14 posts tagged with "coding"

Debugging feels like a power skill in the current age of AI-assisted development and vibe coding. With so much focus on speed, auto-complete, and generated snippets, the discipline of carefully tracing through a problem seems to be fading. Yet it is debugging that ultimately separates functional systems from brittle ones. Breakpoints, step-through execution, and understanding stack traces used to be routine practices, but now they feel like skills only a few consistently use. The shift toward trusting AI for answers is useful, but it also risks eroding the muscle memory of working patiently with a debugger.

When I think about breakpoints, I remember how essential they once were in learning to think like a machine. Setting a breakpoint forced me to stop the code at a precise location, inspect variables, and see not just the output but the process that created it. That kind of visibility shaped intuition about program flow in a way that no explanation or documentation could match. Without this stepwise exploration, bugs often remain hidden or get patched superficially. It is a reminder that debugging is less about fixing errors quickly and more about understanding how the system behaves under different conditions.

In contrast, vibe coding—building by intuition, trial, and generated code—creates momentum but can leave gaps in understanding. AI can produce code that runs, but when it fails, the burden still falls on the developer to trace the problem. This is where debugging remains a core skill. The ability to navigate an unfamiliar codebase, set conditions, and monitor behavior systematically is something AI cannot fully replace. It requires discipline, patience, and awareness of the underlying system. Debugging teaches a way of thinking that survives across languages and frameworks, and that is why it holds power even as tools evolve.

Another part of debugging’s strength lies in how it builds confidence. Running code that works without knowing why always carries a sense of risk. Debugging removes that uncertainty by showing what happens step by step. It reduces reliance on guesswork and makes it possible to handle complex systems with clarity. The same process also develops habits of observation and logical reasoning that extend beyond programming. Whether it is tracing a performance bottleneck or investigating unexpected behavior, debugging provides a framework for problem-solving that is transferable to many contexts.

In the end, debugging is not just a technical exercise but a discipline of thought. Remembering how to set breakpoints and use them effectively feels almost old-fashioned now, but it is precisely this habit that strengthens developers in an era dominated by quick solutions. AI can write and suggest, but debugging ensures that we still understand. It is a quiet skill, often undervalued, yet it continues to carry weight because the real measure of a developer is not just in writing code but in handling what happens when it does not work as expected.

At Edzy we recently ran our first design hackathon, focused on UI and UX challenges, and it turned into an experience that stood out from the usual work rhythm. We kept the format simple, with three design sprints assigned as tasks, but the energy it created throughout the day was something new. A few candidates were invited to work on Figma designs and quick prototypes, and watching ideas take shape in such short cycles showed how much momentum can build when structure and time pressure are combined. It was not only about producing results but also about observing how different people approach design under constraints.

The sprint format gave a clear shape to the day. Each task was limited in scope but wide enough to allow creativity, which meant no one could afford to get stuck on details. This constraint encouraged sharper thinking and faster decisions. Design in longer timelines often allows room for hesitation, but here the sprints demanded focus. Seeing a team move from concept to wireframe in a short burst underlined why hackathons work—they generate urgency that can cut through overthinking. The output was not always polished, but it carried raw clarity, which often gets lost in slower cycles.

Using Figma as the central tool added structure. Because everything was in one shared space, it became easy to follow progress and compare approaches. Prototyping within hours showed how ideas translate from abstract requirements into something visual and functional. For the candidates, it was a chance to showcase not only technical skill but adaptability. For us, it was an opportunity to see how design thinking looks when stripped of long processes and committees. The immediacy of working side by side with designers gave insights that interviews or portfolios rarely provide.

What surprised me most was the atmosphere that formed naturally. There was no need for constant direction because the pace of the sprints carried its own energy. People were engaged, focused, and occasionally playful with ideas, which kept the environment alive. A hackathon does not run on deadlines alone; it also depends on participants feeling they can experiment. That balance of pressure and freedom made the event feel worthwhile. It was clear by the end that we had learned as much from observing the process as from the final designs themselves.

Looking back, the first design hackathon at Edzy was more than a trial run. It became a template for how we might engage with talent and ideas in the future. The format showed that creativity can be structured without being stifled, and that short bursts of intense focus often bring more insight than long, stretched projects. The experience proved that energy is created not only by the tasks but by the people who take them on and the space given to them to build quickly. It left me convinced that hackathons, even in small settings, carry value far beyond the prototypes they produce.

