exploit GTO Nick Petrangelo shares insights on GTO Wizard and the evolution of poker solvers. He explains the differences between traditional CFR solvers and newer machine learning approaches, highlighting their strengths and weaknesses. Petrangelo emphasizes the importance of understanding solver limitations and not blindly trusting outputs. Petrangelo explores the future of poker solving, comparing traditional CFR methods with newer machine learning approaches. He stresses caution when using pre-solved tools. The Future of Poker Solving Traditional CFR solvers vs. new machine learning approaches Limitations of pre-solved tools and libraries Importance of understanding solver precision and outputs Potential for errors in solver results and updates Balance between staying current and relying on fundamentals Caution against blindly trusting solver outputs Need for experience to identify and interpret solver anomalies
