postgres-wasm VS go

I used https://wasm.supabase.com/ to make sure some SQL commands I was writing for a blog were correct.

I miss a feature where I can share a link with some data/schema pre-seeded (maybe from a gist?)

We achieve this by mounting a compressed 9P filesystem in the VM. 9P provides a Python script which takes a filesystem folder,renames every file an 8-character name and produces a filesystem.json file representing a nested structure with files, original file names, sizes, etc.

maybe https://wasm.supabase.com tech?

Hey HN, we’re excited about this launch. This was a collaborative effort with the team at Snaplet [0].

postgres-wasm is an embeddable Linux VM with Postgres installed, which runs inside a browser. It provides some neat features: persisting state to browser, restoring from pg_dump, logical replication from a remote database, etc.

The idea was inspired by CrunchyData’s HN post about a month ago [1]. We love the possibilities of Postgres+WASM, and so Supabase & Snaplet teamed up to create an open source version. The linked blog post explains the technical difficulties we encountered, and the architecture decisions we made.

We’re still working hard on this, but it’s at a good “MVP” stage where you can run it yourself. Snaplet are working on a feature where you can drag-and-drop a snapshot into your browser to restore the state from any backup. Supabase are exploring ways we can run the entire Supabase stack inside the browser. You can find the Snaplet repo here [2], and the Supabase fork here [3]. There’s very little difference between these two, we just have a different browser UI.

Both Supabase team and the Snaplet team will be in here commenting if you want to know anything else about the technical details.

[0] Snaplet: [https://www.snaplet.dev/](https://www.snaplet.dev/)

[1] Crunchy post: [https://news.ycombinator.com/item?id=32498435](https://news....

[2] Snaplet repo: [https://github.com/snaplet/postgres-wasm](https://github.com...

[3] Supabase fork: [https://github.com/supabase-community/postgres-wasm](https:/...

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/.