Prioritizing Results Over Code Style in Engineering

I've noticed a pattern: Most developers focus on writing clean code. And yes, readable, well-structured code matters. But here's the truth: Clean code means nothing if it's solving the wrong problem. I’ve seen engineers spend days polishing code for a feature that users didn’t need… Optimizing functions that didn’t move the business forward… Refactoring components that weren’t even in use. Because we confuse good looking code with good thinking. But great developers do this instead: - They pause before writing. - They ask better questions. - They focus on the real problem, not the prettiest solution. If you want to level up as an engineer, don’t just aim for elegant syntax. Aim for meaningful outcomes. Your code shouldn’t just be clean, it should be right. Because great code doesn’t just look good. It drives results. How do you make sure you're solving the right problem before you start coding? P.S. If you’re a software engineer tired of getting ghosted after applying, check out the Software Engineer Resume System. It’s the exact framework I used to turn 150+ applications into 40+ offers, no fluff, just the system that gets results: https://lnkd.in/dqCp4EHw

I've noticed a pattern in engineering teams: the pursuit of technical elegance often comes at the expense of user value. After building systems processing billions of daily events, I've seen brilliant engineers spend weeks optimizing algorithms that users never experience while ignoring performance bottlenecks that frustrate them constantly. The most successful technical leaders I've worked with ask a simple question before any optimization effort: 'Will users notice if we solve this?' We deliberately track user-facing impact alongside technical metrics to keep our engineering efforts grounded in reality. Perfect code that doesn't improve user experience is just expensive hobby programming. What user-invisible technical work are you prioritizing that might not actually matter? #EngineeringPriorities #UserExperience #TechnicalLeadership #ProductDevelopment

You’re Not as Productive as You Think—The Ugly Truth About Code That Works Ever seen code that looks like it was designed to win a beauty pageant rather than solve a problem? Yeah, I’ve been there too. We all love the idea of writing that perfect, elegant snippet that gets us a few high-fives on GitHub. But let’s be honest—if your code doesn’t solve the problem it was meant to, what’s the point? I’ve fallen into the trap of over-engineering my solutions just to flex a bit. I’d whip out every principle in the book—SOLID, Clean Code, KISS—you name it. But what I’ve learned is that none of that matters if your code isn’t solving the core problem efficiently. Your time is precious, and you can spend it in many ways: 1. You can make your code look "cool" and professional, but that’s not going to add value to your customers who are actually using your app. 2. Or, you can use that time to solve new problems—or even revisit and solve existing problems in a better way. Here’s the deal: First and foremost, your code needs to do what it’s supposed to do—solve the problem. After that, worry about performance, efficiency, cost-effectiveness, scalability, maintainability and consistency. And please, for the love of all things binary, don’t overengineer. Keep it simple. The next time you’re about to dive deep into creating some overly complex, “professional” looking code, ask yourself: am I solving the problem, or just trying to look smart? Trust me, your future self and your team will thank you for prioritizing clarity and functionality over complexity. Remember, the best code is the kind that quietly gets the job done, without anyone cursing your name in the process. Keep it simple, keep it effective, and most importantly, keep it real. Happy coding! 💻 #softwareengineering #programming #coding

🛑 The Hidden Cost of "Perfect" Code: Rethinking Over-Engineering Last week, during our Engineering Week at Kuda , we had some incredibly productive sessions aimed at helping every engineer level up. The topic that generated the most discussion was one that plagues nearly every fast-moving tech company: Over-Engineering. The key takeaway? Over-engineering is not about writing beautiful code; it's about solving problems the product doesn't have yet. What We Discussed: The Double-Edged Sword We often define over-engineering as building complexity that exceeds the current or foreseeable requirements. But is it always bad? Why it's Dangerous (The Default): Time & Cost: It burns precious engineering resources on hypothetical needs. Cognitive Load: It makes the system harder to understand, maintain, and debug. Rigidity: It can lock you into an architecture that prevents a necessary pivot later. When it Might Be Needed (The Exception): Critical Systems: Security, authentication, ledger systems, or high-volume financial endpoints where correctness and future scale are non-negotiable from Day 1. High-Confidence Forecasts: Implementing widely known, non-changing standards (e.g., specific regulatory compliance) The Core Problem: The YAGNI Principle The biggest risk of over-engineering is neglecting the YAGNI principle: You Aren't Gonna Need It. It often stems from a developer's desire to future-proof everything, or sometimes, just to show off technical mastery. But if you spend three days building an abstraction layer for a database you will never switch from, you've missed three days of building value. Our goal is to be efficient, not maximalist. The best engineering is often the simplest: build the solution for the problem today, architected cleanly enough to be extendable when the business demands change tomorrow. If the complexity doesn't deliver immediate, measurable business value, it's debt, not investment. What's the most memorable example of over-engineering you've seen (or accidentally committed)? Share your story! #SoftwareEngineering #KudaLife #EngineeringWeek #OverEngineering #TechDebt #YAGNI

When I started my career as a software engineer, I thought “great engineering” meant: → Writing countless lines of code. → Using the trendiest frameworks. → Splitting everything into distributed microservices. The more code, the better, right? But one day, my mentor said something that flipped my perspective: "𝘛𝘩𝘦 𝘣𝘦𝘴𝘵 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘪𝘴𝘯’𝘵 𝘵𝘩𝘦 𝘰𝘯𝘦 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘤𝘰𝘥𝘦—𝘪𝘵’𝘴 𝘵𝘩𝘦 𝘰𝘯𝘦 𝘵𝘩𝘢𝘵 𝘴𝘰𝘭𝘷𝘦𝘴 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘸𝘪𝘵𝘩 𝘢𝘴 𝘭𝘪𝘵𝘵𝘭𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘢𝘴 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦." Here’s what great software engineering actually looks like: → Understanding your users’ real needs and pain points. → Designing simple, elegant solutions to address those needs. → Writing just enough clean, maintainable code to make it work. → Prioritizing reliability, performance, and scalability over unnecessary bells and whistles. And sometimes? The best solution doesn’t require code at all. A config change, a simple script, or even teaching your users a better way can sometimes do the trick. 💡 𝗠𝘆 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The true mark of a great engineer isn’t how much code they write but how much value they deliver with the simplest solution. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗹𝗲𝘃𝗲𝗹 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹𝘀? 👉 Check out kodekarma.dev, where we focus on building smarter engineers who solve real-world problems with clean, efficient code. If this resonated, hit like and share to inspire others in your network! 🚀 Cheers ❤️ Chirag Goel

