Part of my inspiration to learn to program was that I wanted to blink my capslock key. Did learn to program, did learn Morse, never did that project.
Part of my inspiration to learn to program was that I wanted to blink my capslock key. Did learn to program, did learn Morse, never did that project.
Am vegan. Did not like hot dog. Do not like vegan hot dog.
By the way, miso paste is a remarkable umami ingredient, and Vegetable Kingdom has some great plants-are-actually-delicious recipes.
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.
Company started on Asana, individual teams jumped to Jira, company eventually followed. I was always accidentally creating blank tickets in Asana.