The experience of organizing a frontend hackathon makes me reflect on how coding interviews have changed in the last few years. Tools like Cursor, Copilot, and large language models have become central to how developers approach writing code. In the past, an interview would often test the ability to recall syntax, implement algorithms from scratch, and debug without external help. Today, the process is different because the default assumption is that these tools exist and can be used. This changes both the expectations of what a candidate can demonstrate and the meaning of practical coding skills.

The reliance on AI assistance shifts the focus from memorization to orchestration. A developer is now less about typing every line correctly and more about structuring the problem, guiding the tool toward a solution, and knowing when the output makes sense or fails. This is a useful shift because in real work environments, nobody writes code in a vacuum. At the same time, it raises questions about what is being measured in interviews. If the goal is to assess the depth of understanding, then giving space for debugging sessions or architectural discussions may be more revealing than timed implementation exercises where AI fills the gaps.

Debugging remains the skill that separates surface-level competence from real problem-solving. Even with LLMs generating code, the ability to trace why something is failing, how dependencies interact, and where the logic breaks cannot be outsourced fully. A candidate who only knows how to prompt tools without verifying or correcting results will struggle when systems behave unexpectedly. This is why hackathons often expose more about how someone thinks under pressure than about their ability to deliver a polished product. The code may be partially AI-generated, but the process of integrating, fixing, and deploying shows whether the person understands what is happening.

Another effect of this shift is that interviews emphasizing data structure puzzles or abstract algorithms feel less relevant. They were never a perfect proxy for practical software development, but now they are even further removed from reality. The interview formats that align more closely with actual workflows—building small features, improving existing code, or designing components—seem better suited for evaluating ability. This does not eliminate the need for theoretical grounding, but it acknowledges that knowing how to apply it in an environment rich with automation is what matters most.

Looking ahead, the question is not whether these tools will stay but how hiring processes adapt. A fair interview in today’s context should test how someone uses AI responsibly, how they debug when AI is wrong, and how they design for maintainability. Conducting the frontend hackathon reminded me that the measure of a developer is shifting from rote execution toward judgment, clarity of thought, and the capacity to make sense of complexity. Coding interviews will have to reflect that reality if they want to remain meaningful.

Last night turned into one of those extended work sessions where time blurs into a single stretch of coding, writing, and planning. By the time I finshed, it was 5 AM, and the only sound left was the occasional hum of a distant car. There’s something about these late-night sprints that condenses what would normally take days into hours. Maybe it’s the lack of distractions, or maybe it’s just the momentum of being deep in the zone. Either way, progress happens in these bursts, uneven but undeniable.

Edzy, the gamified AI tutor I’m building, is at a stage where everything feels both urgent and unfinished. The product outline needs refining, emails to potential collaborators sit half-written, and the codebase has gaps that need filling. The scope is wide enough that it’s easy to jump between tasks without finishing any, but last night was different. One feature led to another, and before I knew it, the foundational logic for user progress tracking was in place. It’s not polished, but it’s there—something to build on.

Working late isn’t sustainable, but it’s not always about exhaustion. There’s a clarity that comes when the world is quiet, when the pressure of immediate replies and meetings fades. Last night was less about grinding and more about flow—the kind where ideas connect without force, where the next step seems obvious instead of uncertain. That’s rare, and when it happens, it’s worth leaning into, even if it means resetting sleep for a day.

By 8:30 AM, I was back on calls, shifting from solitary work to conversations with others. The contrast is sharp—switching from deep focus to the scattered energy of meetings, emails, and quick decisions. It’s not ideal, but it’s part of the process. Building something new means balancing both modes: the long, uninterrupted stretches and the rapid back-and-forth of coordination. Neither works alone.

These phases come and go. Right now, the momentum is high, and the list of tasks is longer than the hours available. But that’s how it always is at the start. The key is to keep moving, even if some days look like 5 AM finishes and 8:30 AM starts. The alternative—waiting for the perfect rhythm—means not moving at all.

The time it takes to build software is compressing, thanks to AI and better tooling. This means the most valuable hours of the day should be spent on the highest-leverage activity—writing code. For me, that means blocking the first chunk of the day, starting at 8:30 AM, exclusively for shipping features, debugging, or refining architecture. Once that’s done, other tasks—like writing emails, pitching investors, or handling stakeholder communication—can follow. The reasoning is simple: code directly impacts the product, while most other tasks are secondary. If the product isn’t moving forward, nothing else matters.

