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Is the future of software engineering not really about engineering anymore? As AI takes on more coding tasks, the role of a software engineer is evolving fast—from coding syntax and optimizing systems toward bigger questions: What should we build? Who benefits? What impact will it have? Product thinking, user experience, and business value are taking center stage, and the traditional focus on code and implementation is starting to feel secondary. Skills like memory optimization, low-level debugging, and pure algorithmic prowess—once prized as the gold standard for engineers—might not be at the heart of the job anymore. So, is this evolution a superpower, or are we losing something fundamental by distancing ourselves from the hands-on problem-solving that made engineering special in the first place? What do you think—are we trading technical expertise for business strategy, and is that a good thing? Can we still call it engineering if it’s mostly about high-level problem framing? 

The 𝗕𝗜𝗚𝗚𝗘𝗦𝗧 Lie in 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 We've been told that mastering 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹𝘀 is the key to success. But that’s only part of the story. The other part? 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, and understanding how your work fits into the 𝗯𝗶𝗴𝗴𝗲𝗿 𝗽𝗶𝗰𝘁𝘂𝗿𝗲. We often joke about 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗿𝘀 being 𝗮𝗻𝘁𝗶-𝘀𝗼𝗰𝗶𝗮𝗹, but it’s no laughing matter. Focusing solely on 𝗰𝗼𝗱𝗲 while neglecting the 𝗵𝘂𝗺𝗮𝗻 side of the job can seriously hold you back. I’ve seen how developing these overlooked skills can transform a career. It’s not just about 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 (though that's crucial), but also about 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 and 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲. Don’t let the lie hold you back. Embrace the skills that truly matter. That's what we do at MilkStraw AI!

Last article of the year 🙌🏾 Is Software Engineering still worth it in 2025? How can you be a better developer in 2025? I share my opinions here 👇 https://lnkd.in/eifex2wJ

AI's impact on software jobs is a complex topic. While it can automate routine tasks, it lacks human skills like creativity and problem-solving. Many in the industry see AI as a tool that can augment the capabilities of software engineers and revolutionize software development processes. Embracing AI as a partner rather than a replacement is key to unlocking the full potential of technology and human expertise in shaping the future of software engineering.

There is a trending study about how there are 9.5% "ghost programmers" in a company. The study was a clear ad, its very biased and has tons of problems. Here are some of the things i found problematic: - The study was done on just 70 commits, hand picked from total "1.73 million commits" that were in the first analysis. - While it said that they selected 10 "java experts", if you see the table, you see 7 of them are either a manager, executive or a VP. ??? - the model had hard time predicting "maintanability" and "experience of the author" - the judges couldnt even agree on what "well structured" commit is, "how maintainable" is - it uses random forest model. Its really naive to think such simplistic model can understand very broad concepts such as "maintanability", "quality". State of the art LLMs even struggle with such tasks. You can read the paper here: https://lnkd.in/dFdWysKs Let me know if you read it and what are you thoughts on the whole situation

To all software engineers reading this, we can reimagine society and create new thinking models for society. We are poised for dominance in the upcoming decades. I foresee the 'Rise of the Software Engineer', a future where our profession and culture are not just important, but integral to the fabric of society. We are the ones who will be elevated to increasingly dominant roles. The ultimate value of a software engineer lies not in coding but in the ability to create informational systems and a structure for the information, including the algorithms for what should be done with such info. Additionally, how the information is abstracted and what interfaces are available at each level of abstraction is a critical component of design, and the software engineer plays a critical role in it. If you think about it, structuring information and its proper abstraction is fundamental to all fields of knowledge. This fact, plus the inherent "hacker mentality" to innovate combined with the rise of AI technology, means that the software engineer is well-equipped to add value, disrupt, and reimagine just about every knowledge domain in human society. As we shape society, it's crucial that we consider the ethical implications of our work. Have we fully thought through the impact of what we can do? How can we ensure that we are truly serving humankind to create a better world for all? These are important questions that we will all collectively participate in.

Software Engineering field is not only about writing code; it’s about solving problems systematically and creatively.One fascinating aspect is how software engineering integrates with many disciplines like psychology, for instance, to understand how users interact with software, and mathematics, to create algorithms that process data efficiently. Moreover, as systems scale, software engineers must tackle challenges like handling massive amounts of data, ensuring system security, and achieving high performance and fault tolerance.

We are one step closer to completing the automation of software engineering.

How Mastering Sorting Algorithms Changed My Approach to Problem-Solving in Software Engineering Early in my career, I thought sorting algorithms were just theoretical exercises. But mastering them has been one of the most valuable skills I’ve developed as a software engineer. 1. Optimizing Performance 🏎️ Efficient sorting is often the difference between an application that just works and one that excels. From speeding up database queries to processing massive data streams, knowing how and when to implement the right sort can drastically improve application speed and responsiveness. 2. Cracking Coding Interviews 💡 😊 Sorting problems pop up frequently in coding interviews because they reveal a lot about a candidate’s ability to think critically and optimize. Knowing the ins and outs of algorithms like Quick Sort, Merge Sort, and Heap Sort has given me confidence in interviews and the ability to break down complex problems quickly. 3. Foundations of Data Structure Mastery 🧱 Sorting goes hand-in-hand with other fundamental data structures—like arrays, linked lists, and trees. Building efficient code often means understanding the relationship between data structures and sorting algorithms. Mastering these basics helped me become more versatile and adaptable to complex tasks. A Tip for Software Engineers 🚀 Even if you’re focusing on high-level programming, understanding these foundational algorithms can elevate your problem-solving approach and your ability to write optimized, scalable code. The deeper I dive into these "basics," the more I appreciate their relevance and impact in real-world software engineering.

This AI Paper Introduces a Comprehensive Framework for LLM-Driven Software Engineering Tasks