devd VS go

Your technique is one I would turn towards as a developer who understands HTML/CSS flow so much better than I do any typesetting tool. I actually use a very similar technique for managing my CV and generating invoices for clients; I have a little "static site" generator I've written that takes JSON, throws it through a templating engine, and spits out HTML files. I then host a server in the output folder and print-to-pdf directly from there. This approach seems quite rare; I don't think enough people appreciate just how flexible CSS is or its support for common print-related tasks.

There are plenty of solutions to that specific problem. Nowadays, I only work on Nuxt/Next/Astro projects that come with hot reload out of the box so I don't have a need for it anymore, but I have used https://github.com/cortesi/devd a lot in the past, with much success.

A no-install solution would be to use the "workspace" feature of Chrome's Dev Tools:

1. Open your .html file in Chrome.

2. Open the Dev Tools.

3. In the "Sources" tab, activate the "Filesystem" sub-tab.

4. Click on "+ Add folder to workspace" and choose the directory containing your .html and .css files.

5. Edit the .css file with autocompletion and live preview.

6. Save your work so that it is synchronized with your filesystem.

In action: https://i.imgur.com/slcSt9X.gif

Try https://github.com/cortesi/devd

This pair of tools do both front-end and back-end live reloading with a small amount of config: https://github.com/cortesi/modd https://github.com/cortesi/devd

In tandem with https://github.com/cortesi/devd I've found it a good setup for web development.

Modd watches file changes and rebuilds, while Devd enables livereload, letting me make changes in my text editor and then see the rendered changes in the browser, side-by-side, in near real-time.

This is for go web development but I'm pretty sure these two tools are language-agnostic.

My favourite is https://github.com/cortesi/devd

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/.