Non Coders Predict Future of Software Engineering

Software engineers wear many hats including, surprisingly, that of an 'astrologer'. Design discussions and code reviews are basically predicting the future - prod incidents, edge cases that it'll fail after X years, nulls, butterfly effects of a bad schema, deadlocks. And I find it beautiful. #SoftwareEngineering #CodeReviews #Engineering #DesignDiscussions #humor

“Make no errors. Don’t hallucinate. Be an expert.” Meanwhile, real-world software engineering: – Ambiguous requirements – Flaky environments – “Works on my machine” – Last-minute scope changes Perfection isn’t the job. Handling imperfection is. If you’ve ever debugged something for hours just to find a missing semicolon… you’re already an expert 😄 What’s your most painful “it was just THIS?!” moment?

In the tech world, we often find ourselves saying things like, “Yeah, I’ll just quickly check one thing.” Fast forward four hours, and we’re deep in a rabbit hole, wondering: - Who the hell wrote this code? - Why was it written this way? - What led me to this moment in my life? Today’s highlight: I resolved a “complex system issue” simply by restarting a service. That’s it. No architectural redesign. No clever algorithm. Just the digital equivalent of: “Have you tried turning it off and on again?” And surprisingly, it worked like it always does. Software engineering keeps you humble. One moment, you're designing scalable systems; the next, you're clicking “restart” as if it’s a personality trait. #TechLife #SoftwareEngineering #Debugging #DeveloperHumor

Well said. In fact, you could argue that no engineering problem was solved by AI. It's not generally good at coming up with novel ideas, all it knows is a remix of what we've seen before. And some problems are being _amplified_ by the throughput gen AI allows.

The only way I know of that minimizes the number of mistakes is if we always start from a broken situation. It works in traditional software engineering, it also works in AI-augmented software engineering. However, in my experience, only a select few software engineers I've had the pleasure to work with know how to make broken systems in a way that the process of fixing the breakages results in a correctly implemented system,. I think that knowledge is the secret sauce, a silver bullet if you will, that can tame those crazy AI agents.

Software engineers were never paid to write code. They’re paid to design systems, make tradeoffs, and unblock teams. Level up by thinking clearer—not typing faster. #softwareengineering #systemdesign #staffengineer #techlead #career

🔧 Distributed Systems: The Invisible Backbone of Modern Software As software engineers, we often talk about features, frameworks, and speed — but rarely enough about what makes large-scale systems actually work: distributed systems design. Here are 5 principles every software engineer should deeply understand: —>CAP Theorem — You can only guarantee two of three: Consistency, Availability, and Partition Tolerance. Know which one your system can sacrifice. —>Idempotency — Design your APIs and services so that retrying a request never causes unintended side effects. Networks fail. Retries happen. —>Eventual Consistency -Not every system needs strong consistency. Understanding when eventual consistency is acceptable unlocks massive scalability gains. —>Failure is the default, not the exception Design for failure first. Circuit breakers, retries, timeouts, and fallbacks are not optional extras. —>Observability over debugging In a distributed system, you can't step through code with a debugger. Invest heavily in logging, tracing, and metrics from day one. The engineers who truly master distributed systems don't just write code that works on their machine they write code that holds up under real-world chaos at scale. What's the hardest distributed systems lesson you've learned? Drop it in the comments 👇 #SoftwareEngineering #DistributedSystems #BackendDevelopment #SystemDesign #TechLeadership

🚨 The unwritten laws of software engineering Most of the lessons that actually matter in software engineering aren’t written down anywhere. You learn them after something breaks in production. A few that always seem to come up. If something breaks after a deploy, it’s probably related to your change. Backups don’t count until you’ve actually restored them. Logs always seem fine until you really need them. Every dependency will fail at some point. And nothing is more permanent than a “temporary fix”. There’s also that classic moment where alerts are firing everywhere and you’re thinking “there’s no way it’s related”… and it is. These aren’t new ideas, but most of us only take them seriously after we’ve felt the pain ourselves. Good engineering isn’t just about building things that work. It’s about building systems that fail safely, recover quickly, and don’t take everything down with them. #SoftwareEngineering #DevOps #SRE #Engineering #Programming #TechLessons

Around 4 years into software engineering, I’ve started noticing a pattern: Writing code is rarely the hardest part. Understanding how things behave in real systems is. Recently, I was working on a backend flow where everything looked fine in development. APIs were fast, logic was clean, no obvious issues. But under load, things started breaking. Not because of incorrect logic but because of how dependencies interacted, how calls were chained and how the system handled pressure. It made me realize: • Code that works ≠ Code that scales • Clean logic ≠ Reliable system • Debugging in distributed systems is more about understanding flow than just fixing errors I think a big part of growing as an engineer is shifting from: “Does my code work?” to: “How does this behave in a real system?” Still learning, but definitely seeing things differently now. #BackendEngineering #SoftwareEngineering #Microservices #Learning

“Coding is going first, then all of Software Engineering” As long as Software is needed neither of those are going away. The only thing that is going to change is the layer of abstraction that SWEs work on, like it has always been…