C++ string literals and static_cast behavior

🚀 Turning Logic into Art with C++ Today, I worked on building a pattern generation program using C++ and recursion — and the result was something visually satisfying: a perfectly aligned diamond/star pattern rendered in the console. What looks like a simple pattern actually involves: ✔️ Understanding recursion deeply ✔️ Managing multiple variables efficiently ✔️ Controlling flow for symmetrical design ✔️ Writing clean and optimized logic This small project reminded me that programming is not just about solving problems — it’s also about creativity and precision. Even a console output can feel like art when logic is applied the right way. 💡 Key takeaway: Strong fundamentals in Data Structures and recursion can help you build elegant and efficient solutions, even for problems that seem simple at first glance. Always learning. Always building. #Cplusplus #Programming #DSA #Recursion #CodingJourney #SoftwareDevelopment #ProblemSolving

🔹 Part 1: SFINAE in C++ This short video is part of my "C++ Programming Topics" series 👇 And also included in my broader C++ templates playlist. 💡 The problem: Many developers struggle with controlling which template functions should participate in overload resolution. This can lead to: -Confusing compiler errors -Unintended function matches -Less robust generic code ❌ 📌 This is where SFINAE becomes powerful: 👉 Substitution Failure Is Not An Error allows the compiler to silently discard invalid template instantiations. 💡 The solution: Applying SFINAE techniques to a simple example: int sum(int a, int b) { return a + b; } ✔️ Constrain templates to valid types only ✔️ Prevent invalid overloads from compiling ✔️ Write safer and more expressive generic code ⚙️ Bonus insight from the video: You’ll see how SFINAE works step by step by evolving this basic function: 1️⃣ Basic function behavior Understand the baseline implementation 2️⃣ Applying SFINAE Control when the function is enabled 3️⃣ Safer templates Restrict usage to valid type combinations 🎯 Key takeaway: SFINAE helps you guide the compiler instead of fighting it. It’s a foundational technique for mastering modern C++ templates. 🎥 Watch the video: https://lnkd.in/d7zPHDzb 📚 Full playlist: https://lnkd.in/dDNVWvVC #cpp #moderncpp #programming #softwareengineering #templates #cleancode

🔹 Part2: Exploring type_traits in C++ (is_integral & enable_if) This short video is part of my "C++ Programming Topics" series 👇 And also included in my broader C++ templates playlist. 💡 The problem: When writing generic C++ code, not every type should be treated the same way. This can lead to: -Invalid operations on unsupported types -Hard-to-read template errors -Fragile and unsafe generic code ❌ 📌 This is where the type_traits library becomes essential: 👉 It gives you compile-time tools to inspect and control types. 💡 The solution: Understanding and implementing core utilities like: ✔️ is_integral — detect whether a type is an integral type ✔️ enable_if — conditionally enable/disable functions ✔️ type_traits — the foundation of compile-time type logic ⚙️ Bonus insight from the video: You’ll explore simplified implementations to really understand how they work under the hood: 1️⃣ is_integral How the compiler determines if a type belongs to integral types 2️⃣ enable_if How to include/exclude functions during compilation 3️⃣ Combining both Apply constraints to templates for safer and cleaner code 🎯 Key takeaway: Don’t just use type_traits—understand how they work. That’s what unlocks the real power of modern C++ templates. 🎥 Watch the video: https://lnkd.in/d7zPHDzb 📚 Full playlist: https://lnkd.in/dDNVWvVC #cpp #moderncpp #programming #softwareengineering #templates #metaprogramming #cleancode

Day 10 of My Dynamic Programming Journey Today I solved Edit Distance (LeetCode 72) — one of the most important and practical string DP problems. Problem Insight: Convert one string into another using minimum operations: Insert Delete Replace Core Idea: At every step, I had two scenarios: If characters match → move diagonally (no cost) If not → try all 3 operations and pick the minimum DP Thinking: Defined: dp[i][j] = minimum operations to convert first i characters of string1 into first j characters of string2 Key Realization: “Every mismatch is a choice — insert, delete, or replace — DP helps choose the cheapest one.” Optimization Learning: Started with 2D DP (O(n×m)) Optimized to 1D DP (O(m)) by tracking previous (diagonal) state What I Learned Today: Strong understanding of string DP transitions Importance of base cases (empty string conversions) How to manage state transitions in space-optimized DP Next Goal: Moving to Knapsack pattern (0/1 Knapsack & Partition problems) to explore subset-based DP #DynamicProgramming #DSA #CodingJourney #ProblemSolving #LeetCode #LearningInPublic #Day10