You can choose one of two things. One million lines of clean, well-architected code or one million euros in company revenue. Most developers instinctively lean toward the code. It is concrete, intellectually satisfying, and directly connected to their daily work. Revenue, in contrast, feels abstract and distant. From a business perspective, the choice is trivial. The money wins every time. Agentic engineering makes this tension very visible. As software becomes cheaper and faster to produce, code quality alone stops being the primary value driver. The focus shifts away from elegance and toward impact. Outcomes start to matter more than craftsmanship. This marks a broader transition. Engineering for its own sake is losing relevance. Engineering as a means to achieve concrete business results is becoming the standard. And that shift should force managers to rethink how developer incentives are aligned with actual company goals.

Are You Over-Engineering—In Code and Life? 🤓 At a recent AI hackathon, I watched talented engineers overcomplicate their solutions, while a simpler approach delivered better results. Later, during a discussion on system design (over cocktails, of course 🍸), my friend Madeline Zhang pointed out something that resonated beyond just coding: 👉 "Delia, you’re over-optimizing your solution." Her perspective? "You’re trying to implement A pathfinding when a simple greedy algorithm would work just fine."* It made me realize—whether in tech, business, or even decision-making in life—we often over-engineer solutions, striving for perfection when a practical, working solution is the better approach. Key Takeaways (From Both the Hackathon & Life): ✅ Simple > Complex – clarity and efficiency matter ✅ Ship early, iterate often – perfection can be the enemy of progress ✅ Optimize for impact, not just efficiency ✅ MVP (Minimum Viable Product) > Over-Engineered Solution This principle applies far beyond coding. Sometimes, the best solution isn’t the most complex—it’s the one that works. Have you ever caught yourself over-engineering a problem? Let’s discuss! 🤓

What makes some engineers way better than others? It's not about writing more code or knowing more tools. Everything comes down to effectiveness. But what does that actually mean in practice? Effective engineers don't just do things right. -They do the right things. -They look at the bigger picture. And instead of asking, How do I build this? they ask, Should we even build this? Will it actually provide value to users? They prioritize work that aligns with team goals and creates the biggest impact for users, even when it's not the easiest task on the list. Efficiency is doing things right and effectiveness is doing the right things. One focuses on process, the other on outcomes. When you have both, you're doing the right things right. That's the sweet spot every engineer should aim for. And there's a huge difference between output and outcome. Output is "I wrote 1,000 lines of code" or "I shipped this feature." It tells you what you did, not the value you created. Outcome is the measure of impact. "I improved conversion rate by 15%" or "I reduced errors by 30%." That's matters the most. If you want to grow in your career, stop optimizing for output and start optimizing for outcome.

𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬: 𝐲𝐨𝐮’𝐫𝐞 𝐭𝐡𝐞 𝐚𝐬𝐬𝐞𝐭. You were hired to solve real problems. Give your best hours to the work that moves the business not to noise and you could actually learn how the company system works. 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐟𝐨𝐜𝐮𝐬 𝐭𝐢𝐦𝐞 → claim deep-work blocks; say “heads-down 10 AM to noon.” 𝐀𝐬𝐤 𝐟𝐨𝐫 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 → who’s the user, what’s the goal, constraints, duration, and definition of done. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 → ship value, not activity; cut what doesn’t move a metric soon. 𝐎𝐰𝐧 𝐭𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 → make the call, document one paragraph, close the loop the same day. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 → share one weekly win (faster, safer, cheaper, clearer). 𝐊𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 → run one tiny experiment each week; publish what worked and what didn’t. 𝐀 𝐬𝐢𝐦𝐩𝐥𝐞 𝐰𝐞𝐞𝐤𝐥𝐲 𝐫𝐡𝐲𝐭𝐡𝐦 Mon → pick 1–2 outcomes. Tue–Thu → build and check progress. Fri → share wins, decisions, and the next step. 𝐃𝐨 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤 𝐭𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. 𝐒𝐡𝐨𝐰 𝐢𝐭 𝐞𝐯𝐞𝐫𝐲 𝐰𝐞𝐞𝐤. If you follow this, No one can stop you moving forward to the next step, and get promoted! #SoftwareEngineering #TechLeadership #EngineeringExcellence

Don't confuse velocity with value. Your team is shipping features at record speed, hitting every sprint goal, and clearing the backlog with impressive efficiency. But then what? Silence. No customer feedback. No measurable impact. No real value delivered. 💡 Velocity without driving real value to users is just a waste of time. AI is accelerating how fast we write code, but it's not improving how we make product decisions. If your team is prioritizing output over outcomes, you're just building the wrong things faster. The most successful engineering leaders I know have made this critical shift: ✅ They don't celebrate when code deploys - they celebrate when customers get results ✅ They don't ask "how many story points did we ship?" - they ask "what problem did we solve?" ✅ They don't measure success by velocity - they measure it by customer impact This is the product mindset that separates high-performing engineering teams from the rest. Is your engineering team building features fast or solving customer problems that matter?