Ugh, yep!

Though in this case I guess there’s the benefit of engraved numbers providing accessibility.

Company started on Asana, individual teams jumped to Jira, company eventually followed. I was always accidentally creating blank tickets in Asana.

Yeah, if you don’t want the next dev (or your future self) to accidentally undo that corner case you fixed, better put a unit test on it.

Just being forced to talk about how it’s going and what’s blocking can be helpful, so I’m glad you’re questioning for to be more useful, not doing a little rubber-ducking isn’t all bad.

Where does the NYPD keep getting these expensive but apparently useless robots?

Right. I care less about 60% less power, and more about will it randomly connect my phone to my car as my partner drives away instead to the speakers I was already using on the desk next to me.

There’s still a huge racial disparity in tech work forces. For one example, at Google according to their diversity report (page 66), their tech workforce is 4% Black versus 43% White and 50% Asian. Over the past 9 years (since 2014), that’s an increase from 1.5% to 4% for Black tech workers at Google.

There’s also plenty of news and research illuminating bias in trained models, from commercial facial recognition sets trained with >80% White faces to Timnit Gebru being fired from Google’s AI Ethics group for insisting on admitting bias and many more.

I also think it overlooks serious aspects of racial bias to say it’s hard. Certainly, photographic representation of a Black face is going to provide less contrast within the face than for lighter skin. But that’s also ingrained bias. The thing is people (including software engineers) solve tough problems constantly, have to choose which details to focus on, rely on our experiences, and our experience is centered around outselves. Of course racist outcomes and stereotypes are natural, but we can identify the likely harmful outcomes and work to counter them.