Explores using Swift's internal @_silgen_name attribute for forward declarations to improve build times, with strong warnings about its risks.
Using simulated annealing to optimize parameters (B and J) in a spatial kinetic Ising model for simulating spatial patterns.
Exploring the challenge of creating a tiny, self-contained C# game under 2KB, focusing on size optimization for faster transfers.
A Java programming challenge to process one billion rows of temperature data, focusing on performance optimization and modern Java features.
A developer revisits an Advent of Code puzzle, optimizing the solution with a new algorithm and implementing it in x86_64 assembly for performance.
A technical analysis and performance critique of TheNativeWeb's video search web app, focusing on Lighthouse metrics and potential optimizations.
A deep dive into the Neural Tangent Kernel (NTK) theory, explaining the math behind why wide neural networks converge during gradient descent training.
An independent web performance consultant explains the value they bring to organizations by focusing teams, sharing cross-client best practices, and driving measurable improvements.
Learn strategies to improve Django website performance by increasing throughput and reducing latency.
An analysis of the world's fastest FizzBuzz implementation, written in Assembler and optimized for AVX2, achieving 56 GB/s output.
Announcing the upcoming O'Reilly book 'Efficient Go,' a guide to writing performant and resource-efficient Go code for everyday development.
A guide to optimizing Vue.js applications for faster loading and better Core Web Vitals scores by reducing bundle size and processing time.
React performance tips: two techniques to optimize re-renders before using memo() or useMemo().
A physics analysis of the optimal angle to jump from a swing for maximum distance, contrasting it with simple projectile motion.
Exploring prefetching heuristics for web performance, covering strategies, trade-offs, and implementation considerations.
Explains Neyman allocation for optimal stratified sampling and its exact integer solution, linking it to US Electoral College apportionment.
Explains Python membership testing using 'in' operator vs. for loops, with benchmarks showing performance differences.
Learn how to use Numba's @jit decorator to achieve massive speedups in Python code with minimal changes, demonstrated with a benchmark example.
A series exploring techniques to write faster Python code, covering idiomatic patterns, performance tools, and benchmarking.
A guide explaining how to use React's useMemo Hook to optimize performance by memoizing expensive computations in function components.
