ruminations Software Work Writing

LLMs are good coders, useless writers

My writer friends say Large Language Models (LLMs) like ChatGPT and Bard are overhyped and useless. Software developer friends say they’re a valuable tool, so much so that some pay out-of-pocket for ChatGPT Plus. They’re both correct: the writing they spew is pointless at best, pernicious at worst. … and coding with them has become an exciting part of my job as a data analyst.

Here I share a few concrete examples where they’ve shined for me at work and ruminate on why they’re good at coding but of limited use in writing. Compared to the general public, computer programmers are much more convinced of the potential of so-called Generative AI models. Perhaps these examples will help explain that difference.

Example 1: Finding a typo in my code

I was getting a generic error message from running this command, something whose Google results were not helpful. My prompt to Bard:

Bard told me I had a “significant issue”:

Yep! So trivial, but I wasn’t seeing it. It also suggested a styling change and, conveniently, gave me back the fixed code so that I could copy-paste it instead of correcting my typos. Here the LLM was able to work with my unique situation when StackOverflow and web searches were not helping. I like that the LLM can audit my code.

Example 2: Writing a SQL query

Today I started writing a query to check an assumption about my data. I could see that in translating my thoughts directly to code, I was getting long-winded, already on my third CTE (common table expression). There had to be a simpler way. I described my problem to Bard and it delivered.

My prompt:

Bard replied:

Software Work

Stumbling blocks with Azure CLI on the AzureUSGovernment Cloud

This is foremost a note to my future self, a reference for the next time I get stuck. If someone else finds it via a search engine, bonus!

Using the Azure CLI (command line interface) on Microsoft’s Azure Government cloud is mostly like using their regular, non-gov cloud. Cloud computing on Azure has been a positive experience for me overall. But I’ve gotten burned a few times when the gov cloud operation needs a different command than what’s shown in the official Azure CLI docs.

Each case took me several unhappy hours to figure out. The reason I was seeing a certain error message was unrelated to the reasons other people on the internet were served the same message. No one on StackOverflow asks, “might you be using the Azure gov cloud?”

#rstats Data analysis ruminations Software Work

Same Developer, New Stack

I’ve been fortunate to work with and on open-source software this year. That has been the case for most of a decade: I began using R in 2014. I hit a few milestones this summer that got me thinking about my OSS journey.

I became a committer on the Apache Superset project. I’ve written previously about deploying Superset at work as the City of Ann Arbor’s data visualization platform. The codebase (Python and JavaScript) was totally new to me but I’ve been active in the community and helped update documentation.

Those contributions were sufficient to get me voted in as a committer on the project. It’s a nice recognition and vote of confidence but more importantly gives me tools to have a greater impact. And I’m taking baby steps toward learning Superset’s backend. Yesterday I made my first contribution to the codebase, fixing a small bug just in time for the next major release.

Superset has great momentum and a pleasant and involved (and growing!) community. It’s a great piece of software to use daily and I look forward to being a part of the project for the foreseeable future.

I used pyjanitor for the first time today. I had known of pyjanitor‘s existence for years but only from afar. It started off as a Python port of my janitor R package, then grew to encompass other functionality. My janitor is written for beginners, and that came full circle today as I, a true Python beginner, used pyjanitor to wrangle some data. That was satisfying, though I’m such a Python rookie that I struggled to import the dang package.

Data analysis Local reporting Software Work

Making the Switch to Apache Superset

This is the story of how the City of Ann Arbor adopted Apache Superset as its business intelligence (BI) platform. Superset has been a superior product for both creators and consumers of our data dashboards and saves us 94% in costs compared to our prior solution.


As the City of Ann Arbor’s data analyst, I spend a lot of time building charts and dashboards in our business intelligence / data visualization platform. When I started the job in 2021, we were halfway through a contract and I used that existing software as I completed my initial data reporting projects.

After using it for a year, I was feeling its pain points. Building dashboards was a cumbersome and finicky process and my customers wanted more flexible and aesthetically-pleasing results. I began searching for something better.

Being a government entity makes software procurement tricky – we can’t just shop and buy. Our prior BI platform was obtained via a long Request for Proposals (RFP) process. This time I wanted to try out products to make sure they would perform as expected. Will it work with our data warehouse? Can we embed charts in our public-facing webpages?

The desire to try before buying led me to consider open-source options as well as products that we already had access to through existing contracts (i.e., Microsoft Power BI).


AntennaPod: the open-source podcast app

I still like the idea of spotlighting open-source products that deliver a superior experience while operating under a model that benefits users and society. Last month I wrote about gathio, the event planning site. You can find my musings about FOSS (free, open-source software) in that post. This one will be shorter.

The obvious choice for today would be to write about Mastodon, the decentralized open-source alternative to Twitter. I’m active on the server for Washtenaw County and I support the project on Patreon. However, a good look at the project and its features would take more time than I can muster at present.

But I got this post idea from Masto. Someone asked for recommendations for a podcast app. And as I recommended the lovely AntennaPod to yet another person, I realized I could plug it here too.

I’ve been using AntennaPod for almost a decade, since its early days. It was decent even as it was getting built out, but in the past few years it has stabilized as feature-complete and rock solid.

AntennaPod has all the features I could want in a podcast player. It’s easy to use. And it doesn’t track what I listen to or serve me ads. Period.

It’s free to use. If you try to contribute to support the project, you’ll see a slew of non-monetary options. Should you manage to find the small link to donate money, you’ll be deterred by a popup suggesting you oughtn’t:

Classy <3

So I’ll continue contributing my time and money to other open-source projects while being grateful to the folks who keep AntennaPod humming. I highly recommend it as the app to enjoy podcasts without being surveilled and/or advertised to. It’s available only for Android, not iOS.