Fix the 17x Error: Multi-Agent Systems Scaling Guide

Learn how to avoid the “Bag of Agents” trap and scale Multi-Agent Systems effectively. Based on DeepMind’s research, discover why coordination structure matters more than agent quantity and how to suppress 17x error amplification using functional planes and a centralized orchestrator for robust, performant agentic AI.

Physics-Informed Neural Networks: The Case for Small Architectures

Physics-Informed Neural Networks (PINNs) are often significantly overparameterized in research settings. Senior developer Ahmad Wael critiques this trend, showing that for low-frequency PDEs like Burgers’ equation or hyperelasticity, networks can be reduced by up to 400x without losing accuracy. Learn to build leaner, more efficient ML architectures by starting small.

Data Science as Engineering: Foundations and Identity

Data science is facing an identity crisis. By shifting toward an engineering-first mindset, we move from theoretical “science projects” to robust, maintainable production systems. Learn why treating Data Science as Engineering is essential for modern software development, featuring practical WordPress implementation strategies and senior-level insights.

Inside Cursor Codebase Indexing: The Senior Dev’s Breakdown

Understanding Cursor Codebase Indexing is the key to mastering AI-assisted development. Learn how the RAG pipeline uses AST parsing, Merkle trees for efficient syncing, and vector databases like Turbopuffer to give coding agents deep codebase context while maintaining privacy through client-side path obfuscation. Skip the ‘magic’ and learn the real architecture.