Cassidy's blog·36wI made a tree visualizer
A developer created a standalone web application for visualizing tree data structures, originally used for teaching React component hierarchies and concepts like prop drilling and context. The tool features keyboard shortcuts for node manipulation, visual effects like pulsing and drilling arrows, and the ability to save tree diagrams as images for sharing.
Javarevisited·36w25 Google Interview Questions for Software Engineers(with Resources)
A comprehensive collection of 25 technical interview questions commonly asked at Google for software engineering positions, organized into categories including data structures and algorithms, networking and operating systems, software design, coding and programming, and behavioral questions. The post includes specific examples like finding elements in circular sorted arrays, implementing hash tables, designing distributed systems, and solving coding challenges. It also recommends preparation resources including books like 'Introduction to Algorithms' and platforms like ByteByteGo for system design practice.
The Palindrome·38wThe Competitive Programmer's Introduction to Graph Theory
A comprehensive introduction to graph theory fundamentals covering nodes, edges, paths, and cycles. Explains key concepts like connectivity, directed and weighted graphs, trees, node degrees, and graph coloring including bipartite graphs. Includes practical examples with visual illustrations and practice problems to reinforce learning.
The Palindrome·36wRepresenting Graphs
Explores three fundamental ways to represent graphs in computer programs: edge lists, adjacency matrices, and adjacency lists. Covers how each representation handles directed, undirected, and weighted graphs, analyzing their trade-offs in terms of memory usage and operation efficiency. Edge lists excel for algorithms processing edges sequentially, adjacency matrices provide constant-time edge existence checks but require O(n²) space, while adjacency lists offer the most versatile representation for classic graph algorithms.
Anton Zhiyanov·34wGo proposal: Hashers
Go 1.26 introduces the maphash.Hasher interface to standardize hashing and equality checks in custom collections. The proposal includes a ComparableHasher implementation for comparable types and enables developers to create custom hashers for specialized use cases like case-insensitive string comparison. This provides a consistent approach for building hash-based data structures like sets and maps with pluggable hashing behavior.