That’s an odd assertion to make when the full pharmacodynamics and mechanism of action are unknown. Modafinil doesn’t increase your cognitive performance in the way that a substituted phenethylamine would. Focus and attention are certainly related to wakefulness, but wakefulness in and of itself doesn’t increase cognitive performance.
Substituted phenethylamines[1] are derivative compounds of phenethylamine. Many substituted phenethylamines are stimulants, while some are psychedelic too. Dopamine, epinephrine, amphetamine, pseudoephedrine, MDMA, and many others are considered to be substitued phenethylamines.
Modafinil interferes with sleeping less than other drugs - it's only a problem if you rely on the "crash" of running out of energy over the day to fall asleep.
I used Jclouds [1] a few years back to do this. While it's very powerful, it's also fairly complex (abstracts not just storage but also compute/etc). At the time, I remember wanting a simpler abstraction layer over it, and ended up building my own. Maybe something like that exists now, I'm not sure.
The devil is in the details of how each provider offers vendor-specific things and you want to take advantage of them. For example Reduced-Redundancy storage
is iirc an Amazon-specific offering, or if others offer it, it's probably under different SLA terms/measurements. This rapidly breaks many generic abstractions; maybe this is why everyone ends up writing their own little shim layer for their situation.
In some sense it reminds me a bit of building database-connection-pools in the 90's, before they were really standardized everyone rolled their own and learned all the awful lessons about reference counting along the way. Then along came ODBC, then JDBC, and things were so much easier because you only had to deal with one API, and the databases would conform to their side of it. So I think, isn't that what OpenStack (or something?) is supposed to be for cloud services? But whoa, the depth and complexity of these services far exceeds that of a 90's database. It will take a while -- but over time and with patterns of common use well established, a stable base of standard APIs will abstract away most differences, making things so much nicer. I can dream.
The S3 API signature seems to be the defacto abstraction. Network appliance supports it for their (on premisis) hardware. Minio supports it in their open source object storage software. The API signature seems to support competition via tags[1] and other features. Not feeling a lot of lock-in specific to S3.
Often the reason to open source is to get developer mindshare (tensorflow) or in the hope of gaining traction to compete against existing closed source tech (opencompute).
The problem there being that small companies get fooled into thinking they need it and spend a bunch of time abstracting away (and complicating!) a service layer they'll likely never change.
Plenty of cultures attach identities to their societies - Sunni/Shi’a, Hutu/Tutsi, etc. Other cultures don’t focus as much on their “work identity” as you described, but they fill their identities with others.
Actually yes, because FDIC backing would mean they've gone through all of the necessary steps to become a real bank. I would have some confidence that they actually have the USD to back up their holdings as they claim. If still turn out to be a scam then at least I'd have something to fall back on, and some people over at the FDIC would have some questions to answer.
I’ve ridden some Bird scooters in Santa Monica. They’re amazingly fun. But yes, scooters are left at night, especially on the beach bike path when they finally run out of battery. I can’t imagine Bird not sinking a lot of money into gathering those scooters and charging them every night.
Which gave the biggest performance boost? I’d wager #3. 20 seconds sounds pretty absurd unless you’re being really inefficient on your server or are moving around lots of data.
Definitely #3. That's when I went from an amateurish 1s page generation times to a respectable and professional 50ms or so, just by cutting down on the number of SQL queries from hundreds to a few. This also massively cut down CPU usage.
Using materialized views cut down the 50ms page generation to about 10-15ms, by flattening queries into a flat table so there aren't joins.
Using prepared statements cut down 10-15ms page generation to 5ms. Each Postgres query has an entire setup process that involved about 7ms of overhead, and prepared statements cut all that overhead. You won't notice this overhead when your queries are 50-100ms each, but when your queries are only 10ms, it matters.
Getting rid of Gzip in the page generation by serving cached gzip cut down page generation times down to sub-millisecond range for cache hit fragments.
