What Actually Mattered in AI in 2025
A year-end DataGuy manifesto on why AI in 2025 revealed the limits of scale, context, autonomy, and control – and why systems, not models, defined what actually mattered.
CUDA Without the Marketing: What You Actually Need to Know
This article demystifies CUDA by explaining what it actually abstracts, when GPUs help, when they don’t, and why most GPU waste is architectural, not algorithmic..
CUDA Without the Marketing: What You Actually Need to Know Read More »
Why Statistics and Econometrics Still Win Real Decisions
This article explores why statistics and econometrics continue to outperform black-box models in real decision-making, focusing on causality, interpretability, and durability.
Why Statistics and Econometrics Still Win Real Decisions Read More »
Python Outside Data Science: Finance, Simulation, and Games
This article explores why Python remains central in finance, simulation, and games, showing how it succeeds as a coordination layer rather than a raw performance engine.
Python Outside Data Science: Finance, Simulation, and Games Read More »
Python for Modeling: What Actually Scales Beyond Tutorials
This article examines Python modeling tools through the lens of maintenance and ownership, explaining why many ML stacks collapse and what actually survives in production.
Python for Modeling: What Actually Scales Beyond Tutorials Read More »
Python Packages That Still Matter for Data Analysis
This article examines why certain Python data analysis tools endure long after trends fade, focusing on workflow resilience, interoperability, and analytical realism.
Python Packages That Still Matter for Data Analysis Read More »