As a stablecoin backed by collateralized debt positions, the outstanding loans within DAI’s economy play an important role with regard to DAI’s ability to maintain its peg.
Why $ust's peg has everything to do with @CurveFinance's #3pool
A thread on $ust's collapse with @dicksonlai_ @GabrielGFoo @themlpx 🧵
@TheSpartanGroup @nansen_ai
#luna
As a stablecoin backed by collateralized debt positions, the outstanding loans within DAI’s economy play an important role with regard to DAI’s ability to maintain its peg.
As a result, this increases DAI’s circulating supply, bringing DAI back to peg.
Vice versa.
Diagram by @dicksonlai_:
It was only after the deployment of the 3pool that we could start to see the volatility of DAI being managed more effectively.
The improvement was significant; DAI was able to consistently maintain its peg within a much tighter margin of deviation.
This is logical, for the deep liquidity that DAI enjoys with USDT and USDC via the 3pool definitely helps
In essence, DAI is directly pegging themselves to USDC and USDT via the 3pool
$frax and $ust are amongst 2 of the largest contributors of TVL to the 3pool, and both have pushed out significant bribes to incentivize deep liquidity in it.
The 3pool must count for something wrt their abilities to keep peg
me and @dicksonlai_ were never able to prove it, until today.
here's a non-exhaustive list by @Defi_Maestro:
- 3pool liquidity dip
- Anchor rates drop
- Usdd 30% yield
- Ust futures from ftx
- Lack of automation for LFG peg protection
By some horrible luck, just as @stablekwon and #TFL were withdrawing 150m worth of $ust from the 3pool to prep for the 4pool, 84m of ust was bridged to eth by the attacker
150 + 100 = 500m worth of LP from the 3pool, which is a staggering ~40% of their 3pool holdings, a v significant sum.
here's a thread by @4484 on what happened next: https://t.co/WU8aDDsSu4
ok here is how the $luna $ust attack was coordinated & executed. \U0001f9f5 (quoted from a friend)
— 4484 (@4484) May 10, 2022
- attacker OTC accumulated $1bn of UST
- borrowed $3bn in $btc
- spread around some fud about peg and bank runs
- dumped the fuck out of their $3bn $btc on market to trigger wider panic
And strike he did; the rest is history.
What can we learn from this?
the fact that this saga has proved me and @dicksonlai_'s hypothesis 2 reinforces that
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Margatha Natarajar murthi - Uthirakosamangai temple near Ramanathapuram,TN
#ArudraDarisanam
Unique Natarajar made of emerlad is abt 6 feet tall.
It is always covered with sandal paste.Only on Thriuvadhirai Star in month Margazhi-Nataraja can be worshipped without sandal paste.
After removing the sandal paste,day long rituals & various abhishekam will be https://t.co/e1Ye8DrNWb day Maragatha Nataraja sannandhi will be closed after anointing the murthi with fresh sandal paste.Maragatha Natarajar is covered with sandal paste throughout the year
as Emerald has scientific property of its molecules getting disturbed when exposed to light/water/sound.This is an ancient Shiva temple considered to be 3000 years old -believed to be where Bhagwan Shiva gave Veda gyaana to Parvati Devi.This temple has some stunning sculptures.
#ArudraDarisanam
Unique Natarajar made of emerlad is abt 6 feet tall.
It is always covered with sandal paste.Only on Thriuvadhirai Star in month Margazhi-Nataraja can be worshipped without sandal paste.
After removing the sandal paste,day long rituals & various abhishekam will be https://t.co/e1Ye8DrNWb day Maragatha Nataraja sannandhi will be closed after anointing the murthi with fresh sandal paste.Maragatha Natarajar is covered with sandal paste throughout the year
as Emerald has scientific property of its molecules getting disturbed when exposed to light/water/sound.This is an ancient Shiva temple considered to be 3000 years old -believed to be where Bhagwan Shiva gave Veda gyaana to Parvati Devi.This temple has some stunning sculptures.
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
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Zuckerberg is a figurehead.
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https://t.co/enzOXDCogV
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LifeLog, via DARPA, terminated on Feb 4th, 2004.
Facebook was launched on Feb 4th, 2004.
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Zuckerberg is a figurehead.
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https://t.co/enzOXDCogV
Project: Lifelog
— Robert Horan (@Robby12692) December 13, 2018
Started by DARPA in 1999, the goal of Lifelog was to create a database on civilians without their knowledge, and track everything they do.
The project "ended" on Feb 4th, 2004.
Facebook began the exact same day.
The CIA funneled tens of millions into Facebook. pic.twitter.com/r7hwF0v9kh
Pentagon Kills LifeLog