One may fall into the trap of starting their day with reactive work—clearing inboxes, responding to messages, or attending early meetings. The problem is that these tasks drain mental energy without moving the needle. Writing code requires deep focus, and the best time for that is when the mind is fresh. By deferring communication to later in the day, I ensure that the most important work gets done first. This isn’t about ignoring stakeholders but about recognizing that a functional product is the best pitch.

There’s another advantage to this approach: momentum. Shipping code early creates a sense of progress, which makes handling administrative tasks later feel less burdensome. The psychological shift is significant—instead of ending the day wondering if meaningful work got done, there’s tangible output. This also aligns with how creativity and problem-solving work; the brain is sharper in the morning. By reserving that time for coding, the quality of work improves, reducing the need for revisions or debugging later.

Some might argue that stakeholder communication is equally urgent, but urgency doesn’t always align with importance. Early-stage investors, for example, expect execution above all else. A concise update with real progress is more valuable than a lengthy email with little to show. The same applies to internal communication—teams benefit more from a working feature than a status update. The key is to structure the day so that the highest-impact work happens when cognitive resources are at their peak. Everything else can wait.

Debugging is a fundamental skill that extends beyond just fixing software errors—it applies to solving complex business problems, logical inconsistencies, and even real-world decision-making. The core of debugging lies in structured thinking, where the problem is broken down into smaller, manageable parts. This requires patience, observation, and a methodical approach. The first step is always to isolate the issue, whether it’s a bug in code or a bottleneck in a business process. Replicating the problem consistently is crucial because without a clear understanding of when and why the issue occurs, any solution will be speculative rather than definitive. Debugging is not just about finding what’s broken but understanding why it broke in the first place.

Logical thinking is the backbone of effective debugging. Every problem, whether technical or operational, follows a cause-and-effect chain. The ability to trace this chain backward—from symptom to root cause—is what separates quick fixes from lasting solutions. This often means stepping back to analyze the system as a whole rather than focusing on immediate symptoms. For example, a crashing application might seem like a coding error, but the real issue could be an underlying resource constraint or an unexpected data input. Similarly, in business, declining sales may appear to be a marketing issue, but the root cause could be supply chain inefficiencies or customer service gaps. The key is to ask the right questions rather than jumping to conclusions.

Debugging is a process of elimination, often requiring a "step back and two steps ahead" mindset. The step back involves distancing oneself from assumptions and biases to see the problem objectively. The two steps ahead come from anticipating how changes will affect the system. In coding, this means considering edge cases and regression impacts. In business, it means evaluating second-order consequences of decisions. A common mistake is applying quick patches without understanding downstream effects, leading to recurring issues. True debugging involves not just fixing the immediate problem but ensuring it doesn’t resurface in a different form. This requires a balance of short-term resolution and long-term system resilience.

The thinking required for debugging is both analytical and creative. Analytical thinking helps in systematically narrowing down possibilities, while creativity allows for unconventional approaches when standard solutions fail. It’s about pattern recognition—identifying similarities between past and present problems—and adaptability, knowing when to pivot strategies. The best debuggers are those who treat every problem as a learning opportunity, refining their approach with each iteration. Whether in code or business, the principles remain the same: observe, hypothesize, test, and refine. Mastery comes not from never making mistakes but from knowing how to diagnose and resolve them efficiently.

Telegram has become a popular platform for students and professionals alike, often used for sharing resources like free books, movies, and other digital content. While the ethical and legal concerns around copyright violations on Telegram are worth noting, the platform’s technical flexibility is undeniable. One of its standout features is the ease with which you can create and deploy bots. Unlike WhatsApp, which has stricter limitations and a more complex API, Telegram offers a straightforward and developer-friendly environment for building bots. This simplicity makes it an excellent choice for anyone looking to automate tasks, share information, or create interactive tools without diving into overly complex coding.

Creating a Telegram bot is surprisingly simple, even for those with minimal programming experience. The process begins with setting up a bot through Telegram’s BotFather, which generates a unique API token for your bot. This token acts as the key to integrating your bot with Telegram’s API. From there, you can use a variety of programming languages, such as Python, to define the bot’s functionality. Python, in particular, is well-suited for this task due to its readability and the availability of libraries like python-telegram-bot, which streamline the development process. With just a few lines of code, you can create a bot that responds to commands, sends automated messages, or even interacts with external APIs to fetch data.

