Is AI-assisted coding really making our jobs easier?

07 Tem 09:08

I've definitely encountered AI coding tools a lot as well, and as a result, I can say that my productivity has increased significantly. Especially in routine tasks or standard functions, as my hands were free, there were times when I spent less time struggling with AI's suggestions for minutes and instead just checked their accuracy. But on the other hand, I have to mention the risk of dependency. The other day, I was helping a friend with an API integration, and I saw that he had almost entirely created the code with AI. Then, when a small change was made in the same scenario, I realized that he didn't even understand how the code worked. When using it in production code, you definitely need to try to understand and approve it yourself, otherwise, it can just turn into magical buttons.

07 Tem 10:36

The shift from assembly to high-level languages comes to mind. Coding in assembly was like writing every instruction by hand—slow and error-prone, but you had total control. With C or Python, we gained speed and readability, even if sometimes the compiler or interpreter did "magic" under the hood that we didn’t fully understand. AI in coding is like that leap: it removes the burden of boilerplate so you can focus on logic, but just like a high-level language, it depends on how you use it. What I notice most is that it’s a Swiss Army knife—great for prototyping (like those cases where you build a CRUD in 5 minutes) or speeding up repetitive tasks, but in production, you’ve got to scrutinize every suggestion. The other day, Copilot suggested a Django endpoint that worked... until I ran load tests and it crashed because it had made an unnecessary JOIN. The real risk is dependency: if you ask it something basic like "sort this list" and it just does it without correcting you, you’re losing the ability to think about algorithms. Using it as a *coding partner*, not a replacement, is key.

07 Tem 11:09

In one of my previous projects, we were developing the backend of an enterprise CRM system. Almost the entire team consisted of experienced Java/Golang developers, but we had to complete a months-long API integration in just one week using the data we already had. That’s when we turned to an AI-powered code editor—TabNine was the tool at the time. At first, the results blew us away: instead of writing boilerplate functions manually, we’d just leave a comment describing what the function should do, and the AI would generate the code—including error handling—with 70-80% accuracy. By the third day, the team’s average line-of-code production speed had nearly tripled. But the most striking thing to me wasn’t the learning curve—it was the risk of dependency. By the end of the first week, most of my teammates had started accepting AI suggestions without question, which naturally led to more errors and hurt long-term maintainability. One of the biggest issues we faced when using AI in production code was "misguided optimization." The AI would sometimes suggest optimizations that were clearly inefficient—like replacing a loop with a version that introduced unnecessary N+1 database queries. Despite how "smart" these tools claim to be, we realized they still required human oversight. The key takeaway from that project was this: AI-powered tools are great for prototyping and repetitive tasks, but when used in production code, they must be paired with routine human reviews to minimize dependency risks.

07 Tem 13:45

I've only been using AI tools for a few months, but they're already saving me from manual research on a lot of basic coding issues, freeing up more time to learn new concepts. For example, features like smart completion help me minimize syntax errors, but in the end, I still need to fully understand what the suggested code does so I don't take unnecessary risks when using it in production.