Prashant Sharma Official

How AI Is Changing Backend Developer Interviews (With Real Evidence & Examples) Backend developer interviews are undergoing the biggest structural change since the rise of system design rounds. AI has not just accelerated coding—it has  broken traditional interview signals . As a result, companies are redesigning interviews to measure things that AI cannot reliably fake. This article explains  exactly how interviews have changed ,  what proof exists , and  what you will actually be tested on  as a backend developer in 2026. The Core Problem: Traditional Interviews No Longer Predict Skill Before AI: Coding speed mattered Memorized patterns worked Syntax-heavy questions filtered candidates After AI: Code generation is near-instant Boilerplate is meaningless as a signal Many candidates can “solve” problems they don’t understand Hiring managers noticed a sharp rise in false positives —candidates passing interviews but failing on the job. That forced interviews t...

B.Tech CS/IT Roadmap 2025: A Clear 4-Year Plan to Survive AI, Avoid Layoffs & Become Job-Ready If you are a B.Tech 1st-year CS/IT student and you feel confused, anxious, or scared because of AI growth and daily layoff news , let me assure you of one thing: You are not late. You are not behind. You are exactly where you should be. Most students don’t fail because they are incapable. They fail because they don’t have a clear roadmap . This article gives you that roadmap— year by year , without hype, without panic, and without unrealistic expectations. First, Let’s Address the Fear (Read This Carefully) Is AI Replacing Software Engineers? No. AI is replacing: Repetitive coding Boilerplate work Low-skill execution roles AI cannot replace : Strong problem-solving System design thinking Engineering judgment Real-world debugging and architecture AI eliminates weak fundamentals, not strong engineers. What About Layoffs? Layoffs usually happen due to: Over-hiring during boom periods Busine...

Python wins on developer productivity, ML/AI tooling and fast prototyping; Java wins on raw runtime throughput, mature concurrency, and JVM-based deployment options (with growing JVM–Python bridges like GraalPy narrowing the gap). Choose Python when the backend is tightly coupled to ML/AI workflows and speed-to-market matters; choose Java when you need predictable throughput, strict typing, enterprise-grade observability, or to co-locate AI inference with high-throughput transactional services. Below you’ll find a full-length post suitable for publication, with suggested infographic layout, practical code examples, deployment checklist, and a final recommendation matrix. Python vs Java for Backend in the AI Era Intro — why this comparison matters now The rise of large models, model-serving microservices, on-device and on-edge inference, and faster AI frameworks has changed backend design choices. Backends are no longer just HTTP + DB; they often host model loading, feature preprocess...

Must-Know Python Developer Interview Questions and Answers (50 Questions) Python interviews today evaluate far more than syntax. Interviewers assess how well you understand Python’s object model, memory behavior, concurrency limitations, performance trade-offs, and production readiness . This article covers 50 must-know Python interview questions , ranging from fundamentals to advanced internals, with explanations that prepare you for real follow-up questions . Section 1: Core Language Semantics (Q1–Q10) Q1. Is everything in Python an object? Answer: Yes. In Python, everything is an object—including integers, strings, functions, classes, and modules. Each object has: identity ( id ) type ( type ) value This uniform object model enables features like first-class functions and dynamic behavior. Q2. What is the difference between is and == ? Answer: == checks value equality is checks object identity a = [1, 2] b = [1, 2] a == b # True a is b # False Using is for value comparison ...

🐍 Python Development Crash Guide 2026 — Part 6: Job-Ready Blueprint: Projects, Roadmap, Resume & Interview Preparation Learning Python is easy. Getting hired with Python requires direction, proof, and execution . This final part ties everything together : Skills → Projects Projects → Resume Resume → Interviews Interviews → Job offers If someone follows this post seriously, they will not be “just another Python learner” — they will be employable . 📌 What This Final Part Covers In this post, you will learn: What makes a Python developer job-ready Which projects recruiters actually value How to structure a Python learning → job roadmap How to build a strong Python resume What Python interviewers really ask How to prepare systematically for Python roles in 2026 This is the execution layer of the entire guide. Chapter 1 — What “Job-Ready in Python” Actually Means Being job-ready does not mean : Knowing every Python feature Memorizing syntax Completing random tutorials Being job-ready...

🐍 Python Development Crash Guide 2026 — Part 5: Python in Real-World Engineering: Automation, Backend APIs, Data Science & AI This is the part where Python stops being a “language you learn” and becomes a tool you use to build real systems . Most learners quit after syntax and OOP. Professionals move forward and ask: How is Python used in companies? What kind of systems are actually built? Which Python skills lead to jobs? This part answers those questions in depth . 📌 What This Part Covers In this post, you will learn: How Python is used for automation at scale Python’s role in backend and API development How Python powers data science workflows Python’s dominance in AI and machine learning Python’s interaction with databases Where Python fits in DevOps and cloud Which skills matter most for jobs in 2026 This part connects everything you learned so far to real engineering work. Chapter 1 — Python for Automation (One of the Fastest Job Paths) Automation is where Python delivers ...