1/20 #Bitcoin $BTC I created my own risk management calculator some time ago, 10 entries 10 take profits, avg to position sizes. + many advanced things.
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Out of curiosity I dug into how NFT's actually reference the media you're "buying" and my eyebrows are now orbiting the moon
Short version:
The NFT token you bought either points to a URL on the internet, or an IPFS hash. In most circumstances it references an IPFS gateway on the internet run by the startup you bought the NFT from.
Oh, and that URL is not the media. That URL is a JSON metadata file
Here's an example. This artwork is by Beeple and sold via Nifty:
https://t.co/TlJKH8kAew
The NFT token is for this JSON file hosted directly on Nifty's servers:
https://t.co/GQUaCnObvX
THAT file refers to the actual media you just "bought". Which in this case is hosted via a @cloudinary CDN, served by Nifty's servers again.
So if Nifty goes bust, your token is now worthless. It refers to nothing. This can't be changed.
"But you said some use IPFS!"
Let's look at the $65m Beeple, sold by Christies. Fancy.
https://t.co/1G9nCAdetk
That NFT token refers directly to an IPFS hash (https://t.co/QUdtdgtssH). We can take that IPFS hash and fetch the JSON metadata using a public gateway:
https://t.co/CoML7psBhF
Short version:
The NFT token you bought either points to a URL on the internet, or an IPFS hash. In most circumstances it references an IPFS gateway on the internet run by the startup you bought the NFT from.
Oh, and that URL is not the media. That URL is a JSON metadata file
Here's an example. This artwork is by Beeple and sold via Nifty:
https://t.co/TlJKH8kAew
The NFT token is for this JSON file hosted directly on Nifty's servers:
https://t.co/GQUaCnObvX
THAT file refers to the actual media you just "bought". Which in this case is hosted via a @cloudinary CDN, served by Nifty's servers again.
So if Nifty goes bust, your token is now worthless. It refers to nothing. This can't be changed.
"But you said some use IPFS!"
Let's look at the $65m Beeple, sold by Christies. Fancy.
https://t.co/1G9nCAdetk
That NFT token refers directly to an IPFS hash (https://t.co/QUdtdgtssH). We can take that IPFS hash and fetch the JSON metadata using a public gateway:
https://t.co/CoML7psBhF
You may be wondering why @bristoliver rather cryptically RT’d a chart that I posted last night. The answer is not just that he loves quadratic fits on log axes, but that this chart may –and I stress may– hint at a vaccine effect amongst the over 80s THREAD
WARNING: this is a long thread, and it’s a bit of a roller-coaster. We find some apparently strong patterns in the data, and then start to unpick them a bit. So if you start getting excited half way through you might find you’re less excited at the end. But we’ll see…
First we first have to go back a bit. @bristoliver posted a thread a few days ago explaining why, with a constant vaccination rate, a log plot of cases should show a quadratic form. In other words, it should fit an equation like: a + b.x + c.x^2
I meant to link in the model thread there - here it is
the quadratic coefficient – the ‘c’ in that equation – gives an estimate of the % of the population who are being newly protected by the vaccine each day. Please note ‘protected by the vaccine’, not ‘vaccinated’ – as we don't expect 100% protection after the first dose
ok, I lied. but strictly it's not a new graph, just a new trendline (now a quadratic on the log plot). looks um... quite a good fit. so I'd say that was interesting. pic.twitter.com/qkgyMf1ya8
— James Ward (@JamesWard73) January 27, 2021
WARNING: this is a long thread, and it’s a bit of a roller-coaster. We find some apparently strong patterns in the data, and then start to unpick them a bit. So if you start getting excited half way through you might find you’re less excited at the end. But we’ll see…
First we first have to go back a bit. @bristoliver posted a thread a few days ago explaining why, with a constant vaccination rate, a log plot of cases should show a quadratic form. In other words, it should fit an equation like: a + b.x + c.x^2
I meant to link in the model thread there - here it is
Been thinking about where we are, where we might be going, what effect vaccines might have and how to tell. This thread may not happen all at once, and will get a bit mathematical in a couple of places (sorry!), but I will put in pictures. It's yet another argument for log scales
— Oliver Johnson (@BristOliver) January 24, 2021
the quadratic coefficient – the ‘c’ in that equation – gives an estimate of the % of the population who are being newly protected by the vaccine each day. Please note ‘protected by the vaccine’, not ‘vaccinated’ – as we don't expect 100% protection after the first dose
