datastation VS go

There was a technique that existed already where you could use `go test -cover` and the `-o` flag to produce a binary from `go test` rather than actually running tests. So you could build a binary that had coverage enabled. Then when you ran

Here's an example: https://github.com/multiprocessio/datastation/blob/main/runn....

I can't remember where I found this technique but it's been around for a while.

This new option is the same thing but a way to `go build` with `-cover` instead of `go test -cover -o $out`? Do I have that right?

If my employer doesn't consider paying for dbt cloud, I will use u/eatonphil 's datastation, run the queries on a dev database then put them in dbt.

I build a somewhat similar app, DataStation [0], that is in JavaScript and Go. It supports scripting in Python, Julia, R, JavaScript, Ruby, etc.

The server version of it exists and I run it myself but that process is not documented yet. (Most people use it as a desktop app today.)

[0] https://github.com/multiprocessio/datastation

My biggest issue with Pyodide is the long wait times. I haven't figured out a way around a ~5 second load time where the entire UI hangs every single time you load the page.

My app (similar to Simon's, a lite mode of a data IDE): https://app.datastation.multiprocess.io.

My code: https://github.com/multiprocessio/datastation/blob/main/shar....

I use Go heavily cross-platform developing DataStation [0] and dsq [1]. I am not an expert. And I don't have proof for it but on some rudimentary benchmarks the Linux-specific file idioms in the Go standard library definitely don't seem to translate well to even macOS let alone Windows. For example some good streaming techniques for reading large files on Linux that work really well there seemed to be pretty bad on macOS.

I think Amos has presented more proof than I can on the topic of just how Linux-influenced Go is. And I think it is fine for the majority of Go users because the majority users of Go are building server apps or Linux CLIs.

Amos has spent some time building cross-platform desktop systems with Go for itch.io and I think I'm seeing some of the same things they are in that scenario.

I think this is a reasonable article. If Amos gets flame-y at any point I think it's worth ignoring because there does seem to be something up with Go in cross-platform applications.

I like Go a lot and for most things I'd keep using it still. Just sharing some observations.

[0] https://github.com/multiprocessio/datastation

[1] https://github.com/multiprocessio/dsq

I keep a page of good first projects for two big projects I work on. The only expectation is that you know Go. I've had a couple of people who've never contributed to OSS come in and get some meaningful features merged.

I've got some good first projects if you're interested in OSS data tools and have some Go experience.

Check out: https://github.com/multiprocessio/datastation/blob/main/GOOD...

Some example projects: DataStation (desktop GUI for querying every kind of database, scripting and graphing the results) and dsq (a CLI companion for running SQL queries on many kinds of files), and go-json (a library for fast JSON encoding of arrays of large objects).

I'm building a desktop-first (SaaS-eventual) data IDE for developers [0]. Making a living? Not yet.

It being desktop-first makes it as easy to try out in a corporate environment as Sublime. The data never leaves your machine. Desktop-first is a big deal in devtools for this reason.

[0] https://github.com/multiprocessio/datastation

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/.