107 posts

UV

@uv

We build infrastructure to train & deploy financial LLMs, backed by 3 years of real-world agentic trading data.

Los Angeles | Brisbane

Replies Reposts Media Articles

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UV
@uv
Sep 2
Our agents successfully sniped 70% of the Bundle Cat supply. This supply is locked forever, and will be used to make the Bundle Cat market indefinitely. Thank you all for participating! CA on Pons / Robinhood: 0x07EBB29a38Fbcb41563817e5E19f2ceC619C90D2
Justin 🅿️ebis
@justinbebis
Sep 2
Today we're declaring war on farmers, scammers, and bundlers. Mosh is our new Defi protocol built on Pons that fixes lopsided risk/reward between bundlers and traders by pairing every token with an AI market maker. Watch this video to see how it works, and check out the first
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UV
@uv
Sep 4
Join our founder @justinbebis to talk about Mosh and the future of agentic trading.
MCG
@MCGlive
Sep 4
Tomorrow on MCG $BUN | @BundleCatAI w/@justinbebis 📅Friday, 4th September 🕛1:30PM EDT 📍Tune in here Stream powered by @MetaDAOProject
UV
@uv
Aug 26
Our team has been working around the clock to redefine how AI and Finance will intersect. LLMs combined with smart contracts allow us to facilitate financial engineering at an enormous scale. We've developed a novel HFT architecture and brand new harness to facilitate this.
Justin 🅿️ebis
@justinbebis
Aug 26
About to launch the most exciting DeFi primitive I've ever worked on. It has been very rewarding to push my skills to the limit and beyond.
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UV
@uv
Aug 17
We're opening up a fun little slice of our internal benchmarking toolset. Point your agent at it and see how it scores against an expert - it's free and takes minutes!
Justin 🅿️ebis
@justinbebis
Aug 17
Think your Claude, Codex, Hermes, or OpenClaw can manage a hedge fund? Find out for free in 60 seconds on arena.uvlabs.ai 10^60 procedurally generated markets modeled after the '08 financial crisis, Dotcom bubble, and COVID crash. Trade them on demand with one prompt!
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UV
@uv
Jul 14
AI models have a hammer, and they think everything is a nail. Rigorous post-training takes this to the extreme - RLVR allows models to funnel a wide breadth of inputs to high-quality outputs with greater reliability. The cost is a little bit (or a lot) of over-fitting. Model
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