Coded By RT’s Post

Production Issues Taught Me More Than Coding Ever Did Writing code is one thing… But debugging a production issue at 2 AM? That’s where real engineering begins. ⚠️ Lessons learned the hard way: 🔹 A tiny race condition can break an entire system under real traffic 🔹 Missing timeouts or retries can cascade failures across microservices 🔹 Cache inconsistency can show users incorrect or outdated data 🔹 One slow dependency can create a domino effect across services 🔹 Without proper logging & monitoring, you’re debugging blind ⚡ What changed in my approach: Started designing systems with failure in mind Focused on resilience over just functionality Built APIs to be idempotent and fault-tolerant Invested heavily in observability (logs, metrics, tracing) Real Insight In real-world systems: It’s not about if something fails It’s about how well your system handles it Closing Thought Clean code gets you to production. Resilient systems keep you in production. #SoftwareEngineering #SystemDesign #Microservices #BackendDevelopment #EngineeringLessons #Tech

Have you ever spent hours building a “smart” solution only to realize a simpler one would have worked better? Overengineering is a common trap in software engineering. The intention is usually good. You want to write flexible code, handle every possible edge case, and design something that can scale for the future. But in the process, something gets lost: clarity. Overengineered solutions often come with unnecessary abstraction or increased complexity. What started as a solution becomes something difficult to understand, maintain, and extend. In many cases, the simplest solution solves the problem faster, is easier to debug, and can still evolve when needed. Good engineering isn’t about how advanced the solution looks. It’s about how effectively it solves the problem. Overengineering doesn’t just slow development. It increases long-term cost. Simplicity, when done well, scales better than complexity. Follow for more. . . . #SoftwareEngineering #CleanCode #DeveloperMindset #EngineeringCulture #SystemDesign #CodeQuality #TechCareers #ProblemSolving

Software doesn’t usually fail in production because of “big bugs.” It fails because of small decisions that compound over time. A few patterns that show up everywhere: → A quick workaround that becomes permanent → A dependency added “just for now” that never leaves → A feature shipped without understanding edge cases → A system that works… until real users hit it Most incidents aren’t surprises. They’re predictable outcomes of accumulated shortcuts. Some reality: - A large percentage of engineering time is spent on maintenance, not new features - Many outages trace back to changes that seemed safe at the time - Complexity grows silently, until it suddenly becomes a problem The scary part? Everything looks fine… right up until it breaks. Good engineering isn’t about avoiding mistakes. It’s about building systems that don’t collapse because of them. 💬 What’s a “small decision” you’ve seen turn into a big problem later?

Debugging production issues teaches you more than building features ever will. In development, everything is controlled. In production, nothing is. I’ve seen issues that: ❌ Couldn’t be reproduced locally. ⚠️ Only happened under specific user behavior. 🙃 Disappeared when logs were added. That’s when you realize: Writing code is one skill. Understanding systems is another. Good engineers fix bugs. Experienced engineers design systems where bugs are easier to trace. Logging, monitoring, and observability aren’t “extra work.” They’re part of the product. Because when things fail and they will. clarity matters more than speed. 👉 What’s the most confusing bug you’ve faced in production? #Debugging #SoftwareEngineering #Flutter #SystemDesign #DevLife

The hardest part of engineering isn’t coding. It’s deciding what not to build. Every system could be more scalable, more flexible, more “future-proof”. But every improvement has a cost: time, complexity, maintenance. Strong engineers don’t chase perfect systems. They optimize for current needs, known risks, and realistic growth. Because overengineering is just delayed failure with extra steps. #EngineeringLeadership #SoftwareEngineering #SystemDesign #TechLeadership #ScalableSystems #EngineeringMindset

While working through software code stability issues, I gained a much deeper perspective on what software quality truly means. At one point, issues stop becoming just “tickets” or “bugs” — they start representing real user frustrations, interruptions, safety concerns, and loss of trust. This experience made me empathize far more with the end user and understand the true responsibility engineers carry. One thing this journey reinforced strongly: “Code is the truth.” No presentation, assumption, or explanation can hide instability when software reaches real users. Eventually, the code reflects the true quality, discipline, and engineering maturity behind the product. Software stability reminds me a lot of the medical field. Just as a doctor’s responsibility is not only to diagnose but also to ensure patient safety and long-term well-being, engineers too must build systems that are reliable, stable, and trustworthy. A small oversight in medicine can impact a patient’s life; similarly, a small software instability can create significant real-world impact for customers and systems. Features may attract attention, but stability builds trust. Solving stability issues taught me: • Root cause thinking matters more than temporary fixes • Prevention is more valuable than repeated correction • Robust validation and testing are essential • Engineering empathy is as important as technical expertise • Stability reflects the maturity of both the product and the team Great engineering is not just about innovation — it is about building dependable systems that people can confidently rely on every day. #SoftwareEngineering #CodeQuality #SoftwareStability #Engineering #ProblemSolving #EmbeddedSystems #ContinuousImprovement #Leadership #CustomerCentricity

