Month: November 2024

  • What is Good Code?

    What is Good Code?

    Many junior to mid-level engineers have misconceptions about what “good code” truly mean. Unfortunately, these misunderstandings are often reinforced by flawed hiring practices. LeetCode problems, parroting SOLID principles, or memorizing framework features might showcase technical knowledge, but they don’t inherently make someone a good Software Engineer.

    Mess is Everywhere

    Throughout my career, I’ve encountered my fair share of messy codebases. An example would be functions/methods that stretched over a thousand lines of business logic—an unmaintainable monstrosity. Such code is a hallmark of inexperienced teams and often plagues poorly managed software outsourcing projects. I’ve probably written my share of such code when younger too.

    Seasoned engineers would say that this is “common.” Even at tech giants like Google or Microsoft, codebases aren’t pristine. Messes are inevitable, and documentation can also be inconsistent.

    Still, there’s a difference between an unavoidable mess and a completely avoidable disaster.

    If It Ain’t Broke, Don’t Touch It?

    A few years ago, I was troubleshooting a particularly stubborn issue with another team. The tech lead said that adding more code to an already bloated, thousand-line controller method was risky. He wasn’t wrong—it could take days to figure out where to make even minor changes, and the risk of breaking something was high. To add to the problem, the codebase did not have unit tests.

    But what happened next left me speechless.

    The Undocumented, Untraceable Code

    The tech lead decided to “solve” the issue by writing a standalone PHP script, completely outside the Laravel framework we were using. His justification? Frameworks were “too slow” and “too complicated.”

    His script lived in some random folder on the server, undocumented and untracked. Hours of debugging later, we stumbled upon it by sheer luck. And it wasn’t a one-off—we later found there were several such scripts scattered across the server, mostly undocumented and introducing untraceable logic into production.

    At that point, nobody cared about the quality of his algorithms (they were terrible, by the way) or whether his code followed SOLID principles (it didn’t). The real issue was far worse: he prioritized personal convenience over team collaboration. His decisions created a codebase that was not only a nightmare to maintain but also actively sabotaged the team’s ability to function effectively.

    Coding Beyond Yourself

    As software engineers, we don’t work in silos. Writing code that you alone can understand is easy. Writing code that a hundred others can maintain? That’s the real challenge.

    The example above is a cautionary tale of what not to do. Good engineering isn’t about showing off your technical prowess; it’s about making thoughtful decisions that benefit the team, ensure long-term maintainability, and foster a culture of collaboration.

  • Rethinking Technical Interviews: Lessons from My Experience

    Rethinking Technical Interviews: Lessons from My Experience

    Earlier this year, after being laid off, I went through several interviews for technical roles. These interviews often involved take-home tests, coding assignments, and live coding sessions. While I completed a few, I eventually started declining most of them, finding many to be time-consuming and, frankly, ineffective.

    The Limits of Coding Tests

    Coding tests can serve as a basic filter for entry-level positions, but their value diminishes when applied to senior-level roles. If you’re hiring a Senior Engineer with 10–20 years of experience, coding proficiency isn’t the primary skill to assess—especially in a world where AI tools like ChatGPT can handle many coding tasks faster and more efficiently.

    Instead, the focus should shift to evaluating Problem-Solving, Critical Thinking, Learning Aptitude, and Communication Skills—competencies that I find many interviews overlook. These are the skills that enable senior engineers to lead, adapt, and contribute meaningfully to a team.

    The Core Skills: Problem Solving, Critical Thinking, Learning, and Communication

    These skills apply to candidates across all experience levels. Over the years, I’ve hired many mid-career switchers, often with limited coding backgrounds. People ask how I gauge their suitability, and my approach is simple:

    • Assess their problem-solving ability.
    • Understand their interests and what excites them.
    • Observe the quality of their questions and how well they articulate their thoughts.

    While I do conduct technical screenings to ensure foundational competency, I avoid assigning time-wasting take-home tasks or algorithmic puzzles that don’t reflect real-world job demands.

    Navigating the Era of AI-Assisted Interviews

    The rise of AI tools this year has also transformed interviews. Candidates can now use AI dicatation off-screen to assist with technical questions, making traditional coding tests even less reliable indicators of ability.

    To counter this, I focus on questions AI can’t answer effectively:

    • What are your hobbies?
    • What are you learning now, and why?
    • If you could explore something new tomorrow, what would it be?
    • What’s the most challenging or interesting project you’ve worked on?
    • How would you approach solving this real-world problem based on a scenario?

    These questions help reveal a candidate’s genuine interests, adaptability, and approach to problem-solving.

