UV
83 posts
@uv
We build infrastructure to train & deploy financial LLMs, backed by 3 years of real-world agentic trading data.
I presented @uv at the 0G Apollo demo day. UV is a culmination of nearly 4 years of research and development: 1. A financial harness designed for performance at scale 2. Data & research for training better financial LLMs Give it a watch and tell me what you think!
In a world of slop cannons, companies that sell the highest quality tokens will stand out.
10+ deep-tech AI startups, pitching live to investors. Apollo, our AI accelerator with Stanford veterans, brings its 2026 cohort to Demo Day. Enterprise AI, deepfake detection, AI agents, and more. June 25, 9am PST. Online, open to all. luma.com/8bmbnkqi
Prove it before you trade it. Backtesting → V2. 😎
American traders had access to trillions of dollars in AI upside since the launch of ChatGPT. Most didn't see a penny of it. @uv asks: how can we teach AI to navigate long-horizon macro trades like this? The hard part is - if you showed an AI model one of these charts and told it to trade, it would cheat by mapping data or news to the trading outcome. All of this information is already contained in the model. So to teach AI how to trade long-horizon outcomes, you need to: 1. Enrich these charts with every possible detail 2. Study them to understand their fundamental nature 3. Create new, imaginary charts that are sufficiently realistic for models to learn without cheating. This is how reinforcement learning can help models navigate real markets - backed by tremendous amounts of human labor!
On data markets: A while ago, Anthropic said that they would be spending a billion dollars this year on RL data. This year, that amount will be far exceeded, with good data rarely being turned down for budget concerns. We can expect OpenAI to be of similar mindset, although the window for banal data projects serviced by the likes of Mercor is rumored to be closing entirely this year. Deepmind, Meta, Microsoft, Amazon, and xAI are known to be N-1 labs who may buy datasets already saturated by the likes of Anthropic, or buy RL environments in light of not having a system like Tundra in Anthropic. The TAM is still 10s of billions if not more and the raw aggregate spent on data will only continue to increase. But one must remember what is bought when data is sold, because few today can really differentiate Mercor/Handhshake from a Mechanize/Surge. Data is valuable, to frontier labs, based on how much it can be easily used to improve frontier models. To show this capability, it matters whether teams selling data can show how most directly it can be used to hillclimb models, how much frontier SOTA models struggle on its benchmarks, and how much trouble they can save the frontier lab in its continual acquisition. Data sold is, therefore, very much resembling selling outcomes rather than an actual reusable product, which is why one must obsess about indexing on the scalable means of producing internal systems that can help end model trainers produce outcomes rather than fixating on data itself when evaluating RL environment companies. In this way, the TAM of data markets is actually extremely greenfield and growing, because few teams have the sophistication for research services and scale for on demand consistently QA’ed data. It is the semblance of this product with which Mercor was able to overtake Scale, the semblance of this product which many newer upstarts are painting as an argument to chip away at Mercor/Handshake/Surge’s lunches. From my April's edition of State of Data on substack:
JUST IN: Robinhood is rolling out AI agents that can trade stocks & make credit card purchases on users’ behalf via tools like Claude.
So Jane Street is going public because obviously they see the future where the model labs compete directly with them in the market. The strategic decision is therefore to become a a specialized infrastructure harness for a future frontier model. Tellingly they point out that the latency constraints mean there is no time for inference at the GPU layer, or agentic tool use at the CPU layer, only reflexive heuristics at the FPGA layer. @yminsky is trying to fend off future model lab competition by making Jane Street indispensable to a future AGI. interesting strategy
NEW: Citadel CEO Ken Griffin says AI agents are now automating “extraordinarily high-skilled” finance jobs.
the personal finance product openai launched is actually one of the worst personal finance tools i’ve ever used in my entire life. borderline hazardous for people with low financial literacy to be using i had chatgpt tell me everything it messed up:
And claude kills 9 billion dollar startup Harvey If your not building real infrastructure your next
