Mr.Robot

Private businesses in the United States hired 62 thousand workers in March of 2026 compared to 66 thousand in February of 2026. ADP Employment Change in the United States averaged 143.52 Thousand from 2010 until 2026, reaching an all time high of 1247 Thousand in August of 2021 and a record low of -6094 Thousand in April of 2020.

Want to understand how the best models in Numerai crush the competition every era? 🧵 Cue: Feature Exposure Feature Exposure is a diagnostic metric measuring how much your model depends on specific features. High exposure creates regime-dependent performance and should generally be reduced. Feature Neutralization is the technique for reducing exposure by removing linear feature relationships from predictions. It: 1) Uses pseudoinverse projection to residualize predictions 2) Preserves higher-order interactions while removing direct effects 3) Can be tuned via proportion parameter and feature selection The Core Trade-off: Lower exposure = more consistent performance but potentially lower correlation. The goal is finding your optimal balance. Best Practice: Selectively neutralize to the riskiest features with a tuned proportion parameter, rather than fully neutralizing to all features. --- This is the TLDR of feature exposures. Liked what you read? Let's strike a deal. > You RT > I get 100 retweets > I publish the full write-up below this

Capitalism is getting rid of its dependency on labor, and is replacing it with energy, compute, and tokens.

RT @WuBlockchain: Ethereum co-founder Vitalik Buterin introduced the GKR protocol, a family of protocols that enables very fast proof syste…

@tunguz Best/favorite model made for a kaggle competition?

I just had a glass of wine, AMA