    The Rapid Pace of Technology

    Over my 20-year career, technology has evolved very quickly. I’ve worked with Turbo Pascal, PERL, Java, PHP, C, C#/.NET, Swift, Python, JavaScript, and countless frameworks, libraries, tools and operating systems. Every shift required adaptability and a willingness to learn.

    A person who can learn and adapt will thrive as technologies, tools, and frameworks continue to change.

    Final Thoughts

    Hiring the right people isn’t about filtering for a specific tech stack or testing for algorithmic skills your team may never need. It’s about finding individuals who can solve problems, adapt quickly, and communicate effectively. Those are the qualities that matter—and they’re what will drive your team forward.

  • How AI will transform Learning and Hiring in Software Development

    How AI will transform Learning and Hiring in Software Development

    The era of Google

    I remember my time as a student back in 1999: a new search engine, Google, was starting to edge out AltaVista and Yahoo — both of which dominated the Internet search landscape throughout the 90s. It was an exciting time for Computer Engineering, as open-source software like Linux was gaining traction in Enterprises. Microsoft and the Java ecosystem were also equipping us with powerful IDEs like Visual Studio, JBuilder, and IntelliJ with features like code completion. Yet, despite these advancements, we still relied heavily on books as primary sources of information. Exams were still handwritten, even for coding: we memorised library functions and syntax, despite having access to the Internet and some fairly advanced tools.

    Our schools insisted on handwritten code and taught us programming using Notepad instead of IDEs. At the time, student laptops were underpowered, and IDEs were huge memory and CPU hogs, running painfully slow. Despite the inconvenience, students like me made every endeavour to set them up, knowing the efficiencies they offered.

    Today, this all sounds ridiculous. With modern IDEs, who would memorise hundreds of function calls or API structures? I actively work across multiple programming languages and frameworks and use countless DevOps tools and certainly can not memorise everything; perhaps it’s age, but it’s nearly impossible to keep all these details in my head.

    The rise of AI

    Twenty-five years on, the world is vastly different, but education systems still lag in adapting to technological shifts. Educators remain cautious about how AI impacts academic integrity and the assessment of written or coding assignments. While AI use isn’t necessarily discouraged, it’s also not fully encouraged; students are however already using AI, much as we once turned to Google and IDEs in place of traditional textbooks.

    For Software Development, AI coding assistants are here to stay, yet they’re unlikely to replace Software Developers soon — especially those who leverage AI-enabled tools. There are stories of startups built by non-technical founders using AI-generated code, but the actual efficacy of purely AI-driven development remains unproven. 

    So, what should we teach students, and how should we evaluate professionals?

    Educational institutions and hiring managers must rethink how they assess skills in this new landscape.

    Developing Strong Fundamentals

    AI models today, especially large language models (LLMs), are not infallible. They can produce erroneous or nonsensical responses. Strong foundational knowledge enables students and professionals to evaluate AI outputs critically and refine prompts to achieve the desired results. From my experience with students using AI-generated code, those lacking fundamentals struggle to interpret the code correctly and often can’t integrate it with existing codebases. For instance, AI models can generate verbose scaffolding or unnecessary details that may disrupt the intended functionality. Without solid fundamentals, these additional complexities create confusion instead of clarity.

    Cultivating Critical Thinking

    Critical thinking is crucial for discerning when and how to use AI solutions effectively. Humans bring valuable contextual knowledge to problem-solving — something not easily available to AI. Tackling complex real-world problems often involves navigating nuances such as geographical, cultural, and political factors. As a simple example, a form with “first and last name” fields may not be culturally appropriate in some countries, especially in parts of Asia where such naming conventions are uncommon. While an AI model might “know” this in theory, it often requires a user’s insight and critical thinking to specify it in an AI prompt when generating a form.

    Mastering Communication

    Although AI can generate well-structured text, effective communication remains a uniquely human skill. Communication goes beyond words to include emotional intelligence, empathy, and cultural sensitivity. AI may one day grow up with us and learn every little bit of detail of our lives (it is a scary thought, but absolutely possible future) but it won’t easily replicate the nuances of face-to-face interactions, emotional cues, or the subtleties involved in team dynamics. Communication skills, therefore, remain vital for collaborating with others and expressing complex ideas in ways that are both clear and motivating.

    Conclusion

    As we move forward, Software Developers will still need to synthesize large amounts of information before even engaging AI. We may no longer need to memorise every technical detail or write every bit of code, but we’ll still need a robust foundation to understand, prompt, and critically evaluate AI-generated responses. AI is reshaping learning and hiring, but the fundamentals of understanding, thinking critically, and communicating effectively will remain core competencies in software development for the foreseeable future.

    This article was also posted on Medium.com:
    https://medium.com/@detach8/how-ai-will-transform-learning-and-hiring-in-software-development-642e3d678cc5