Explores how prohibiting comparisons on Go struct types can reduce binary size by eliminating equality functions and padding.
Explains the limits of function inlining in Go, including the inlining budget and trade-offs between binary size and performance.
Analysis of how JavaScript frameworks impact website performance, referencing Tim Kadlec's detailed research on the topic.
A software engineer argues that effective performance optimization requires understanding system bottlenecks and applying a scientific, evidence-based approach.
A performance investigation into optimizing JavaScript's spread operator, revealing a Chrome-specific speed boost when placing it at the start of an array literal.
Explains how colocating React state can improve app performance by reducing unnecessary re-renders, with code examples.
A guide to React performance optimization, explaining the difference between the render and commit phases and why focusing on slow renders is key.
Discusses the importance and adoption of the Save-Data HTTP header for reducing web page weight, with real-world usage statistics.
A Rails performance expert shares key lessons on why Rails apps become slow and how to improve them, based on extensive teaching and consulting experience.
A technical guide analyzing and improving serialization performance in Django Rest Framework, with benchmarks and practical solutions.
Analyzes when React's useCallback and useMemo hooks actually improve performance versus when they add unnecessary overhead.
Explains a React optimization trick to prevent unnecessary re-renders by passing stable element references.
A critique of modern web bloat and a case for conservative web development principles like minimal JavaScript and optimized assets.
Explains how JavaScript engines optimize prototype property access, covering trade-offs between interpreters and optimizing compilers like V8's Ignition and TurboFan.
Highlights from a deep learning conference covering optimization algorithms' impact on generalization and human-in-the-loop efficiency.
A method for faster generalized linear models on large datasets using a single database query and one Newton-Raphson iteration.
A collection of insightful quotes and laws from engineering and computing, focusing on data-driven decisions, measurement, and avoiding premature optimization.
A review and tips for the OMSCS CS7641 Machine Learning course, covering assignments, exams, and workload.
Explores methods to optimize the gradient descent algorithm in JavaScript, focusing on selecting the right learning rate for convergence.
Explores applying Evolution Strategies (ES) to reinforcement learning problems for finding stable and robust neural network policies.
