development

Late to “AI” assisted development?

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Earlier this week, someone asked me if they were perhaps late to making use of AI-assisted development, as they dove into it in the past 2 months (using GitHub Copilot) and are already seeing large gains in a small team in terms of leverage of time. I thought for a second and responded that they might have seen comparably worthwhile gains roughly a year ago. In this post, I’m going to take a look back over the past years to try and figure out what the timeline has actually looked like.

My own vauge memory isn’t very certain, and roughly speaking pre COVID I dont remember much AI being used in software development, and after COVID we were in the AI era? The first place I personally remember using assisted development was via the initial VSCode GitHub Copilot auto completions, which were at the time questionably useful to start with but still showed promise. Included along the way will likely be the first version of Claude Code, Gemini entering the scene, and within GitHub copilot the advancements from completions, to ask & edit, to agent, and finally autopilot and cloud agents.

2017 – 2022: The Transformer era

And although there are other notable mentions, such as BERT by Google in 2018 and CodeBERT in 2020, most of the above comes far before most people will have started looking at or using AI for coding, and that includes me. As I initially started using models during development with the introduction of GitHub Copilot and the autocompletions within VSCode.

GitHub Copilot Technical Preview (June 2021+)

My email innivation to the GitHub Copilot Technical Preview came back in on the 8th July 2021, and it looks like the public announcement on the GitHub blog can still be found dated 29th June 2021.

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mwcli (a MediaWiki focused command line tool targeting developers) over the years

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mcwli includes the third of so generation of “developer environments” that I have made for MediaWiki over the years. You can see the backstory in this earlier post.

Since the early days of 2022, there has been optional metric collection included within the mwcli tool.

This metric collection simply collects what command you run, and when you run it (without any parameters or inputs) so that the data can be aggregated, and the various commands usage can be graphed.

Each command run includes something like:

Commanddocker mediawiki exec
DateTime2025-01-07T12:45:18.213Z
Version0.25.1

I used to have live (ish) graphs in the Wikimedia Superset installation, however, the queries there appear to fail now. So I took some time to export the dataset as a CSV, and shove it around a bit in a Python notebook.

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Wikibase Repository development environment (mwcli)

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This entry is part 4 of 7 in the series Wikibase Entities

Back in 2022, while working at Wikimedia Germany, I ran two sessions with people from the Wikibase Stakeholder Group, focused on Ecosystem Enablement.

These sessions were video recorded and documented in quite a lot of detail, but following through with the videos would probably lead to a bit of a drawn out experience, as they were focused around a workshop setting with participants following along.

  • Session 1, 2022-04-28: Using mwcli, loading extensions, understanding Mediawiki’s general extension mechanism (Video, Overview)
  • Session 2, 2022-05-24: Running your first extension, Wikibase stable interface policy, Mediawiki hooks, building a new API function (Video, Overview)

In this post, I will focus on the core steps required to get a MediaWiki and Wikibase Repository development environment setup in a few minutes with mwcli, and will serve as a basis for some blog posts that I will be writing in the future.

Getting mwcli

If you head to the home page of mwcli, you’ll see a link to an installation guide.

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Autoreload go code on code change

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For developers, the ability to autoreload code upon any changes is nothing short of a game-changer. It not only streamlines the development process but also fosters an environment of continuous improvement and experimentation.

There are so many packages for languages such as JavaScript for such a behaviour, but I struggled to easily find a simple-to-use go version with an example.

And that’s where this post comes in, as a simple to-the-point example extracted from what I learnt while reading https://dev.to/jacobsngoodwin/full-stack-memory-app-01-setup-go-server-with-reload-in-docker-62n

We will be making use of https://github.com/cespare/reflex, which is a go package and small tool to watch a directory and rerun a command when certain files change. 

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Small commits

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There are many blog posts and articles out there about making small git commits. I’m sure most people (including me) bring up the same few topics around why small commits are good and why we should all probably be making smaller commits.

In this post, I’ll look at some of the key topics from my perspective, and try to tie these topics to concrete examples from repositories that I have worked on. The topics are in no particular order, so be sure to give them all a read.

One thing to note is that “small” doesn’t necessarily mean small in terms of lines of code. Small here is also relative. Also, small commits can benefit you in many different places, but to stand the test of time they must end up in your main branch.

Git features during development

Git only takes full responsibility for your data when you commit

Commit Often, Perfect Later, Publish Once: Git Best Practices

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