The versatility of Telegram bots is another reason they are so appealing. For instance, you can build a bot that helps students find and download free educational resources, organizes study groups, or even sends daily reminders for assignments. The possibilities are vast, limited only by your imagination and coding skills. What makes Telegram stand out is its open approach to bot development. Unlike WhatsApp, which requires businesses to go through a lengthy approval process for certain bot functionalities, Telegram allows developers to experiment and deploy bots with minimal restrictions. This openness fosters creativity and innovation, making it a preferred platform for developers and users alike.

Bots that facilitate the sharing of copyrighted material or engage in unethical practices can have serious consequences. However, when used thoughtfully, Telegram bots can be powerful tools for productivity, education, and community building. The process of creating one is not only straightforward but also rewarding, offering a practical introduction to coding and automation. If you’ve ever considered building a bot, Telegram is an excellent place to start. Its simplicity and flexibility make it an ideal platform for turning ideas into functional tools with minimal effort.

In frontend development, new tools and libraries constantly emerge to simplify the process of creating beautiful, functional user interfaces. One such tool that has gained significant traction in recent months is Shadcn UI, a collection of reusable components created by the developer known as shadcn. This library has quickly become a go-to resource for many developers, offering a blend of flexibility, performance, and ease of use that sets it apart from many alternatives. Shadcn UI is not a traditional component library in the sense that you might expect. Rather than being a package that you install via npm, it's a collection of components that you can copy and paste into your project. This approach gives developers full control over the components they use, allowing for easy customization and optimization. The components are built using Radix UI primitives and styled with Tailwind CSS, providing a solid foundation for creating accessible and visually appealing interfaces.

One of the key strengths of Shadcn UI is its focus on developer experience. The components are well-documented and easy to understand, making it simple for developers of all skill levels to integrate them into their projects. Additionally, the library is designed to be framework-agnostic, meaning it can be used with various frontend frameworks like Next.js, Gatsby, or even vanilla React. The rise of Shadcn UI is part of a broader trend in the frontend development community towards more modular, customizable tools. Developers are increasingly looking for solutions that offer flexibility and control, rather than monolithic libraries that dictate the entire structure of an application. This shift is driven by a desire for better performance, smaller bundle sizes, and the ability to create unique user experiences that aren't constrained by the limitations of a particular framework or library.

While Shadcn UI has garnered much attention, it's important to recognize that it builds upon the work of other influential projects in the JavaScript ecosystem. One such project is Radix UI, a collection of low-level UI primitives that form the foundation for many of Shadcn UI's components. Radix UI, developed by Workos, provides unstyled, accessible components that developers can use as building blocks for their own design systems. Radix UI's approach to component design emphasizes accessibility and customization. By providing unstyled components, Radix UI allows developers to implement their own design language while ensuring that the underlying functionality and accessibility features are robust and well-tested. This philosophy aligns well with the goals of Shadcn UI, which uses Radix UI primitives as a starting point for creating more opinionated, styled components.

The success of projects like Shadcn UI, Radix UI, and Vaul highlights a shift in how developers approach frontend development. Rather than relying on monolithic frameworks or all-encompassing component libraries, many developers are now opting for a more modular approach. This involves combining specialized tools and libraries to create custom solutions that perfectly fit their project requirements. This modular approach offers several advantages. First, it allows developers to choose the best tool for each specific task, rather than being locked into a single ecosystem. Second, it promotes a deeper understanding of the underlying technologies, as developers must integrate different tools and resolve any conflicts or inconsistencies. Finally, it often results in more performant applications, as developers can include only the components and functionality they need, rather than importing an entire library. However, this approach also comes with challenges. The abundance of choices can be overwhelming, especially for less experienced developers. It requires a good understanding of various tools and how they interact with each other. Additionally, maintaining a project that uses multiple libraries and tools can be more complex than working within a single, well-defined ecosystem.

Despite these challenges, the popularity of libraries like Shadcn UI suggests that many developers find the benefits of this modular approach outweigh the drawbacks. The ability to create unique, performant user interfaces without being constrained by the limitations of a particular framework is a powerful draw for many developers. Looking to the future, it's likely that we'll see continued growth in this ecosystem of specialized, modular tools for frontend development. As web applications become more complex and user expectations for performance and interactivity continue to rise, developers will need increasingly sophisticated tools to meet these demands. At the same time, we may see efforts to standardize and consolidate some of these tools. Projects like Shadcn UI, which build upon and integrate other libraries and tools, could play an important role in this process. By providing a curated set of components and utilities that work well together, these projects can offer a middle ground between the flexibility of a fully modular approach and the convenience of a comprehensive framework.

