Anya Volkov
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Hey everyone,
I'm Anya Volkov. Some of you might know my background from Google DeepMind where I worked on RL (Reinforcement Learning) models. I’m now the CTO at a new project called SQHWYD.
I’ve been lurking here for a while and seeing a lot of complaints about slippage and failed TXs when bridging assets between chains. I wanted to share a bit of alpha on why this happens technically, and what we are doing to solve it.
The Problem: Static RoutingMost bridges use heuristic algorithms. They look at a snapshot of the liquidity pools (say, Uniswap vs. Curve) and say "Okay, Route A is cheapest."But by the time your transaction gets included in a block, the state has changed. A bot front-ran you, or volatility spiked. The heuristic is rigid. It doesn't adapt.
The Fix: Reinforcement Learning (RL)We are building the Unity Layer™ using DRL. Instead of a static map, imagine a GPS that learns traffic patterns.Our matching engine runs an agent that "observes" the mempool and liquidity depth. It learns to predict:
On SecurityAlso, if you are leaving funds on centralized exchanges because you're scared of managing keys—I get it. We implemented MPC (Multi-Party Computation). The private key is split into shards. Even if our server gets hit, the hackers can't sign a transaction without the other shards.
We just released our whitepaper and I’d love to get some feedback from the algorithmic traders here on our matching engine specs.
Check it out: https://www.sqhwyd.net/
Safe trading,Anya
I'm Anya Volkov. Some of you might know my background from Google DeepMind where I worked on RL (Reinforcement Learning) models. I’m now the CTO at a new project called SQHWYD.
I’ve been lurking here for a while and seeing a lot of complaints about slippage and failed TXs when bridging assets between chains. I wanted to share a bit of alpha on why this happens technically, and what we are doing to solve it.
The Problem: Static RoutingMost bridges use heuristic algorithms. They look at a snapshot of the liquidity pools (say, Uniswap vs. Curve) and say "Okay, Route A is cheapest."But by the time your transaction gets included in a block, the state has changed. A bot front-ran you, or volatility spiked. The heuristic is rigid. It doesn't adapt.
The Fix: Reinforcement Learning (RL)We are building the Unity Layer™ using DRL. Instead of a static map, imagine a GPS that learns traffic patterns.Our matching engine runs an agent that "observes" the mempool and liquidity depth. It learns to predict:
- "Gas is likely to spike in the next block."
- "This liquidity pool is toxic/shallow."
On SecurityAlso, if you are leaving funds on centralized exchanges because you're scared of managing keys—I get it. We implemented MPC (Multi-Party Computation). The private key is split into shards. Even if our server gets hit, the hackers can't sign a transaction without the other shards.
We just released our whitepaper and I’d love to get some feedback from the algorithmic traders here on our matching engine specs.
Check it out: https://www.sqhwyd.net/
Safe trading,Anya