Provoke Solutions’ Post

What would you do ⁉️ #codecomplexity🧩🧩 Lets say you are introducing a new logic in your application, you have limited time to do the job. You can add some if else😀 and boolean in your methods to add your logic but that will make the code more complex and break the principles or you can refactor the complete solution after analysing with the new requirements which will take more time then given. 😌 Drop your thoughts in the comments 😉

👋 Hi Techies, As coders and data enthusiasts, we're always fascinated by code editors and their new updates and functionalities. There are many code editors, but favorites like VS Code, PyCharm, Sublime, and Atom stand out. Among these, the most used is VS Code. BTW, did you know VS Code is written in Electron (JavaScript)? It's cool to use but often leaves us craving more speed. 🚀 Exciting news! A new code editor named Zed (https://zed.dev/) has been released, and it's written in RUST 🦀, which promises to be faster than all available editors. It's definitely worth a try! Note: It's currently available only on macOS, but they will release it for other OS soon. 🎉 #CodeNewbies #Programming #CodeEditors #TechNews #ZedEditor #DataScientist #DataEngineers #Coders #Programmers #DataAnalyst

Why Rust Is Fast 🔥 1. Ownership and Borrowing 🧠: Rust's ownership system ensures that every piece of data has a single owner, and once it's done, it’s automatically dropped (memory is freed). This avoids garbage collection, making Rust faster by eliminating runtime overhead. Borrowing lets you pass references to data without taking ownership, ensuring safe, concurrent access without risking memory errors like null pointer dereferencing or data races. 2. LLVM Optimizations ⚙️: Rust uses the LLVM compiler backend to apply aggressive optimizations during compilation. These optimizations include loop unrolling, function inlining, and constant propagation, making the generated machine code highly efficient. This allows Rust to execute at speeds comparable to C, while maintaining safety and reliability. 3. Zero-Cost Abstractions 🏗️: Rust allows for high-level abstractions (like closures, iterators) without adding performance overhead. It’s clean code with C-like speed! ⚡ 4. Concurrency Without Data Races 🛠️: Rust ensures safe concurrency without locking, enabling multi-threaded performance without data races ⚡. 5. Memory Safety 🛡️: Rust guarantees memory safety without sacrificing performance, avoiding errors like null dereferencing or buffer overflows. No segfaults! 🚫 6. Compiles to Native Code ⚙️: Like C, Rust compiles directly to machine code, ensuring efficient execution with minimal runtime overhead 🖥️. ✨ Why Rust is Comparable to C ✨ C has been the king of performance for decades 🏆, giving developers low-level access to the hardware. Rust is just as fast as C because it also compiles to machine code and gives developers control over the system, while also providing safety features (like preventing memory errors), making it a safer C. It’s like driving a fast car but with seatbelts and airbags! 🏎️💨 Head over to : https://lnkd.in/gz9idFXa for more. #Rust #CProgramming #TechExplained #FastAndSafe #MemoryManagement #Programming #CodingWithEmojis

When was the last time you did something valuable as a developer which was not: 1. Solving a data issue because some dependent system was not providing data properly 2. Fixing docker files, docker-compose or helm charts which suddenly stopped working 3. Hunting around for hours iin log files to trace an issue 4. Updating packages and modules and dealing with all the weird issues that came from doing it. Where am I going with this post? I think in the last 20 years software engineering has not evolved, but devolved from building well thought out layers to hastily hacked but heavily marketed stuff that is neither maintained reliably, designed well or documented properly for us to be able to rely on them, forcing us to work on meaningless rubbish most of the time. We need a hard reset, soon, because this is killing productivity across the board and costing us billions of dollars of wasted developer hours