For those looking to stay at the forefront of frontend development, exploring tools like Shadcn UI, Radix UI, and Vaul is certainly worthwhile. These libraries not only offer powerful capabilities for building modern user interfaces but also provide insights into current best practices and trends in the field. As with any tool, the key is to understand its strengths and limitations, and to choose the right tool for each specific project and use case.

In the modern web development, animations have become an integral part of creating engaging and immersive user experiences. While traditional CSS animations have served us well, modern frontend frameworks demand more powerful and flexible solutions. Enter Framer Motion – a remarkable library that has revolutionized the way we approach animations in frontend development.

Framer Motion: A Game-Changer for Frontend Animations​

Developed by the team at Framer, Framer Motion is a production-ready library built for React that offers a comprehensive suite of tools and features for creating stunning animations. Its declarative syntax and intuitive API make it a joy to work with, allowing developers to seamlessly integrate animations into their React components with minimal boilerplate code.

One of the key advantages of Framer Motion is its ability to leverage the power of React's component lifecycle methods, enabling developers to easily orchestrate animations based on state changes or user interactions. This tight integration with React's ecosystem ensures a smooth and consistent experience across all components.

Simplicity at Its Core​

Framer Motion's simplicity is one of its biggest selling points. With a concise and expressive syntax, developers can define complex animations using straightforward markup. The library's extensive documentation and robust community support make it accessible even for those new to frontend animations.

Unleashing Creative Potential​

Framer Motion's versatility extends beyond simple transitions and transforms. It supports complex animations involving SVG elements, gesture-based interactions, and even physics-based simulations. With its powerful interpolation capabilities, developers can create fluid and natural-looking animations that respond dynamically to user input or data changes.

Furthermore, Framer Motion plays well with other popular React libraries and frameworks, making it a seamless addition to any existing codebase. Whether you're building a sleek portfolio website, a dynamic e-commerce platform, or a cutting-edge data visualization tool, Framer Motion empowers you to elevate your frontend animations to new heights. In the realm of frontend development, animations are no longer just a nice-to-have feature; they are essential for creating captivating and engaging user experiences. Framer Motion, coupled with the power of Hover.dev, has emerged as a game-changing solution for developers seeking to unlock the full potential of frontend animations.

Long coding session can feel like diving into an ocean of possibilities. However, the deeper you swim, the more likely you are to encounter unintended bugs lurking beneath the surface. During coding, these bugs are the shadows cast by prolonged screen time, disrupting not only your code but also the coveted flow state achieved during intense coding sessions.

The Unseen Culprits - Unintended Bugs in Code​

Long coding sessions often introduce unintended bugs into the codebase. The more lines you write, the higher the chances of overlooking human error, or an unnoticed logical flaw. These bugs, while seemingly trivial, can have a cascading effect, causing frustration, delays, and, in worst cases, a breakdown of the entire system.

Optimal Coding Time and Programmer Productivity​

Studies suggest that the optimal time for a coding session is around 25-30 minutes, with a short break afterward. The Pomodoro Technique, a time management method, advocates for such structured intervals to maintain focus and prevent burnout. Beyond this timeframe, cognitive fatigue sets in, leading to decreased productivity and an increased likelihood of introducing errors.

Measuring Programmer Productivity - Beyond Lines of Code​

While lines of code written per day may be a traditional metric, it often falls short in gauging true productivity. Metrics like code review feedback, issue resolution time, and the ability to meet project milestones provide a more comprehensive picture. Balancing productivity with quality is the key to sustainable progress.

GitHub Copilot - A Coding Companion​

Intelligent tools like GitHub Copilot aim to mitigate coding challenges. Leveraging machine learning, Copilot assists in generating code snippets, reducing the risk of common errors introduced during coding marathons. While not flawless, it has become a valuable ally in the coder's toolkit.

The Flow State - Fragile yet Powerful​

Achieving the flow state is a delicate dance between challenge and skill. Unintended bugs disrupt this delicate equilibrium, leading to a lapse in concentration. Regaining the flow becomes a formidable task, as frustration replaces the previously seamless coding experience. Striking a balance between productivity and mental well-being is paramount. As we navigate the coding maze, let's embrace structured coding intervals, leverage intelligent tools, and prioritize the delicate dance of achieving and maintaining the elusive flow state.