Yeah. It doesn’t take much imagination to figure out that a Coke Zero looks identical to regular Coca Cola, yet they have vastly different calorie content, to put it mildly.
Sauces loaded with butter, sugar or other goodies will of course be the same story.
Index of files stored in git pointing to a remote storage. That sounds exactly like git LFS. Is there any significant difference? In particular in terms of backups.
Git LFS is 50k loc, this is 891 loc. There are other differences, but that is the main one.
I don't want a sophisticated backup system. I want one so simple that it disappears into the background.
I want to never fear data loss or my ability to restore with broken tools and a new computer while floating on a raft down a river during a thunder storm. This is what we train for.
Go work at a big company. The patent lawyers come around and ask what you've been working on, and a month or two later, your name's on 10 patents, none of which make any sense whatsoever. If you're very lucky you might get a dollar bill for each.
For a while at google you would get $5k per patent submission and $10k for each approved(?) one. Given how easy it was, I could have matched my annual salary. It's depressing how easy it is to get a system architecture (unimplemented) patented at bigco.
When I was at Microsoft, years ago, it took more effort to avoid having my name end up on a patent than I'd have had to exert if I'd actually wanted one.
You burrow this simple idea in pages and pages of obfuscated tedium, and that's good enough that everyone is happy. Patent office gets their fee, lawyers get paid, company can say it has a supercharged patented innovation.
I was wondering the same thing. I've had to derive unique identifiers from hundreds of different data sets over the years. What makes it special when it's a plane?
The million dollar question is how far can one get on this trick. Maybe this is exactly how our own brains operate? If not, what fundamental building blocks are missing to get there.
> If not, what fundamental building blocks are missing to get there
If I were to guess, the missing building block is the ability to abstract - which is the ability to create a symbol to represent something. Concrete example of abstraction is seen in the axioms of lambda calculus. 1) ability to posit a variable, 2) ability to define a function using said variable, and 3) the ability to apply functions to things. Abstraction arises from a process in the brain which we have not understood yet and could be outside of computation as we know it per [1]
"We used an antimicrotubular agent (parbendazole) and disrupted microtubular dynamics in paramecium to see if microtubules are an integral part of information storage and processing in paramecium’s learning process. We observed that a partial allosteric modulator of GABA (midazolam) could disrupt the learning process in paramecium, but the antimicrotubular agent could not. Therefore, our results suggest that microtubules are probably not vital for the learning behavior in P. caudatum. Consequently, our results call for a further revisitation of the microtubular information processing hypothesis."
Raw protocol, really. No marshaling, no conversions, none of the overhead from type management you get with modern Python, none of the turtles-all-the-way-down dependencies of NodeJS equivalents. I like it, although I would probably port it back to “lightweight” Python in about half the size :)
Some of us still prize compute efficiency, especially those who have been using Python for a long time and are contemplating the new kinds of code patterns that have emerged from data science...
