Fern (YC W23)’s Post

𝗚𝘂𝘆𝘀, 𝘄𝗵𝗮𝘁 𝗮 𝗿𝗶𝗱𝗲 𝘁𝗵𝗶𝘀 𝘆𝗲𝗮𝗿 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗳𝗼𝗿 𝗥𝗗𝗦! To all our RENOMIA software and data 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀, 𝗗𝗲𝘃𝗢𝗽𝘀, 𝗕𝗔𝘀, 𝗤𝗔𝘀, 𝗣𝗢𝘀, 𝗣𝗠𝘀 and every single 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 (yes, looking at you Credit Management Group - ESG Solutions, DENEVY, Devx, DTForce, ELEVUP, Headwork, intecs data, Revolt BI, SDMK Design, Vacuumlabs, WebToad) - you’ve helped build and release improvements across dozens of apps and services that made life easier for our users and 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲! A very special shout-out to our 𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲𝘀 𝗮𝗻𝗱 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 🤝 who provide invaluable feedback and continue setting our North Star 🌟 The real story here is 𝗮𝗹𝗹 𝗼𝗳 𝘆𝗼𝘂 🫵 and the passion you bring every day. But if you’re less into emoji and more into numbers (like I am), check this out: in 2024, together we resolved 𝟮𝟯,𝟳𝟱𝟭 𝗝𝗶𝗿𝗮 𝗶𝘀𝘀𝘂𝗲𝘀, made 𝟮𝟭,𝟲𝟰𝟲 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 across 255 GitLab projects, changed 𝟭𝟯,𝟭𝟳𝟲,𝟴𝟴𝟳 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗰𝗼𝗱𝗲, merged 𝟲,𝟯𝟵𝟯 𝗠𝗥𝘀, built and deployed 𝟰,𝟰𝟰𝟭 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝘁𝗮𝗴𝘀, and ran 𝟰𝟭,𝟴𝟰𝟳 𝗖𝗜/𝗖𝗗 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (that’s 127+ days of pure runners crunch). Talk about a team effort, a 𝗛𝗨𝗚𝗘 𝘁𝗵𝗮𝗻𝗸 𝘆'𝗮𝗹𝗹 ❤️! Oh, and let’s not forget our biggest endeavor yet (RIS20) has really kicked off this year and is well under way, with the first production release set for next year. Exciting times ahead! Finally, because it’s the season of giving, here’s our open-sourced GitLab stats extraction script for any fellow techies who want to check their own numbers: https://lnkd.in/djXZ-DcR Thanks again for making this year truly epic. Onward and upward 🚀

💡 How LeetCode problems helped me Cut Report processing Time by 50% I was working on a report that processed massive amounts of data and noticed a performance issue. After every initial API call, the system made repeated secondary API calls for object details. The details became available progressively, but we kept hitting the API unnecessarily. LeetCode-style thinking, where efficiency matters, helps in these cases, especially for data-heavy tasks. I decided to use a dictionary (hashmap) to store object details progressively and avoid second redundant API calls for objects already present in the dictionary. Instead of repeatedly hitting the server, I could pull the data directly from the dictionary in constant time, reducing the report creation time by about 50%! ⚡ Problem-solving techniques and optimization can make a difference in real-world situations. With today's large capacity and availability, it's easy to forget performance. Have you used problem-solving techniques to improve performance in your projects? #PerformanceOptimization #LeetCode #TechTips #ProblemSolving #DeveloperLife #Efficiency #DataProcessing

Have you ever encountered a misconfigured .git directory but struggled to extract its contents, even with well-known tools like GitDumper? Let me introduce you to GitRaptor an advanced tool that works alongside GitDumper, using a fresh and innovative approach to retrieve the latest and most complete data. Stay tuned, and I'll share the code with you along with tips to help you unlock its full potential! PoC: https://lnkd.in/es9TP2Gr

Working on an old legacy project can be quite challenging, especially when the previous developer has implemented temporary solutions that make the codebase complex and difficult to navigate. Functions are scattered, data is being fetched from unpredictable locations, and tracing errors becomes a daunting task. Despite the challenges, it's an opportunity to bring stability and efficiency to the system by cleaning up the code and implementing best practices. #LegacyCode #CodeMaintenance #Debugging #SoftwareDevelopment #CodeRefactoring #TechChallenges #CodeOptimization

Do you work with software daily? Teams of 3 or more? Technical debts stacked up and you'll "take care of them later"? You already have several code analysis tools but not sure how they are actually helping business? Then you might want to check out CodeScene. I've tried CodeScene with several of our customer projects and I must say it's quite different from other code analysis tools. We usually end up with a setup where we use CodeScene especially for understanding knowledge gaps, visualisation of problems and hot spots in the project code, and other static code analysis tools as complement. This has proven crucial for my projects when working with medium (>3 devs) to large projects and especially projects with history of developer turnovers (growth, replacement, losses). Yes, I'm writing this post because I know the founders and the team behind CodeScene. :) I also know the extensive research they continuously do in this area, how driven they are and not least that some of them are among the sharpest people I know.

I am excited to dive into this code extension! Calling all developers: What's your go-to tool for showcasing your code? Check out the link below: https://lnkd.in/gf3RdbDN

✨ 𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗛𝗮𝘀𝗵𝗠𝗮𝗽𝘀 𝗶𝗻 𝗖#: 𝗔 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿’𝘀 𝗚𝘂𝗶𝗱𝗲 As a seasoned software developer with experience, I’ve seen data structures come and go, but one that has consistently proven its worth is the HashMap. In C#, this is embodied by the 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝘆<𝗧𝗞𝗲𝘆, 𝗧𝗩𝗮𝗹𝘂𝗲> class, a powerhouse for managing key-value pairs with efficiency and grace. ❓ 𝗪𝗵𝘆 𝗛𝗮𝘀𝗵𝗠𝗮𝗽𝘀?  HashMaps are the go-to when you need rapid access to data. They map unique keys to values, allowing for quick retrieval without the overhead of iterating through a collection. This makes them ideal for scenarios where performance is key. ✍ 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 Ensure your keys are immutable for the lifecycle of the HashMap to maintain integrity. Utilize the ContainsKey() method to check for key existence before adding new elements to avoid exceptions. Leverage the power of foreach loops to iterate through your HashMap when needed. ⌛ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀  The beauty of HashMaps in C# lies in their performance. With an average time complexity of O(1) for retrieval, insertion, and deletion, they are a developer’s dream for high-performance applications. #CSharpDevelopers #DataStructures #CodingBestPractices #PerformanceOptimization #SoftwareDevelopment #DevCommunity 📑 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐨𝐧 𝐦𝐞𝐝𝐢𝐮𝐦 𝐰𝐢𝐭𝐡 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲<> 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐲𝐨𝐮 𝐦𝐮𝐬𝐭 𝐤𝐧𝐨𝐰.

The author debunks the myth that achieving 100% code coverage means your software is bug-free. Using a simple yet impactful example, Kapelonis demonstrates that even with perfect code coverage, bugs can still exist. The article discusses a one-line function that, despite having 100% unit test coverage, contains a divide-by-zero error, highlighting the limitations of code coverage as a metric for software quality. https://lnkd.in/dQ-_xdCR

🔴 Clean Code Tip 01: Meaningful Names _____ 🔵Choosing clear and meaningful names in code : 🔹️Saves future developers (and yourself) time and effort. 🔹️Acts like documentation, making code easier to understand. 🔹️Improves the readability and maintainability of your code. ▪ My Telegram Channel : https://t.me/algolab_2024 #cleancode #clean_code #CleanCode