v8.dev VS go

Semi-related - I saw this trick on https://v8.dev a while back, but `filter: hue-rotate(180deg) invert();` can be a neat CSS trick to 'dark mode' some kinds of graphics. The `hue-rotate` help a bit to keep 'blues blue' etc.

It's far from perfect, but it's a neat trick to be aware of.

V8 is a high-performance JavaScript engine developed by Google. Its purpose is to convert JavaScript code into machine code for execution by the underlying hardware. Primarily developed by the V8 team at Google, the engine focuses on speed and efficiency. In particular, it relies on just-in-time compilation and uses inline caching for high performance.

V8

Exactly the same as JavaScript engines. Be it Mozilla's SpiderMoneky, Google's V8, Apple's Webkit, or Microsoft's Chakra. No matter how specific we draft a specification there is always room for interpretation. Every team has a different take on what part of a spec is describing. Oftentimes it's just a matter of varying pros and cons of different approaches on the road to matching spec; various teams just kind of have to pick a direction and run with it. Other times a spec is not only too vague, but clearly short-sighted to boot.

Remember that we earlier established that every source gets parsed into an AST at some point before it gets compiled or interpreted. For example, platforms like Nodejs and chromium-based browsers use Gooogle's V8 engine behind the scenes to run JavaScript and of course, some AST parsing is always involved before the interpreter kicks in. I looked V8's source and I discovered it uses its own internal parser to achieve this.

While Node.js uses Google's V8 engine that power's Chrome browser, Bun uses JavaScriptCore (JSC), which is an open source JavaScript engine developed by Apple for Safari.

While the idea of server-side JavaScript was not novel, what powered Node.js was! The V8 engine, birthed by Google for Chrome, was now driving Node.js, bringing unparalleled speed and performance to JavaScript execution.

V8 JavaScript Engine

Node runs on the V8 JavaScript engine, which handles memory allocation. The entirety of memory used for a running Node application is held in what is known as a Resident Set. This includes:

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/.