whatgotdone VS go

I wrote a tool specifically for this, mostly inspired by the Snippets tool at Google. I've been publishing my weekly log in it every week for almost three years:

https://whatgotdone.com/michael/2021-12-03

The code is all open source if you're interested in playing around with it:

https://github.com/mtlynch/whatgotdone

I had the same objection to SQLite, and then I heard about Litestream, and it won me over.[0]

Litestream watches your SQLite database and then streams changes to a cloud storage provider (e.g., S3, Backblaze). You get the performance and simplicity of writing SQLite to the local filesystem, but it's syncing to the cloud. And the cool part is that you don't have to change any of your application code to do it - as far as your app is concerned, it's writing to a local SQLite file.

I wrote a little log uploading utility for my business that uses Litestream, and it's been fantastic.[1] It essentially carries around its data with it, so I can deploy my app to Heroku, blow away the instance and then launch it on fly.io, and it pops up with the exact same data.[2]

I'm currently in the process of rewriting an open-source AppEngine app to use SQLite + Litestream instead of Google Firestore.[2] It's such a relief to get away from all the complexity of GCP and Firestore and get back to simple SQLite.

[0] https://litestream.io/

[1] https://mtlynch.io/litestream/

[2] https://asciinema.org/a/I2HcYheYayeh7aHj23QSY9Vyf/embed?size...

[3] https://github.com/mtlynch/whatgotdone/pull/639

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/.