Penpot MCP Server: AI Workflows With Real Design Context

The Penpot MCP server experiment marks a major shift in AI-powered design workflows. By leveraging the Model Context Protocol, Penpot provides a secure, structured bridge for LLMs like Claude to interact with real design data. This senior dev’s take explores why design-expressed-as-code is the only way to eliminate AI hallucinations and technical debt.

Fourier Features: Solving Spectral Bias in Neural Networks

Teaching a neural network to render the Mandelbrot set reveals a fundamental flaw in standard MLPs: spectral bias. By implementing Multi-Scale Gaussian Fourier Features, we can transform blurry approximations into sharp fractal boundaries. Learn why input representation matters more than model depth when dealing with high-frequency data and complex coordinate-based systems.