gonb VS Hugo

I've had these strong feelings and the OP describes it really well. Despite being a polyglot programmer, I really struggle with Python, both in expression and performance (unless it's just config for GPUs).

Some of this frustration was recently an "Unpopular Opinion" on the Go Time Podcast regarding Python being great for "data exploration" but not for "data engineering": https://changelog.com/gotime/304#t=3196

I've been yearning for better interactive tooling and ML-related libraries bridge this gap and started using some even in just the last week:

* GoNB (Golang-support for Jupyter notebooks, also from a Googler) https://github.com/janpfeifer/gonb

* That uses Go-Plotly for graphs/UI: https://github.com/MetalBlueberry/go-plotly

* GoMLX (GoNB author is also on that project, many thanks Jan!) https://github.com/gomlx/gomlx

* Hidden at the end of OP is LangChainGo for LLMs, which I haven't used yet: https://github.com/tmc/langchaingo

Pick those up and let's make the Go community stronger together!

When I created a new Jupyter file in Go, I faced a challenge trying to replicate the development process I usually follow with Python. In Python and Jupyter Notebook I can conveniently run code in separate parts, saving previous values in memory and using cells to organize code. This flexibility was missing in Go, and it took me some time to figure out a solution. However, I came across a helpful tutorial that explained how to use caching with the Go Kernel, making the process smoother with gonb.

I needed one more thing to make myself feel at home, something I usually use with Python. When working with data, I often turned to the Jupyter VSCode extension for its convenience. To my relief, I discovered that a Go kernel existed, tailored perfectly for my needs.

Training library, with some pretty-printing. Including plots for GoNB Jupyter notebook.

Tutorial (and demo) here. Source code in github.com/janpfeifer/gonb.

At one point though I realized there is a scaling problem with my build minutes. I knew that golang has considerably faster builds and in my case the easy fix is swapping over to Hugo.

This blog is running on Hugo. It had previously been running on Jekyll. Both these SSGs ship with the ability to create excerpts from your markdown content in 1 line or thereabouts.

Hugo

Hugo is a popular static site generator specifically designed to create websites and documentation lightning-fast. Its minimalist approach, emphasis on speed, and ease of use have made it popular among developers, technical writers, and anybody looking to construct high-quality websites without the complexity of typical CMS platforms.

As per many other comments, it sounds like a static site generator like Hugo (https://gohugo.io/) or Jekyll (https://jekyllrb.com/), hosted on GitHub Pages (https://pages.github.com/) or GitLab Pages (https://about.gitlab.com/stages-devops-lifecycle/pages/), would be a good match. If you set up GitHub Actions or GitLab CI/CD to do the build and deploy (see e.g. https://gohugo.io/hosting-and-deployment/hosting-on-github/), your normal workflow will simply be to edit markdown and do a git push to make your changes live. There are a number of pre-built themes (e.g. https://themes.gohugo.io/) you can use, and these are realtively straightforward to tweak to your requirements.

Create the technical documentation of your project You can use any of the following options: * A wiki, like the ArchWiki that uses MediaWiki * Read the Docs, used by projects like Setuptools. Check Awesome Read the Docs for more examples. * Create a website * Create a blog, like the documentation of Blowfish, a theme for Hugo.

Doing this made me appreciate existing SSGs like Hugo and Next.js even more👏👏

I suggest hugo: https://gohugo.io/

Generates a completely static website from MD (and other formats) files; also handles themes (including a lot of them rendering well on mobile), and different types of content - posts, articles, etc. - depending on the theme.

It's open source and, being completely static, cheap as fuck to self host.