Also, Madden is not a master's student anymore. He's a 62 year old doctoral candidate and the lead author on this study.

https://www.nasa.gov/image-detail/amf-art002e000193/

(But also, my conspiratorially-inclined mind is quite entertained by the thought of some sort of parallel construction or tip from a TLA.)

Yes, of course distances are measured starting from 0. But we count discrete things starting at 1. You can do mental gymnastics to enumerate from zero and many programmers are (unfortunately IMO) taught to do so. It's a hard thing to learn that way, so for the folks that have done so, it often becomes a point of pride and a shibboleth.

As a classic example, a four story building has four floors. But you only need to go up three flights to get to the top. You can legitimately call the top floor either 3 or 4, and folks are similarly tribal about their own cultural norms around this one, too.

You don't need to note the commercial licensing option in the license itself; it's irrelevant to that grant. You just state that elsewhere.

Goodness, that doesn't look like a choice to me.

All I can say is that many of "us" live in that tension between high level and low level every day. It's actually going to become more pronounced with `--trim` and the efforts on static compilation in the near term. The fact that Julia can span both is why I'm a part of it.

https://www.youtube.com/watch?v=PixPSNRDNMU

As a simplistic analogy, evolutionary designs of FPGA boards can end up relying upon idiosyncratic properties of the board(s) and create circuits that "shouldn't work" based on an idealized electrical circuit model. And they may not be transferable to other boards. In other words, to "understand" some evolutionary FPGA circuits, you need to "observe" more than just the gate configurations and idealized schematic.

Brains are not FPGAs or even circuits, but I think the analogy holds. They're not _just_ idealized representations of spiking neural networks.

If your work is well-served by existing libraries, great! There's no need to compete against something that's already working well. But that's frequently not the case for modeling, simulation, differential equations, and SciML.