The author argues that a technical manager should devote about 80% of their time to coding and only 20% to management, warning that abandoning hands‑on development erodes technical judgment, team trust, and long‑term career competitiveness.
This article explains the distinction between global and local variables in Python, demonstrates how to use the global keyword to modify variables across scopes, provides detailed examples, best‑practice guidelines, common pitfalls, and exercises to help you write cleaner, more maintainable code.
This article outlines the scoring criteria used in interviewing.io mock interviews, highlights five frequent problems candidates face—such as coding too fast, half‑thought ideas, lack of clarifying questions, assuming all rules belong to the interviewer, and not seeking help early—and offers practical advice on communication, design, and practice to improve interview performance.
This article shares practical advice on improving a programmer's design ability, covering coding practice, theory study, hands‑on implementation, revisiting concepts, summarizing lessons, sharing knowledge, and additional tips such as designing before coding and avoiding blind reliance on patterns.
This article presents eight hands‑on project ideas—from cloning Trello and building a user‑management dashboard to creating a native cryptocurrency tracker and a Messenger clone—designed to strengthen your coding skills across frontend, backend, and mobile development while teaching essential tools like React, Redux, Webpack, and APIs.
Copy‑pasting code from Stack Overflow may save time, but retyping each snippet forces deeper understanding, encourages critical evaluation of solutions, and leads to cleaner, more maintainable code, making you a better developer rather than a mere “Stack Overflow‑style” coder.
This article compiles practical advice on learning algorithms, recommending beginner‑friendly books, video courses, coding exercises, and optimization tricks while explaining why certain resources like Sedgewick's Algorithms and Princeton's Coursera lectures can be more approachable than the traditional "Introduction to Algorithms".