Error Handling Is A Senior-Level Skill One thing I’ve come to believe strongly: Error handling is a senior-level skill. A lot of engineers can build the happy path. Fewer can design for the messy path. That difference shows up very quickly in production. The strongest engineers don’t just ask, “Will this work?” They ask: -What happens when this dependency times out? -What happens when the payload is malformed? -What happens when the retry makes things worse? -What happens when the user does the right thing, but the system partially fails? That mindset is what separates code that demos well from systems that survive real usage. Good error handling is not just try/catch. It is: -Returning useful, consistent error responses -Logging enough context without creating noise -Preserving debuggability under pressure -Handling retries deliberately -Avoiding silent failures -Designing idempotent operations -Knowing when to fail fast and when to degrade gracefully In my experience, weak error handling creates hidden costs: -Slower incident response -Confused users -Fragile integrations -Hard-to-reproduce bugs -Teams that spend more time firefighting than building Strong error handling does the opposite. It creates trust. Trust from users. Trust from teammates. Trust that the system can take a hit and still behave predictably. Senior engineers are not defined only by how well they build success paths. They are defined by how well they anticipate failure. Because in production, failure is not the edge case. It is part of the system. What’s one error-handling principle you think more engineers should take seriously? #SoftwareEngineering #BackendDevelopment #SystemDesign #ErrorHandling #TechLeadership #Engineering #Developers #CleanCode #Reliability #ScalableSystems

A team I saw spent 3 days debugging intermittent 500 errors. 3 engineers. Reading code line by line. Going through every service, every endpoint. Nobody found anything. Then someone new joined the call and asked one question: "Did anything change in the last week?" Turns out a config change had reduced a connection timeout from 30 seconds to 3 seconds. Writes to a slow external service were timing out. Cascading failures downstream. The fix: revert one config value. 2 minutes. 3 days of code reading. 2 minutes of behavior observation. This is the pattern I see over and over: → Teams debug by reading code → The bug isn't in the code → It's in a config, a dependency, a traffic pattern, an infrastructure change 90% of production issues trace back to something that changed. Not something that was written wrong. Before opening any file, ask: "What's different between when it worked and when it didn't?" That one question is worth more than 10,000 lines of code review. Skill #4 of 12 AI-proof engineering skills. → Follow for the full series. — #AIProofSkills #SoftwareEngineering #Debugging #ProductionDebugging #EngineeringLessons #SystemsThinking #Engineering #BuildInPublic

What I wish someone told me earlier in my engineering career: The best code you'll ever write is the code you delete.                                Early on, I measured progress by lines added. Features shipped. Pull requests merged. But over time, I learned that the engineers I admired most were the ones who could look at a 500-line file and say, "We don't need 400 of these."     Simplicity isn't a shortcut — it's a skill. It takes more effort to write something clean and minimal than to pile on abstractions "just in case." Three things that changed how I build software:                                    1. Solve the problem in front of you. Not the one you think might show up in six months.                  2. Read more code than you write. Understanding existing systems is underrated. 3. Optimize for the next person. That person is usually future you, at 2am, during an incident.                                                                            Ship less. Delete more. Keep it boring.                                                                                                        What's a lesson that took you too long to learn?

The day I broke production, I learned something no code review ever taught me. Production is not your local machine. Small change. Looked perfect in testing. Pushed it with confidence. A few minutes later, requests slowing down, actions failing, data looking off. No obvious error. No clear culprit. I went back to my code. Everything looked fine. And that was exactly the problem. In development, you control everything. Clean data. One user. No competing processes. No real load. Production doesn't care about any of that. Real users hit your system in ways you never anticipated. Edge cases you couldn't simulate. Timing issues that never showed up locally. Load that exposes every assumption you silently made. That incident shifted something in how I build. I stopped treating a passing test as proof something works. I started asking: how does this behave when the conditions aren't ideal? When there's load, unexpected input, or two things happening at once? The code didn't fail. It exposed how different production can be. What's a production failure that changed how you think as an engineer? #SoftwareEngineering #Production #Debugging #DevLife #Programming #Backend #FullStack #EngineeringLife #TechLessons #DevTips #SRE