In this thread, I will share my thoughts on what caused the depeg.
UST depeg summary: WHY AND WHAT CAUSED UST DEPEG?
Hint is in Anchor protocol and the status of Curve UST pool. @anchor_protocol @terra_money
In this thread, I will share my thoughts on what caused the depeg.
First, UST deposits in Anchor protocol started to exit, which means more circulating UST in the market → sell pressure.
https://t.co/gx9nr7kNcN (05-08-22, 02:30 AM)
https://t.co/bPKhvgfT72 (05-08-22, 02:40 AM)
https://t.co/8Yd056olrL (05-08-22, 03:00 AM)
However, one big problem remains. Liquidity decreased a lot in Curve UST3CRV pool. 3CRV tokens were almost drained in the pool compared to week before.
This means, little UST absorption liquidity for downside.
On May 10th, 2022, around 17:00, another massive withdrawls of UST initiated in anchor protocol. $0.8B withdrawls during one hour.
This generated negative feedback loops where Luna price dropped more drastically.
However, in this time, there weren’t much liquidity left in the curve pool.
Therefore, UST price in the pool also dropped significantly even with a stableswap mechanism.
However, it didn’t take long to congest the on-chain vAMM with high spread fees.
- Don’t ever grow the protocol with un-sustainable yields. This will trigger fear during downside.
- Be ware of the status of Curve pool and take care of the liquidity there.
More from All
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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