Suppose hypothetically you have an account at a brokerage with some valuable asset in it. You take a margin loan against that asset to fund your normal spending, or pay a tax bill, or maybe buy something at another brokerage.
Torn between "I think losing $100 million when someone beats you at security research is pretty much exactly what you signed up for doing yield farming" and "Maaaaaaaybe not the future of finance you were expecting, huh."
Suppose hypothetically you have an account at a brokerage with some valuable asset in it. You take a margin loan against that asset to fund your normal spending, or pay a tax bill, or maybe buy something at another brokerage.
And you think "Hmm, I have a large equity cushion against this loan."
Which I don't; I just feel like this is why you don't trust a CPU built out of redstone to build reliable financial infrastructure on top of.
Ill-timed liquidations can and do, but attacking someone doing something not-risky to force a liquidation is harder, because of many built in safeguards.
Two, if you regulated financial institution has a goof in your data feeds causes you to mechanically disadvantage retail...
This is not the consensus viewpoint among engineers, who do not have good calibration.
For the treasury auction? Oh heck yes I do. Expect a turf war between the money people and the terrorism people over who gets to lead the investigation.)
More from Patrick McKenzie
I like this heuristic, and have a few which are similar in intent to it:
Hiring efficiency:
How long does it take, measured from initial expression of interest through offer of employment signed, for a typical candidate cold inbounding to the company?
What is the *theoretical minimum* for *any* candidate?
How long does it take, as a developer newly hired at the company:
* To get a fully credentialed machine issued to you
* To get a fully functional development environment on that machine which could push code to production immediately
* To solo ship one material quanta of work
How long does it take, from first idea floated to "It's on the Internet", to create a piece of marketing collateral.
(For bonus points: break down by ambitiousness / form factor.)
How many people have to say yes to do something which is clearly worth doing which costs $5,000 / $15,000 / $250,000 and has never been done before.
Here's how I'd measure the health of any tech company:
— Jeff Atwood (@codinghorror) October 25, 2018
How long, as measured from the inception of idea to the modified software arriving in the user's hands, does it take to roll out a *1 word copy change* in your primary product?
Hiring efficiency:
How long does it take, measured from initial expression of interest through offer of employment signed, for a typical candidate cold inbounding to the company?
What is the *theoretical minimum* for *any* candidate?
How long does it take, as a developer newly hired at the company:
* To get a fully credentialed machine issued to you
* To get a fully functional development environment on that machine which could push code to production immediately
* To solo ship one material quanta of work
How long does it take, from first idea floated to "It's on the Internet", to create a piece of marketing collateral.
(For bonus points: break down by ambitiousness / form factor.)
How many people have to say yes to do something which is clearly worth doing which costs $5,000 / $15,000 / $250,000 and has never been done before.
More from Tech
There has been a lot of discussion about negative emissions technologies (NETs) lately. While we need to be skeptical of assumed planetary-scale engineering and wary of moral hazard, we also need much greater RD&D funding to keep our options open. A quick thread: 1/10
Energy system models love NETs, particularly for very rapid mitigation scenarios like 1.5C (where the alternative is zero global emissions by 2040)! More problematically, they also like tons of NETs in 2C scenarios where NETs are less essential. https://t.co/M3ACyD4cv7 2/10
In model world the math is simple: very rapid mitigation is expensive today, particularly once you get outside the power sector, and technological advancement may make later NETs cheaper than near-term mitigation after a point. 3/10
This is, of course, problematic if the aim is to ensure that particular targets (such as well-below 2C) are met; betting that a "backstop" technology that does not exist today at any meaningful scale will save the day is a hell of a moral hazard. 4/10
Many models go completely overboard with CCS, seeing a future resurgence of coal and a large part of global primary energy occurring with carbon capture. For example, here is what the MESSAGE SSP2-1.9 scenario shows: 5/10
Energy system models love NETs, particularly for very rapid mitigation scenarios like 1.5C (where the alternative is zero global emissions by 2040)! More problematically, they also like tons of NETs in 2C scenarios where NETs are less essential. https://t.co/M3ACyD4cv7 2/10
There is a lot of confusion about carbon budgets and how quickly emissions need to fall to zero to meet various warming targets. To cut through some of this morass, we can use some very simple emission pathways to explore what various targets would entail. 1/11 pic.twitter.com/Kriedtf0Ec
— Zeke Hausfather (@hausfath) September 24, 2020
In model world the math is simple: very rapid mitigation is expensive today, particularly once you get outside the power sector, and technological advancement may make later NETs cheaper than near-term mitigation after a point. 3/10
This is, of course, problematic if the aim is to ensure that particular targets (such as well-below 2C) are met; betting that a "backstop" technology that does not exist today at any meaningful scale will save the day is a hell of a moral hazard. 4/10
Many models go completely overboard with CCS, seeing a future resurgence of coal and a large part of global primary energy occurring with carbon capture. For example, here is what the MESSAGE SSP2-1.9 scenario shows: 5/10

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A brief analysis and comparison of the CSS for Twitter's PWA vs Twitter's legacy desktop website. The difference is dramatic and I'll touch on some reasons why.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x
PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ
The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x

PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ

The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
Ivor Cummins has been wrong (or lying) almost entirely throughout this pandemic and got paid handsomly for it.
He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...
... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:
Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.
Tweet: https://t.co/36FnYnsRT9
Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."
I wonder why...
Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x
Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.
Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq
He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...

... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:
Ivor Cummins BE (Chem) is a former R&D Manager at HP (sourcre: https://t.co/Wbf5scf7gn), turned Content Creator/Podcast Host/YouTube personality. (Call it what you will.)
— Steve (@braidedmanga) November 17, 2020
Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.
Tweet: https://t.co/36FnYnsRT9

Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."
I wonder why...
Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x

Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.
Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq
