Hamza Boutaleb’s Post

🚀 A Small Stack Problem That Changed My Thinking "When I first learned Stack, I thought I understood it." 🚀 What I Learned from Solving “Decode String” (LeetCode 394) Before solving this problem, my understanding of Stack was very basic. I thought: 👉 Just push everything into the stack — characters, numbers, symbols — and then use pop() and peek() whenever needed. But this question changed my thinking. 💡 Key Learnings: ✅ Stack is not about pushing everything blindly. We should push elements only until a specific condition is met. In this problem, we push elements into the stack until we encounter ]. Only then do we start popping and processing. This showed me a clear pattern-based stack usage. ✅ Conditional stack processing is important. Instead of random pop/peek operations, we must: Pop until [ Extract the number before [ Repeat the substring correctly This improved my problem-solving structure. ✅ Stack does not support primitive data types directly. In Java, Stack works with objects. So we cannot directly store char or int as primitives — we need to use: Character Integer or String That clarified an important Java concept for me. ✅ Learned how and why to use String.repeat() method. If we have a string and we want to multiply it multiple times, we can use repeat(). For example: If we have: "abc" and we want it 3 times Instead of manually looping, we can write: "abc".repeat(3) Output:👉 abcabcabc Special Thanks to Hareesh Rajendran na for Clear Explanation and his Support #DataStructures #Stack #Java #LeetCode #ProblemSolving #CodingJourney

To understand the spectrum from low-level to high-level programming, and identify exactly where your code sits, you can simply look at how you handle memory. Assembly Level: At the Assembly level, the concept of a "heap" doesn’t inherently exist. You explicitly make a system call to the OS (like brk or mmap) saying, "Give me a page (4096 bytes)," and the OS returns a raw memory address in a register. That is your base pointer. Once you receive that starting address (let's say 0x400000), you are solely responsible for every byte and bit flip within that address space. C: In C, the standard library (typically glibc on Linux) manages the allocation logic for you via malloc, but you are still manually in charge of the object lifecycle. If you allocate it, you must free it. Rust: Rust takes this a step further. It utilizes the same underlying "Wholesaler" (the Allocator), but it automates the lifecycle management. Through ownership and borrowing rules, the compiler ensures memory safety without manual intervention. High-Level (Java, JS, Python): In managed languages like Java, JavaScript, or Python, you don't care about memory management; the Garbage Collector handles it. You focus almost exclusively on business logic. That is, until you hit a performance cliff and realize your service can't handle the load, forcing you to hunt for optimization tricks. Essentially, these languages aren't for talking to the hardware—they are for talking to the runtime environment. Low-Code: At this point, you don't even bother talking to the code. Your only drive is solving the immediate business problem. The platform abstracts away the entire execution environment for you. LLM Vibe-Coding: Finally, there is LLM "vibe-coding." At this stage, you simply state the problem and don't even bother with the solution logic. You are operating purely on intent. #memory #management #lowlevel #highlevel #lowcode #ai #vibe #context #business

28.6% resolution rate on real Rust repository issues. That is the current ceiling for LLM-based coding agents. Rust-SWE-bench tests agents on 500 real GitHub issues from 34 Rust projects. The failure analysis is where it gets interesting. 44.5% of tasks fail at the issue reproduction stage. The agent never even gets to write a patch. It cannot set up the environment, cannot reproduce the bug, cannot run the tests. Nearly half the failures have nothing to do with code generation ability. For the other half, compilation errors come from two sources: failure to model repository-wide code structure, and failure to comply with Rust's strict type and trait semantics. When the required patch exceeds 150 lines, the gap between agent architectures widens dramatically. This tells us something important about where the real bottleneck is. We keep optimizing for "can the model write better code." But in strongly-typed, real-world codebases, the harder problem is everything around the code: environment setup, dependency resolution, cross-file navigation, and compiler feedback loops. RUSTFORGER addresses this by adding automated test environment isolation and Rust metaprogramming-driven dynamic tracing. It pushes resolution from 21.2% to 28.6%, and uniquely solves 46 tasks that no other agent could solve across all LLMs tested. For anyone building coding agents: if your evaluation only covers Python, you are measuring the easy part. Strongly-typed languages expose whether your agent actually reasons about code structure or just pattern-matches on syntax. Paper: https://lnkd.in/ea6EmRBs #LLM #CodingAgents #Rust #SWEBench #SoftwareEngineering #AIEngineering

