there has to be some missing information on how he found the area of water that fully consumed exactly that amount of oil as it simply doesn’t make sense without that. for instance one can spread a teaspoon of oil over 1, 2, n square meters and at some point the oil goes from m later thick to one to less than one.
This is one of those “so cool yet so silly” brainstorms that I’m grateful someone was audacious enough to entertain. I’m both relieved and saddened that it never came to fruition.
There’s a word, chindogu, to describe things that are less than useless. In some sense this project engendered more problems than it solved. Like so many other attractive brainstorms.
Most SO answers were non-answers or word salads. Thankfully one of you added an answer explaining that the cursor is already straight given that it must be visible from the graphics coordinate origin (upper left) and is 45 degrees wide.
> If you just make up a number claim it’s prime and nobody disputes it
You can test if a number is prime in polynomial time, much faster than a sieve. There’s no need to test every divisor to know whether a number is prime or not.
Algos like RSA generate large primes millions of times every day—-there’s nothing to take on faith.
He's probably a *-sulfide or copper-* expert. Or maybe just a physical chemist that the press is ginning up. Actually, the latter. His page doesn't even mention copper or sulfide; and makes only one mention of conductors.
Algorithms do not shun creativity. They are born creatively. But they do set a high bar for new contributions. You either beat the existing complexity or you don't.
If you can improve upon either the space/time complexity or even the comprehensibility/maintainability of algorithms, the industry is your oyster.
This is close to home. Our approach to DevOps for ML Data is to use S3 as the git core and build immutable datasets and models on top of S3 object versioning. I wrote the piece below on "Versioning data and models for faster iteration in ML" earlier this year. The key idea is for every model iteration to be a pure function F(code, environment, data). Ideas welcome: https://medium.com/pytorch/how-to-iterate-faster-in-machine-...
The question one must ask: where were the chances of zoonosis greatest (where did people have the most contact with relevant animals)? and everything else falls into place
Not quite. Abstraction layers like min.io support versioning. More importantly, Quilt manifests only require a "fully qualified physical key" that points to the data. In theory, the manifest can work with any URI: S3, local disk, etc.