Looking for ways to improve your codebase? Here are some of the approaches that you can take: - Follow DRY. - Remove dead code. - Don’t use global variables. - Avoid casts. Use polymorphism. - Keep functions small and focused. - Favor immutability wherever possible. - Avoid passing booleans as parameters. - Use constants instead of magic numbers. - Follow the Law of Demeter: don’t talk to strangers. - Use exceptions for exceptional cases, not control flow. - Reduce deep nesting (too many 'if' or 'else' statements). - Break down large classes into smaller, single-purpose ones. - Write tests before refactoring to ensure behavior remains intact. - Rename functions if their name doesn’t clearly describe their purpose. - More than two arguments in a function? Bundle them into a relevant class. - Avoid side effects in functions; return consistent outputs for the same inputs. All these tips can be a separate topic of their own, but overall they should give you a good foundation for improvement. Follow them, research why they’re useful and your codebase health should change for the better. Anything I missed? #androiddevelopment #software #tips #softwareengineering

Hello Connections! I attempted yesterday's Codeforces Round 943 (Div 3), and was able to solve all problems till G1. Ideas for the problems - A - There is an obvious answer for y = x-1, giving us the highest equation value as x. We cannot go above x, so an O(1) is directly visible. B - Two pointers classic implementation, increase length and both pointer i and j when a[i] matches b[j], else increment j till a[i]!=b[j]. C - Some math and pattern building. Start your construction from the last term, then put second last value as 1e9. Now from the back each value can be last answer - a[i], this keeps the mod as expected x[i]. D - Do what is asked, find the path cycle on which both players travel, and explore all possibilities of using K turns to find their maximum scores. Print accordingly. E - Pattern and observation based question, we can always build all manhattan distances from 0 to 2*n - 2, by smartly placing the cells at i,i (leaving 1,2 or 2,1 position). This is the highest set size. F - Use the prefixXor property to handle cases of k=2 or k=3 separately, if we see segment xor is 0, answer is "YES" due to 2 segments getting fulfilled, else use xor properties to derive the possibility of 3 intersections with preXor[l-1]^preXor[r] value. G1 - Binary search on LCP value, and use string hashing or any suitable algorithm to optimise window checks. I discussed about all the problems in depth in a video editorial - https://lnkd.in/g4mQvjTf

Software has a unique characteristic that it requires a lot of context and knowledge to appreciate the work as art. When I show my mom a woodworking project, she can see it, feel it, and appreciate the effort I put into making it. I’m easily validated. When I show her a new algorithm that moves work from the CPU to the GPU, I mostly get blank stares. She doesn’t have the context or knowledge to understand the effort and ingenuity that went into that solution. You’re probably thinking: “wrong audience, dude”… However, when I show that same algorithm refactor to a web dev friend, they have the same reaction as my mom. This is an experienced engineer with a decade+ of experience. They’re very skilled and knowledgable, but having never written a line of code for the GPU, they lack the context to know what the work entailed. When I share that code with my pairing partner, who has been in the weeds with me for weeks trying to eke out every bit of performance wins we can for Tuple, the reaction is precisely what my soul needs. Authentic excitement. Follow up questions that help me share the brilliant moment I had to discover the solution. Eagerness to test the algorithm out on their machine. I feel heard. Seen. Proud. Less alone. 

The vast majority of code, estimated at around 70-90%, is never actually used! This is often referred to as "dead code." It can clutter programs, making them harder to understand and maintain. It also takes up space and processing power, even though it serves no purpose. Focusing on code quality helps developers write efficient programs that only contain the code they actually need. This not only saves on resources but also makes the codebase easier to navigate and update in the future.

If you're working on code models you should check out this notebook with distilabel, argilla, and Qwen Coder 2.5. You could use it for use cases like this: - Code generation dataset in a specific domain or language - Code classification dataset - Code retrieval dataset for a custom IDE Let's build some great open code models!

🚀 Enhance Your Rust Code Performance! With Rust's memory safety and zero-cost abstractions, it's already a high-performance language, but there are key optimization techniques that can make it even better. From leveraging ownership and borrowing systems to using multi-threading and profiling tools, discover how to write faster, more efficient code. https://lnkd.in/ggpD8Bmm #RustLang #CodeOptimization #SoftwareEngineering #TechInsights #ProgrammingBestPractices