Built a simple C++ Compiler from Scratch! Sharing my latest project - a simple yet functional C++ compiler built from the ground up using Flex, Bison, and ANTLR4. This project dives into the core phases of compilation and helped me strengthen my understanding of how programming languages actually work behind the scenes. Key Features: • Lexical Analysis using Flex • Syntax Parsing using Bison & ANTLR4 • Three Address Code (TAC) Generation • Basic Assembly Code Generation • Detailed logging of each compilation phase How it works: 1. Source code is tokenized into meaningful units 2. Tokens are parsed based on grammar rules 3. Intermediate code (TAC) is generated 4. Final pseudo-assembly code is produced What I learned: Building a compiler from scratch gave me hands-on experience with: • Language design fundamentals • Parsing techniques and grammar rules (Bison & ANTLR4) • Intermediate representations • Low-level code generation concepts 🛠️ Tech Stack: C++ | Flex | Bison | ANTLR4 | Makefile 📌 Example outputs include: ✔ Token logs from lexical analysis ✔ Three Address Code ✔ Assembly-like instructions This project is a small but meaningful step into the world of compilers and systems programming. 🔗 GitHub Repo: https://lnkd.in/ggkcdTyV I’d love to hear your thoughts or suggestions for improvement! #CPlusPlus #CompilerDesign #Flex #Bison #ANTLR4 #SystemsProgramming #SoftwareEngineering #LearningByBuilding #BIE #bioinformatics #BAU

Free course on "Calculator implementation in C++ using Expression Grammar (context free grammar)" From Chapter 6 of the book "Programming: Principles and Practice using C++" by Bjarne Stroustrup. I have tried to share my learning experience while converting Expression Grammar into the Calculator program using C++. Hope, it will help people like me to understand the concepts in a faster way and help to resolve doubts. While coding the program, I am also sharing the thought process which goes on in developers mind. We will face compile-time, run-time errors and then we will resolve them using gdb debugger. Implementing Expression Grammar in C++ gives us a bit of understanding on how the parsing works. Instructor: Kashinath Chillal (Founder, Aloraq Software (OPC) Pvt. Ltd.) https://lnkd.in/gX8eXRK3

Deep dive into C++ Generic Programming: Implementing the "Graal" Library. "I’m excited to share my latest project: Project Graal. 🚀 As part of my Computer Science studies, I developed a custom C++ template library that recreates essential algorithms from the STL (Standard Template Library). The goal was to master Generic Programming and understand how memory is manipulated under the hood using iterators. Key implementations include: Search & Comparison: find_if, all_of, any_of, equal. Range Manipulation: reverse, copy, and partition. The Challenge: Implementing unique using the two-pointer technique (slow and fast iterators) to filter data in-place. This project pushed me to understand how C++ handles types at compile-time using templates and how to write clean, documented code using Doxygen. Check out the full repository here: 🔗 https://lnkd.in/darWrFUm #Cpp #Programming #SoftwareDevelopment #GenericProgramming #ComputerScience #Algorithms"

🚀 Day 212 of #300DaysOfCoding Today I solved a challenging DP problem: 👉 Minimum Distance to Type a Word Using Two Fingers This problem really tested my understanding of Dynamic Programming, state optimization, and decision making. 🧠 Key Learning At every step, we have two choices: Use finger 1 Use finger 2 The goal is to minimize total movement distance I implemented a DP solution where: State = positions of both fingers Transition = try both fingers and take minimum ⚙️ Approach Represent each character as coordinates on a grid Use Manhattan Distance for movement Apply Bottom-Up DP (27 × 27 states) Optimize transitions carefully to avoid recomputation 💡 What I Learned DP problems are not just about memorization — they are about thinking in states and transitions Small mistakes in optimization can completely break the solution 😅 Debugging teaches more than solving 🧪 Example Input: "CAKE" Output: 3 🔥 Takeaway Consistency is the real game changer. Every day, I’m getting better at problem-solving and thinking logically. #300DaysOfCoding #DataStructures #Algorithms #DynamicProgramming #CodingJourney #LeetCode #Cplusplus #ProblemSolving

Solved a Gray Code problem in C++ today. The task was to generate bit patterns from 0 to 2^n - 1 such that every consecutive pattern differs by only one bit, while always starting from 0. I used the Gray code formula: gray = i ^ (i >> 1) This makes the solution clean and efficient, and guarantees that adjacent codes differ by exactly one bit. Example for n = 2: 00 -> 01 -> 11 -> 10 What I like about this problem is how a simple bit manipulation formula can solve what looks like a complex sequence-generation challenge. Concepts practiced: Bit Manipulation Binary Representation Pattern Generation C++ Problem Solving #cpp #coding #programming #datastructures #algorithms #problemsolving #bitmanipulation #leetcode #geekforgeeks

Copy vs Move semantics in C++ — small change, big performance impact. At first glance, both can look like a simple assignment. But under the hood, they behave very differently. Copy → duplicates data → allocates new memory → keeps the source unchanged Move → transfers ownership of resources → avoids unnecessary allocations → leaves the source in a valid but unspecified state 💡 This difference becomes especially important with resource-heavy types such as std::string, std::vector, or user-defined classes managing dynamic memory. Another key point: std::move does not move anything by itself. It only casts an object to an rvalue, enabling move semantics if the type supports them. I put together this visual to make the difference easier to understand at a glance. Curious how others approach this in real code. #cpp #cplusplus #moderncpp #performance #softwareengineering #programming #coding