I went to NormConf 2022, but didn't attend the whole thing. It was entirely online as a YouTube livestream for something like 14 hours split into three sessions. It had a very active Slack instance.
I like doing post-conference write-ups because then I have some record of what I was thinking at the time. Sometimes that's useful for other people. Often it's helpful for me.
I'm data engineer adjacent. I work on a data pipeline for crash reporting, but it's a streaming pipeline, entirely bespoke, and doesn't use any/many of the tools in the data engineer toolkit. There's no ML. There's no NLP. I don't have a data large-body-of-water. I'm not using SQL much. I'm not having Python packaging problems. Because of that, I kind of skipped over the data engineer related talks.
The conference was well done. Everyone did a great job. The Slack channels I lurked in were hopping. The way they did questions worked really well.
These are my thoughts on the talks I watched.
All the lightning talks are on YouTube now:
The lightning talks are great (and short!) and worth watching. These were the handful that I watched.
Everything is on fire and you should contribute
This lightning talk was one of the first things I saw of NormConf and encapsulates my work world. "Write your code, do your best."
How to name files
Nice talk covering filenames and the consequences of schemes (or lack of schemes) on what you can do with them.
PyScript: Run Python in your HTML
Intro to pyscript, what it is, and what it can be used for.
Hell is other people's bugs
Talks about the intricacies of writing good A/B tests.
Have you tried rubbing a hash on it?
Talks about hashes, hash functions, etc.
Putting Git's commit hash in
__version__, two ways
All the talks are on YouTube now:
Talks are around 20 minutes each. It's interesting that the playlist is sorted in reverse order from when the talk occurred in the schedule. I think I remember some talks referring to earlier talks, but I don't think it's a big deal. If it is a big deal to you, watch the talks in the playlist in reverse order.
Five semesters of linear algebra and all I do is solve Python dependency problems
This covers Tim's journey following his interests to eventually end up with data science and machine learning. I like learning about peoples' journeys.
NLP tips and tricks
I don't have any of the problems Lynn talks about because I don't do NLP (natural language processin), but it was pretty interesting and fun to listen to.
How small can I get that Docker container?
I work on a set of services all of which are manipulated as Docker images. As such, I've spent time learning how layers work and the consequences of that on image sizes.
I hadn't heard of Dive before--that was neat to learn about.
Geriatric data science: life after senior
I had no idea what this talk was going to be about from the title, so it was a bit of a surprise for me. It talks about what happens after you get promoted beyond "senior engineer", how the role changes, and how companies could do a better job paving the path beyond just like they do with the equivalent management track.
I wish I had known a lot more about this when I was promoted beyond senior engineer. I spent years floundering about trying to understand what was going on particularly around what was "normal" and part of the role.
The takeaways are valuable for anyone who's looking to get promoted beyond senior engineer.
A Game of Construction
I didn't take any notes, but I thoroughly enjoyed the talk and will probably watch it again some time. The puns were delightful.
Hack your way to a better API
I kind of wish it had touched on the consequences for these hacks. How do you maintain the hacks over time? How are the hacks fragile? How can you make them less fragile?
What's the simplest possible thing that might work, and why didn't you try that first?
This talk explores simplicity, problem solving, layers of abstraction, tools, and thinking about problem solving. It's in the domain of ML, but I think it's generally applicable to any kind of problem solving and worth 20 minutes to watch.
Also, the slide into woodworking really clicked with me. I've watched most of the videos he mentions and shows thumbnails of. Last year, I had switched to woodworking with hand tools and have been learning about hand planes and sharpening blades and joints and hand-sawing such. I'm currently making a cabinet for my bathroom using hand tools. It's been educational.
Anyhow, his digression into creating jigs to create simplicity and paving the way for simple choices allowed me to reframe a huge amount of work I've done over the years on libraries, processes, documented conventions, and tooling: making jigs that make future work for me and others simple. Just feeling validated made this talk completely worthwhile to watch.
Also, omg--the slides! I won't say more because I don't want to spoil it.
Building an HTTPS Model API for Cheap: AWS, Docker, and the Normconf API
The talk is about choosing "for cheap" along the axis of preferring "normy software" over new things.
At work, we have a "New Tech Checklist" now which adds a bunch of friction towards picking new things. As someone who has taken over many many projects each of which is a unique snowflake in regards to tech stacks, new things are often a drag and a huge time sink over the long term.
Our "New Tech Checklist" isn't quite "normy software", but it is a helpful way to evaluate options in tech stack choices.
I liked this talk and it's in a friendly digestible framing to send to people who might be new to these ideas.
One of the slides is "There's not enough time." I feel this viscerally.
The zen of tedium
I thought this talk was going to be along the lines of choosing boring technology a la Dan McKinley (my boss' boss). Instead, it's about something something doing things the hard way and how that's not a moral failing or something like that.
I think "the hard way" in this context is "automating" vs. "doing it manually". Maybe this is a talk about premature optimization via automation?
Maybe I missed something in the first part of the talk? I found it confusing. It's definitely not about choosing boring technology. I ended up skipping out about 2/3 of the way through.
I thought about not including this talk in my post-conference summary, but figured I'd include it in case automating vs. manual is interesting to someone.
Data ethics: the non-sexy parts
This talk starts out with a high-level enumeration of general areas of ethics in the world of data. I found that interesting. But then it took a hard-left turn into talking about ethical issues that come up far more often, but aren't discussed in the news. Those are pretty interesting.
It ends with:
"Often the real world is very vague and frogs are slowly getting boiled. Often you don't realize that you're in an ethics situation until you're already in it and it's too late. So I think you need to ask yourself, if you're in a situation, if you're going to look back at what you did how would you judge yourself about that. It's particularly important for early career people who may not have encountered this stuff before and may not have heard anyone talk about it. You senior level people--this is a good thing to bring up to those earlier people."
I'd have written a shorter solution but I didn't have the time
The first part is about how prompts matter and subtractive thinking is hard. This is a segue into MVP and reductive thinking.
This appealed to me. I've spent the large part of the last 6 years deprecating API endpoints, winding down projects, removing features, coalescing options, etc. Also, MVPs, prototypes, project plans, abandoning projects before they start, etc. Also, writing bug reports with minimal viable example, test cases, etc. Seriously, this talk is my jam.
Don't do invisible work
This talk is about tracking your work in some way so that you know what you did later on down the line for evaluations, reviews, promotions, proving your value, etc. It also captures invisible work that isn't amenable to being tracked in easy to discover metrics (commits, bugs fixed, lines of code removed, etc).
This talk also covers how to tell people about what you did which is equally important.