C++ vs Rust Performance Benchmark: Rust Edges Out C++ in Key Areas

Using a bare except: block might prevent your application from crashing, but it often hides the real problem. Catching every exception without specifying the error type makes debugging extremely difficult and can allow corrupted data or failed operations to silently continue. The code appears stable while critical failures remain undetected. In backend systems and data pipelines, this can lead to incomplete database updates, incorrect analytics results, or unnoticed processing failures that surface much later. Handling specific exceptions helps isolate failure points, improves error tracking, and makes systems more reliable and maintainable. Robust software is not built by ignoring errors. It is built by understanding and handling them precisely. What is the most difficult bug you’ve traced back to poor exception handling? #Python #BackendDevelopment #SoftwareEngineering #ErrorHandling #CodingBestPractices

This note is a practical example of a code generation session within the mshell Ecosystem — an AI-and-Mathematics powered shell environment that supports 7 programming languages (C, C++, Rust, Go, Python, Lua, and Bash) plus native mshell scripting. The session demonstrates how an user interacts with LLM through a conversational prompt to generate a C/OpenGL application featuring three perpetually bouncing balls rendered in gold, silver, and copper. Beyond single-language code generation, mshell uses Markdown as the foundation for building documentation, multi-language pipelines, and interlanguage solutions. The core idea is that code blocks in different languages within a single .md document can exchange data through a session variable system — one block writes its output to a named variable, and the next block in any other language reads it as input. This means, for example, a C program can compute data, pass it to Python for statistical processing, then send the result to an LLM for analysis, and finally display the conclusion in Bash — all within one document, executed sequentially. LLM model calls can also be embedded inline, supporting Ollama, OpenAI, and Claude backends.

📌 Today’s DSA Series Topic: Linked List A Linked List is a linear data structure where elements (called nodes) are stored in non-contiguous memory locations. Each node contains: Data – the value stored Next – reference to the next node The list starts with a head node and ends with NULL. Unlike arrays, linked lists are dynamic in size and allow efficient insertions and deletions. 🔹 CRUD Operations on Linked List (Explained visually in the images below 👇) ✅ Create – Insert a new node (at beginning or end) ✅ Read – Traverse the list from head to NULL ✅ Update – Modify the data of an existing node ✅ Delete – Remove a node and adjust pointers 📊 Time Complexity Overview Insert at Beginning: O(1) Read / Traverse: O(n) Update: O(n) Delete: O(1) / O(n) (based on position) 💬 Want to learn Linked List CRUD operations in a specific programming language (C, C++, Java, Python, JavaScript)? Comment the language name, and I’ll explain it with code examples. 🚀 Keep learning. Keep growing. #DSASeries #LinkedList #DataStructures #CRUD #Programming #TimeComplexity #SoftwareEngineering

Instead of choosing which #Python function to run with if/elif/else, use a dispatch table — a dict with functions as values: from operator import add, sub, mul ops = {'+':add, '-':sub, '*':mul} s = input('Enter +, -, or * : ') r = ops[s](10, 20) print(f'10 {s} 20 = {r}')

Understanding C++ pointers isn’t about syntax — it’s about understanding memory. I’ve written a Medium article explaining: • How stack and heap memory differ • What a pointer represents at the hardware level • How dynamic memory allocation works • Why improper ownership leads to leaks and undefined behavior • When to use smart pointers and why These concepts are foundational for systems programming, performance engineering, and quant infrastructure. The article explores C, C++ and a bit of Python. If you’re strengthening your C++ fundamentals, I’d appreciate your thoughts. Link below. #Cpp #C #Python #pointers #malloc

To be able to integrate agentic code within deterministic code , I think programming languages like Python need another datatypes ( derived data type ) It can be library based like ndarray,pd dataframe or tensor kind and also a different kind of database that can operate on Chat based and voice based sequnce to sequence data. I am feeling the need for it.

𝗗𝗮𝘆 𝟯𝟰/𝟳𝟱 | 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝟳𝟱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 1143. Longest Common Subsequence 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆: Medium 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘂𝗺𝗺𝗮𝗿𝘆: Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0. A subsequence of a string is a new string generated from the original string with some characters deleted without changing the relative order of the remaining characters. 𝗠𝘆 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: I used dynamic programming with a 2D table. create a dp array of size (m+1) × (n+1), where dp[i][j] represents the LCS length of the first i characters of text1 and the first j characters of text2. If the characters match, add 1 to the diagonal value. Otherwise, take the maximum of the top or left cell. The final answer is stored in dp[m][n]. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: • Time Complexity: O(m × n), due to filling the DP table • Space Complexity: O(m × n), for the 2D DP array 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Dynamic programming breaks a complex comparison problem into smaller overlapping subproblems and builds the solution bottom-up. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸: https://lnkd.in/g9RzHv-j #Day34of75 #LeetCode75 #DSA #Python #Java #MachineLearning #DataScience #ML #DataAnalyst #LearningInPublic #TechJourney #LeetCode

Just published a detailed solution breakdown on LeetCode I recently wrote an explanation for the “Prime Number of Set Bits” problem, covering: • Intuition and brute-force approach • Bit counting optimization • Bitmask compression trick (storing prime checks inside a single integer) • Multi-language implementations (C++, Java, Python, C#, C) One part I enjoyed exploring was replacing repeated prime checks with a compact bitmask: (1 << bitCount) & 665772 Small problem — but great practice in thinking about constraints and constant-time optimizations. Here’s the full explanation: https://lnkd.in/gAjX6MMt Always trying to get better at writing clean explanations, not just solving problems. #Algorithms #DataStructures #BitManipulation #LeetCode #ProblemSolving

This note is a practical example of a code generation session within the mshell Ecosystem — an AI-and-Mathematics powered shell environment that supports 7 programming languages (C, C++, Rust, Go, Python, Lua, and Bash) plus native mshell scripting. The session demonstrates how an user interacts with LLM through a conversational prompt to generate a C/OpenGL application featuring three perpetually bouncing balls rendered in gold, silver, and copper. Beyond single-language code generation, mshell uses Markdown as the foundation for building documentation, multi-language pipelines, and interlanguage solutions. The core idea is that code blocks in different languages within a single .md document can exchange data through a session variable system — one block writes its output to a named variable, and the next block in any other language reads it as input. This means, for example, a C program can compute data, pass it to Python for statistical processing, then send the result to an LLM for analysis, and finally display the conclusion in Bash — all within one document, executed sequentially. LLM model calls can also be embedded inline, supporting Ollama, OpenAI, and Claude backends. https://lnkd.in/g7XpDGMJ

Lately I’ve been thinking less about “does it work?” and more about: • Can someone else read this in 6 months? • Can I test this without spinning up the whole system? • Can I change one feature without breaking five others? A few things I actively practice: • Keeping business logic out of controllers • Small, focused functions (no 200-line service methods) • Dependency inversion instead of hard-wiring implementations • Clear module boundaries Clean code isn’t about being academic. It’s about reducing the cost of change. Most systems don’t fail because of complexity. They fail because they become hard to evolve. #SystemDesign #CleanCode #Python