hugo-site VS go

This month, I took my site squarely into npm-ville when I brought in the npm version of Sass and added PostCSS to make "future" CSS work with current browsers. As it turns out, those changes made my site an unexpectedly appropriate target for the use case that Hugo Installer presents. I’m sure I’ll find nits to pick over time but, for now, I’m impressed by what I’ve seen.

Fortunately, while there are limits to how much you’ll be able to improve your experience with online search in general, you can optimize your own website’s search capabilities. That’s assuming, of course, that your website is built with a static site generator (SSG), as I’ve recommended on my own website over the years, and has search capabilities in the first place. If it lacks search, you can fix that readily enough with the free Pagefind tool about which I wrote earlier this year.

In Line 2 of the partial that I use for the search bar and results, I comment out the line of code that calls to the Pagefind CSS. (I derived it from the Pagefind documentation.) It's this step for which I can't find the corresponding code in your repo, but I'm sure you know where it is; and that's the key to this.

(a.) My own site, https://www.brycewray.com --- currently hosted on Cloudflare Pages, although it's also been on other Jamstack hosts such as Netlify, Vercel, and (briefly) Render.

although I (b.) also sometimes put stuff on dev.to.

Thanks! I will at some point. The code — in its current, very “as-is” state — is in my repo at (as of now) https://github.com/brycewray/hugo_site/blob/main/layouts/partials/webmentions-pipes.html if you can bear its spaghetti-ness. But, assuming you mean you’ll want a walk-through explanation: yes, that’s yet to come. There are some things I need to refine, first.

When I have the code somewhat DRY-er, I’ll write about it. In the meantime, I’ve left the following comment within the webmentions-pipes partial template I’m using to suck all this into each applicable post, just in case the curious happen to find that partial on the site repo:

By Bryce Wray

Having just moved my own site to Astro yesterday after a week or two of experimentation and grunt work, I can offer some opinions which may help you with that question. I’ll go through the “boxes” which I believe any SSG or other website development platform should “check” before you should give it a shot at this task, along with how I judge Astro’s ability to do so in each case.

The description indicates it is not production ready, and is archived at the same time.

If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.

For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).

Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).

There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.

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/.