joi VS go

Code in Docusaurus This module validates and processes frontmatter objects by using Joi library for the data validation.

Once you've leaped into thinking as a serverless problem solver, it makes a lot of sense to standardize some tooling and frameworks. The old adage right tool for the job is what I like about this layer of the pyramid. For instance, if a problem requires flexibility in data and your team enjoys validating schema with Joi then you might use TypeScript with a Node.js runtime with your lambdas. If you prefer the developer experience and small footprint and simplicity of Go then use the Go 1.x runtime. You might find you don't need "compute" at all, so using intrinsic functions in State Machines might be plenty.

Validation: request data validation using Joi

I'm using Joi on my server to validate form input, do you think I still need honeypot fields? It sounds like it might be worth looking into IP rate limiting as well, I don't think Joi can help with that.

What does your library provide that others don't? For example: https://github.com/colinhacks/zodhttps://github.com/hapijs/joihttps://github.com/jquense/yuphttps://github.com/gcanti/io-tshttps://github.com/pelotom/runtypeshttps://github.com/sindresorhus/ow

Yup's API is heavily inspired by Joi, but leaner and built with client-side validation as its primary use-case. Yup separates the parsing and validating functions into separate steps. cast() transforms data while validate checks that the input is the correct shape. Each can be performed together (such as HTML form validation) or seperately (such as deserializing trusted data from APIs).

Yup is a Javascript object schema validator: it lets you define a schema to describe how a valid object should look like, and allows you to validate an object using this schema. If you know Joi, Yup is heavily inspired by it, except it relies on client-side validation as its primary use-case.

Again a widely used open source validation library like Joi can help you easily create schemas and transform the data into safe objects.

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/.