2022#10 Readings 🇺🇦🌻
4 minutes read | 726 words by Ruben BerenguelLorem ipsum dolor sit amet
Nothing in particular to mention. This week aside from reading a lot (because my lists were on the rise) I have had some time to tinker with MicroPython on my M5Stack Core2 device. I got it to listen to Bluetooth devices around, and next steps should be fun for a totally useless project I’ve had for… years. Now let’s see if I execute, because the combination of it being useless and the potential to be hard are a bad combination.
🍿 Carrot clarinet
Well, yes. In this short TEDx talk, the speaker builds a clarinet with a carrot, a clarinet mouthpiece, a funnel and a few drill. And it sounds pretty nice. I prefer my sax or xaphoon, but it’s a fun idea.
Shopify’s Data Science & Engineering Foundations
These sound like a solid set of foundations to build data products on.
Staff Engineering at Carta
To be paired with the post above. As Will Larson mentions in his book (I have only read summaries, because I could not stand An Elegant Puzzle and didn’t want to read another book by him), staff engineers come in several different shapes.
Engineering levels at Carta
They seem to be similar-ish to Typeform. They look clear enough until you work there, that’s for sure.
Using Kafka as a message queue
The algorithm described reminds me of two-phase commit, at least in spirit (where Kafka brokers act as the distributed consensus).
Pre-allocated lists in Python
I would argue if you need to go that down the micro-optimisation way, maybe you should not be using Python. On the other hand, I have a project using a device with Micropython where I may need to go there (with the goal of saving CPU cycles & battery more than anything though).
Encouraging meeting participation with a check-in
I did this at one of the first meetings I led (I volunteered to cover for who was going to lead it), the question was about being a coffee or tea person. It took longer than expected (to be fair, we were 30+) but it warmed up everybody.
Make your team miserable with one of these popular project-management anti-patterns
Somehow I feel called out reading this 😩
How to make my Data Engineering department shine again
Given infinite time and resources? 🙄
Use the M1 Mac GPU with Go
In particular, creating shaders with Metal and running the CPU side in Go. It’s pretty straightforward from what I can see in the post.
The Five Conditions for Improvement
This looks obvious… but I wrote them down. I think it is worth to have these in mind when working in team leadership roles.
Yes, You Can Charge More. Artists, hustlers, consultants…
If you do anything where you can set your own prices, you should read this.
How To Do Less
I could have written this, for sure I endorse it. Just say no, often and clearly.
Twin Anxieties of the Engineer/Manager Pendulum
As an engineering manager (although for some reason we don’t call it like that in our Data area 🤷), this is very relevant. There’s always the question “will I find more jobs like this one?”, or “can I go back to doing engineering work?”.
Pure Print-Style Debugging in Haskell
Tutorial-post to bookmark, for me. This is something I have always found painful when I wrote some easy Haskell. I’m kind of “proudly” a print-style debugger (my argument is always that “print works in all languages I code in”), although I occasionally use real debuggers for Scala or Python (or Go) because you can get more mileage out of them.
