firebaseui-web VS go

One great product that can be used for authentication is Clerk but we currently use the default Firebase authentication system as it works pretty well. Firebase even has a separate package called firebaseui-web that helps one set up Firebase authentication without writing any UI code.

In this example, you can see how flexible and customizable the library is. For full documentation, you can see the github repository page here.

Firebase authentication allows for the complete customization of the entire authentication experience for beginners with speed and ease. Nobody sets out to build a bespoke authentication solution unless your business is identity like Auth0. Firebase authentication provides an open-source user interface (UI) library which streamlines building diverse authentication flows for a great user experience. These flows come with the wisdom of years of UX research optimizing the authentication on Google, Youtube, and Android to get you started.

SvelteKit, Serverless, Backendless.com

SvelteKit[1] is a framework for SvelteJS (like Next.js is a framework for ReactJS). I've tried both Svelte and React. Svelte seems to be more elegant and lets me implement my ideas faster with less code. Svelte is very flexible; SvelteKit adds some opinions on how to do things like routing.

SvelteKit also embraces the serverless paradigm[2] (AKA JAMstack[3]). Although a node.js server is still an option, you can also have pages rendered in serverless functions or pre-rendered at build time. Even static pages can be "hydrated" on the client so they are not totally static. So this results in fine-grained SSR (server-side rendering) at the page level. The two main reasons for SSR are performance (especially on mobile devices) and SEO.

Backendless[4] is a VADP/MBaaS. This platform offers a lot of services, but the main one I'm looking at is authentication/identity. I was looking for an authentication service that supports anonymous guest login, social login, as well as traditional email/password login. The other contenders were Google Firebase (slow, and confusing sign in/sign up flow[5]) and AWS Cognito (too complicated/difficult to use). Auth0 was a contender, but they don't support guest logins.

[1]: https://kit.svelte.dev

[2]: https://www.serverless.com

[3]: https://jamstack.org

[4]: https://backendless.com

[5]: https://github.com/firebase/firebaseui-web/issues/665

They provide a web library too - see https://github.com/firebase/firebaseui-web

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/.