Recursive Reasoning: Why Tiny AI Models Outsmart ChatGPT

Ahmad Wael explores why Recursive Reasoning is the death of “bigger is better” in AI. Discover how a 7M-parameter model outperformed giants like DeepSeek R1 and Gemini 2.5 Pro on the ARC-AGI benchmark by trading massive parameter counts for iterative, cyclic logic. Learn why “depth in time” beats “depth in space.”

Robust Credit Scoring Models with Python: A Pragmatic Guide

Building robust credit scoring models with Python requires more than just training algorithms; it demands a deep understanding of variable relationships. Senior developer Ahmad Wael explains how to use Kruskal-Wallis, Cramer’s V, and Spearman correlation for effective feature selection, dimensionality reduction, and avoiding multicollinearity in your financial risk models.

Scaling Deep Learning: The DenseNet Architecture Guide

Dealing with vanishing gradients in deep networks? Ahmad Wael breaks down the DenseNet architecture, explaining why channel-wise concatenation beats ResNet’s summation. Learn how to implement bottleneck blocks and transition layers in PyTorch for efficient feature reuse and better model performance without the parameter bloat.

Solving the Inversion Error in Safe AI System Design

Current AI development suffers from the ‘Inversion Error,’ building massive symbolic layers on an absent physical base. To create safe AGI, we must implement an enactive floor and state-space reversibility. As developers, we know that building a high-level API without a solid database schema is a recipe for disaster; AI is no different.