[Ian Curtis]

Originally Posted by gblake

It's become so trendy that I worry that folks are losing sight of the differences between efficiently operating on small datasets and efficiently operating on large datasets. For small datasets, low-overhead brute force will often beat the pants off of manipulating some fancy data structure that is appropriate for larger datasets. There is more to writing efficient code than learning the big O characteristics of various data structures and algorithms.

"The purpose of computation is insight, not numbers." -- Richard Hamming

Why this matters:

Shortly before the first field test (you realize that no small scale experiment can be done—either you have a critical mass or you do not), a man asked me to check some arithmetic he had done, and I agreed, thinking to fob it off on some subordinate. When I asked what it was, he said, "It is the probability that the test bomb will ignite the whole atmosphere." I decided I would check it myself! The next day when he came for the answers I remarked to him, "The arithmetic was apparently correct but I do not know about the formulas for the capture cross sections for oxygen and nitrogen—after all, there could be no experiments at the needed energy levels." He replied, like a physicist talking to a mathematician, that he wanted me to check the arithmetic not the physics, and left. I said to myself, "What have you done, Hamming, you are involved in risking all of life that is known in the Universe, and you do not know much of an essential part?" I was pacing up and down the corridor when a friend asked me what was bothering me. I told him. His reply was, "Never mind, Hamming, no one will ever blame you."[5]

But that's not the point. It's that frc ultimately fails at demonstrating what [CHOOSE YOUR OWN FIELD] really is.

FRC "fails" to demonstrate what every field truly is -- but its true value is you can get exposure to the essence of almost ANY field.