Still writing boilerplate code in 2026? If your data models are full of getters, setters, constructors, equals(), and toString() methods — you’re paying a hidden productivity tax. Modern languages give us better tools ✅ Kotlin data class ✅ Python @dataclass ✅ Java record ✅ C# record These constructs are not just syntactic sugar. They: Reduce 60–80% of repetitive code Improve readability and intent Encourage immutability and safer designs Make production systems easier to maintain and reason about In my latest blog, I break down real, production-ready examples showing how these features help eliminate boilerplate across Kotlin, Python, Java, and C# — and when you should (and shouldn’t) use them. Read the full article here: https://lnkd.in/euK7AmNg If you care about clean architecture, developer productivity, and writing less code that does more, this one’s for you. 💬 Which language do you use most to fight boilerplate — Kotlin, Java, Python, or C#? #SoftwareEngineering #CleanCode #Programming #Kotlin #Python #Java #CSharp #BackendDevelopment #SoftwareArchitecture #DeveloperProductivity #CodingBestPractices #TechLeadership #JVM #DotNet #Microservices #EngineeringCulture #Immutability #CodeQuality #TechBlog #LearningAndDevelopment

Compiled vs. Interpreted Languages: Which One Is Better? One of the first major concepts every programmer encounters is the difference between compiled and interpreted languages. But the real question is: Is one actually better than the other? Let’s break it down in simple terms. Compiled Languages: A compiler translates the entire source code into machine code before execution. Once compiled, the program runs directly on the CPU. Examples: C, C++ Faster performance Better optimization Stronger code protection Platform-dependent Requires recompilation after changes Best for: operating systems, embedded systems, high-performance applications. Interpreted Languages: An interpreter translates and executes code line-by-line at runtime. Examples: Python, JavaScript Faster development cycle Easier debugging High portability (if interpreter exists) Slightly slower execution Source code often exposed Best for: web development, automation, data science, rapid prototyping. What About Java? Java uses a hybrid approach. It compiles code into bytecode, which is then interpreted or just-in-time compiled by the JVM. This balances portability and performance. So, Which One Is Better? There is no universal winner. It depends on: • Performance requirements • Development speed • Portability needs • System-level control • Team expertise Smart developers don’t ask which is better. They ask: Which is better for this problem? Understanding both models makes you a more versatile and strategic engineer. Which do you prefer working with compiled or interpreted languages? Read More: https://lnkd.in/gCZNKtZu Podcast: https://lnkd.in/gb84Yarh #Programming #SoftwareDevelopment #Coding #Java #Python #JavaScript #CPlusPlus #ComputerScience #Developers #TechEducation #RoyalResearch

Arrays are taught as fixed-size data structures in Data Structures and Algorithms (DSA). But when we code in languages like JavaScript or Python, we easily use operations like push() and pop() that seem to change the size of the array. So how does that actually work 🤔 ? In DSA theory, an array is defined as a collection of elements: • stored in contiguous memory locations • having the same data type • with a fixed size once created This means the size of an array cannot change 🙅♀️ after memory is allocated. However, when solving problems in programming languages, we often perform operations that appear to add or remove elements. This is because most languages provide dynamic alternatives or internal implementations that handle resizing automatically. Examples across languages: • Java int[] arr = new int[5] → Fixed-size array ArrayList → Dynamic structure that can grow or shrink • C++ int arr[5] → Fixed-size array vector<int> → Dynamic array implementation • JavaScript Arrays behave like dynamic arrays, allowing operations like push() and pop(). • Python list is used as a dynamic array-like structure, allowing flexible resizing. 💡 Key takeaway: Arrays are fixed-size in DSA theory, but many programming languages provide dynamic implementations to make them easier 😇 to use. #DataStructures #Arrays #DSA #ProgrammingConcepts

