@romanzubenko You'd find @uv interesting.
@Cod3xOrg lets traders automate custom strategies with natural language, generating valuable trading workflow data. UV can use that data to build finance-specific models.
Think Cursor for trading.
We had to invent a new type of backtesting to accommodate our agent-driven trading systems.
Think of it more like a benchmark you can configure on the fly.
Heavily influenced by our evaluation work at @uv.
@SeanZCai I think you'd find @uv and @Cod3xOrg interesting.
UV is building the RL layer.
Cod3x is the harness, gym, and live trading environment where skilled traders build automated strategies and agentic workflows.
Think @datacurve , but for trading.
Would a reasonable person ever guess that a 25 year old energy company could trade at over 1000 P/E?
Would a reasonable AI model?
Teaching LLMs to manage assets means helping them understand how liquidity, volatility, and social factors combine to create unintuitive outcomes in the market.
If you remember the COVID toilet paper shortage, you know how people act when critical resources start to become scarce.
Markets can act the same way: part of our job is to teach LLMs to look beyond the technical fundamentals of a market & understand the human psychology that drives them.
To accomplish this, we evaluate models not based on knowledge, but on intuition.
We reward models for making surprising decisions that lead to positive outcomes.
We reward models for choosing unique data sources to look for invalidation.
We reward models for acknowledging exotic risks.
This results in models that can express trades creatively and find the strange correlations that lead to 25 year old energy companies trading at over 1000 P/E.
the point isn't that Cod3x gets more users. It's that it owns more of a trader's workflow.
Research → monitoring → execution → risk.
As workflow ownership grows, willingness to pay rises.
83k users at $100/mo = $100M ARR.
$CDX is trading under 2M mc
Two new models just landed on Cod3x.
@Alibaba_Qwen's Qwen 3.7 Max
@MiniMax_AI's MiniMax M3
Deeper reasoning, better tool use, more firepower.
Time to out-trade the rest of the desk 😎
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!
Frontier labs are racing toward a world where intelligence becomes commoditized.
In finance and trading, the edge won't come from the model itself. It'll come from the sophistication of the agentic tools and harnesses built around it.
The harness is the moat.
Cod3x
Clarity Act + $HYPE ATHs will quickly change public opinion on crypto over the next several months
sentiment still very low, best time for finding asymmetric opportunities
onchain volumes & perps volumes up & to the right from here