Here's another "floor Punk" that's been trading at even lower prices. Market maker (and sharp price maker) @punksOTC bought for 56, and listed today for only 57 ETH...
What's not obvious: 1) how many other Punks are worth ~55-60 ETH in this market 2) are some worth even less, like 45 ETH? 3) why are my valuations still showing the floor values at ~70 when it's obviously lower... [working on it! Details below]
It's almost an axiom, that to know what is happening in a falling market, you look at what listing aren't getting hit. Currently ~20 Punks are listed for 65 ETH or less. Many, I'm sure would also take those prices if offered.
Most of the listings are female Punks, without premier attributes like 3D, Orange Side, Tiara, etc. Although there are fewer male Punks as well.
It's a wrinkle of the market, that the bottom tier/true floor is often a bit over-valued as it's seen as more liquid.
Thus male and female Punks always have the same lower bound. Even though the average male Punk is worth more, or has been for the past few months anyway.
Getting back to the how here... when a piece lists for X -- and especially when it lists and not selling -- it also forms an implicit upper bound on *very similar* pieces.
The question is, how do you define very similar...
That is what I've been up to. Just another attempt to encode common sense that every trader knows in this market.
For example this is the cheapest Orange Side in market, listed for 110 ETH. Which upper bounds does this also imply?
That's the power of exp growth. There are 1465 Punks listed. But only a couple hundred of these listings are "actionable" -- i.e. within 30% of a possible sale.
Spreading the lower bound to near neighbors... greatly increases your coverage
I don't think the 'neighbors approach' is as true for bids as it is for asks... but it may help us there a bit also. Just have to be more conservative in lowering the weight on those bids. An "ask" in the market is a true-er signal, since anyone can hit that button and take it.
Anyway, expect a version of the prices out very soon, with lower and more robust lower bounds.
Right now it's simply not true that the median female Punk is worth 86 ETH. I'm not sure it's 70 ETH tbh.
We are slow to catch-up with the dropping Punk floor, hence we see show a the 57-60 ETH listings as good value. Newest iteration of the model will adjust.. but I do think the top three Punks we show are good value
* Zombie listed 1.1KΞ
* Beanie 285Ξ
* Bandana 60Ξ
The Beanie has green (ask) and orange (bid) dots approaching each other. When this happen, the pieces always sell.
Knowing a lot about applied machine learning, means every day you're
* explaining basic things to other so-called experts (try to be nice)
* have VP types ask you to work for "their team"
* have them ask to send candidates "their way" since you're not available
for all the hype and anti-hype on web3, ML, deep learning etc -- machine logic isn't going anywhere, and there's a strong demand of this skill set. Especially when paired with common sense, product focus, attention to detail and a little patience
if you're young and think you have a mind for these things, I would encourage you to pursue applied machine learning. We will *never* run out of things to do. And people will be grateful to have you. Including those tech VPs -- a lot of whom are pretty cool tbh
I am pro vaccine (see previous posts). You should most likely get it.
This isn't about the freedom of choice issue, or inflatioooon, etc. Just obstinate to think some combination of shots and no more covid.
At a high level there are two viable options: 1) intense system of measures, like China, NZ, Israel for a while -- expensive, intrusive, AFAIK temporary 2) provide a ton of mitigation measure (including but not only vaccines), protect the vulnerable, learn to live with it
This account has become mostly CT, but I still care (deeply) about deep learning and large language models.
While models have gotten bigger and better, it seems this is having surprisingly little effect on downstream applications...
🧵 (1/n)
The growth in parameter counts has been extraordinary. I had a tiny part to play, my friends and team-mates have been on the forefront, in the lab, taking state of the art language models from 300M params to 8B-11B (when I was there), to 1/2 T params
(2/n) developer.nvidia.com/blog/using-dee…
Work from Nvidia. MSFT, OpenAI, Google and FB research, has transformed NLP into a large scale deep learning field. It's amazing you can encode that many params, go through that many documents, in 100+ languages. Even handle everything as bytes...