple to learn and customize, but there are plugins for the things you don't want to spend time writing yourself.

For example, I'm using plugins to: check for broken links, generate an RSS feed, and run a test server with automatic reloading.

But then I was able to easily add in my own code to handle relative links, generate Graphviz diagrams, and format dates.

One other recommendation: I hated almost every template language I ran across (Hugo's, Liquid, Nunjucks, EJS), but I'm thrilled with the simplicity of Handlebars (https://handlebarsjs.com/), although it is a bit limiting and the "block helper with parameters" syntax is strange (perhaps an indicator that I'm trying to do too much in the templating language!).

I believe Metalsmith [1] is trying that approach

[1] https://metalsmith.io/

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/.