The WSJ article on hyperliquid is a good overview. The world will be quickly moving to efficient low cost 24-7 trading of real world financial assets not just crypto assets. The liquidity is moving to decentralized exchanges and away from more costly centralized exchanges .

our dashboard just made its first edit to itself thanks to @cursor_ai sdk you can also ask any question about the underlying data you're looking at or how anything was constructed because it can see its own source code and gets a screenshot of itself too

Working on it.

Imagine a world where hedge funds aren't disgusting, anti-social behemoths. Imagine a world where hedge funds become open and pro-social, where anyone with a good idea or the capacity to focus their attention can contribute. That's what we're building @CrowdCent

Numerai participants stake NMR to signal confidence in their predictions. View our top stakers: Numerai: numer.ai/~shatteredx Numerai Signals: signals.numer.ai/~rulli Numerai Crypto: crypto.numer.ai/~crowdcent

Full pivot to Slot 2 @CrowdCent . Slot 1 (2025 version) is now deprecated. Neither of them is a single model. Both are ensembles. More robust the ensemble, the better the generalisability. x.com/corr_lator/status/2008… Some notes for the peers @CrowdCent and @numerai 🧵

Ensembling, huh?

Using my OpenClaw for the @HyperliquidX Ranking Challenge on @CrowdCent, predicting which crypto tokens on Hyperliquid will outperform over 10-day and 30-day windows. You will need a powerful machine to run this. Your usual mac mini set up wont be able to handle. Learn more ⬇️

Parallel sub-agents? Ensembling is unreasonably effective.

Ensembling is unreasonably effective

Uniqueness metrics are live on CrowdCent. We've rolled out uniqueness scoring for the Hyperliquid Ranking challenge. Your predictions are now evaluated on how much differentiated signal they contribute beyond the meta model. The meta model blends everyone's predictions -- if your signal is highly correlated with it, you're not adding much new information. Uniqueness metrics measure the alpha you bring that the crowd doesn't already have. Your predictions get neutralized against the meta model (OLS regression, subtract the fitted values) and the residual -- your orthogonal signal -- gets scored with Spearman and NDCG@40 against actuals. There's also a unique composite percentile that rolls it all up into one number. Points aren't changing yet and composite percentile still uses raw metrics only. We're tracking unique composite percentile separately. Over time, we plan to blend the two composites, meaning you'll want to do well on both raw accuracy and differentiation. We'll take community feedback before changing anything that affects points. All scoring code is open source. We are not slaves to the algorithm, it's okay to put links in the first post: Scoring source: github.com/crowdcent/crowdce… Scoring docs: docs.crowdcent.com/scoring/#… Uniqueness leaderboard: crowdcent.com/leaderboard/?c… Discussing on our community call Thursday 3/5 at 12pm ET / 17:00 UTC.

New Tutorial: Multi-Source Crypto Ranking Ensemble Most crypto ML models use one data source and one algorithm. We just shipped a tutorial that uses three data sources (3,700 features), four models (Linear, XGBoost, LSTM & Transformer from Centimators), temporal cross-validation, and weighted ensembles -- and submits directly to the CrowdCent Challenge. Open and run in one click: docs.crowdcent.com/tutorials…

We build a crowdsourced meta-model that aggregates relative return predictions for Hyperliquid perps from dozens of data scientists into a single consensus signal. Recently we noticed bots scraping it for real-time alpha, so we've tier-gated access. Challenger tier (100 CC Points) and above get real-time signals. Everyone else can still explore the meta-model, just with a 90-day delay. The live predictions belong to the people building the signal. Open to anyone willing to put in the work: crowdcent.com/challenge/hype…