If your Spring Boot app can call an API… it can now think, search, and act too. Can Java replace Python? This document breaks down the Spring AI ✅ what it is ✅ what it can build (Chat, RAG, Tool Calling, Structured output) ✅ what you need to set it up ✅ mini code examples + real enterprise adoption pattern ✅ the honest answer: “Will it replace Python?” If you’re a Java #dev #architect #manager planning #GenAI in #production, this is your quick roadmap. Which part are you most interested in? RAG or Tool Calling? #SpringAI #SpringBoot #Java #GenerativeAI #LLM

🚀 SOLID Principles Explained — With Real Backend Examples Ever opened a codebase and thought: 👉 “Why does changing one small feature touch 10 files… and still break tests?” That usually means one thing: SOLID principles were missing. I just published a detailed Medium article breaking down: ✅ What SOLID really means ✅ Each principle in simple language ✅ Real-world backend-style examples (Go / Java / Node) ✅ What goes wrong when you ignore them ✅ How they make systems easier to scale and test If you’re building microservices or large backend systems, this will give you practical clarity — not textbook theory. 📖 Read the full blog below: 👇 Would love to hear from fellow engineers: 💬 Which SOLID principle has helped you the most in production? #SoftwareEngineering #BackendDevelopment #SOLID #CleanCode #SystemDesign #Architecture #Microservices #Programming #GoLang #Java

Most Python codebases rely on dynamic typing — until they scale. At scale, silent bugs, fragile refactors, and unclear contracts become real productivity killers. One of the most powerful (and underused) tools in modern Python for building robust, production-grade systems is: Protocols + Generics These features bring interface-driven design and compile-time safety to Python — without sacrificing flexibility. 🔹 Protocols enable structural typing (“if it behaves like X, it is X”) 🔹 Generics allow reusable, type-safe abstractions 🔹 No inheritance required — just the correct shape 🔹 Perfect for Clean Architecture, DI, and testable systems Example use cases: ✅ Repository patterns (DB / API / Cache interchangeable) ✅ Plugin systems ✅ SDK & library design ✅ Service layer decoupling ✅ Mocking without brittle test doubles ✅ Large-scale refactoring with confidence By depending on capabilities instead of concrete classes, your business logic becomes storage-agnostic, test-friendly, and future-proof. In modern Python (3.11+), combining strong typing + static analysis (Pyright/mypy) delivers many benefits traditionally associated with statically typed languages — while retaining Python’s developer velocity. If you’re building serious backend systems, this is no longer optional knowledge — it’s a force multiplier. Dynamic language. Static guarantees. Clean architecture. Read More: https://lnkd.in/gRtdPtP2 #Python #SoftwareEngineering #BackendDevelopment #CleanArchitecture #TypeSafety #StaticTyping #Programming #Developers #TechLeadership #SystemDesign #APIDevelopment #CodeQuality #ScalableSystems #DesignPatterns #ProgrammingLanguages #PythonDeveloper #SoftwareDevelopment #TechInnovation #EngineeringExcellence #CodingBestPractices

Why does Python prefer object.method() over simple function calls? 🤔 That design choice is exactly why Object-Oriented Programming scales. An object isn’t just data. It’s data + behavior bundled together. A string doesn’t only store characters — it knows how to search, transform, and slice itself. A file object doesn’t just hold a file name — it tracks state, position, and how data flows in and out. This approach is called Object-Oriented Programming (OOP), and it exists for very practical reasons 👇 Why OOP works in real systems • Organization → cleaner structure and safer namespaces • Encapsulation → multiple independent objects without side effects • Reusability → write once, use everywhere • Easier debugging → behavior lives in one place • Relationships between types → same operation, different meaning, handled uniformly This is why large automation frameworks, backend systems, and production codebases don’t survive without OOP. 💡 OOP isn’t about syntax. It’s about modeling complexity in a clean, scalable way. 👉 When did OOP finally click for you? Or what part of it felt the most confusing early on? #ObjectOrientedProgramming #OOP #Python #SoftwareEngineering #SDET #QAEngineering #BackendDevelopment #ProgrammingConcepts #LearningToCode #TechCareers #CleanCode