josh iza.ac

Sometimes when you build software, you shave a yak so deeply that you end up with a surprisingly nice sweater (while the yak, presumably, wonders why it is suddenly rather drafty).

There is a specific, sharp mist that hangs in the air when you slice into a calamansi.

“I didn’t cook dinner last night,” my sister mentioned to me the other week.

My previous blog post, The anxiety of the perfect loaf: the illusion of culinary precision, has been getting a (minor) amount of attention over on Hackernews and Metafilter, and I wanted to take a moment to go through some of the comments, share my though…

Update: This blogpost has been getting a (minor) amount of attention over on Hackernews and Metafilter.

There is a peculiar theater to the internet’s culinary landscape, and if you spend enough time observing it, you will inevitably stumble into a warzone over authenticity.

I enjoy cooking.

Earlier this year, I was lucky enough to give a talk on quantum computing and near-term quantum machine learning at PyCon 2021.

Most autodifferentiation libraries, such as PyTorch, TensorFlow, Autograd — and even PennyLane in the quantum case — allow you to create new functions and register custom gradients that the autodiff framework makes use of during backpropagation.