Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories.
So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests.
My name is Emmimal P Alexander. I’m an AI engineer, technical writer, and founder of EmiTechLogic, where I publish practical guides on Python, machine learning, and modern AI systems.
My work focuses on making complex AI and software engineering concepts understandable for developers. I spend most of my time building experiments, writing technical articles, and exploring how neural networks and AI infrastructure actually behave in real systems.
I’m also the author of two books: “Agentic AI for Executives,” which explores how organizations can adopt autonomous AI systems in practical ways, and “Neural Network and Deep Learning with Python: A Practical Approach,” which focuses on explaining neural networks through hands-on implementation and clear technical explanations.
Interesting! What was your latest Hackernoon Top Story about?
My latest story, “What Happens if You Remove ReLU From a Deep Neural Network?”, was a technical deep dive based on a real debugging experience.
In the article, I described a situation where a neural network was trained without any errors but still failed to learn. By carefully analyzing the model’s behavior, I eventually discovered that the issue was related to missing activation functions.
The story focuses on how small architectural decisions—like activation functions—can dramatically affect the learning capability of deep neural networks.
Do you usually write on similar topics? If not, what do you usually write about?
Yes, I primarily write about AI, specifically deep learning, neural networks, and practical, hands-on experiences with Python-based AI frameworks.
I enjoy writing about the practical side of AI, especially the gap between academic theory and real engineering systems.
Many articles come from things I discovered while debugging or experimenting, because those moments often contain the most useful lessons.
Great! What is your usual writing routine like (if you have one?)
Most of my writing begins with a technical problem I’m trying to solve. I spend time experimenting, debugging, and testing different model training approaches. Once I understand what’s happening, I write about the process, the mistakes, and the insights, so others can learn from the experience.
Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing?
The biggest challenge for me is balancing deep technical investigation with clear communication. A lot of the work behind my articles involves debugging systems, running experiments, and analyzing how AI models behave in different scenarios. That process can take a significant amount of time.
The next step is translating those technical discoveries into something readers can easily understand. Explaining complex AI behavior in a clear and engaging way without losing the technical accuracy requires careful writing and thoughtful structure.
What is the next thing you hope to achieve in your career?
I want to continue working at the intersection of AI research, engineering, and education.
One of my long-term goals is to produce high-quality technical resources that help engineers understand how modern AI systems actually work—from neural networks to large-scale AI infrastructure. There is often a gap between research papers and real-world implementation, and I enjoy creating content that helps bridge that gap.
I also plan to write more books and long-form technical content that explore AI systems in depth.
At the same time, I’m working on growing EmiTechLogic into a platform where developers, researchers, and learners can find practical guides, experiments, and detailed explanations about AI, machine learning, and Python.
Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice?
My guilty pleasure is spending more time optimizing code than necessary. Sometimes a program already works fine, but I still feel the urge to improve performance or simplify the logic just to see how much better it can get.
Do you have a non-tech-related hobby? If yes, what is it?
Yes, one of my hobbies is watching K-series and C-series dramas. I enjoy the storytelling, character development, and the different cultural perspectives they bring. It’s also a great way for me to relax and take a break from technical work after spending long hours coding or writing.
What can the Hacker Noon community expect to read from you next?
The HackerNoon community can expect more articles from me on AI engineering, neural networks, and practical machine learning systems. I’m particularly interested in writing about the real challenges engineers face when building and debugging AI models, including performance issues, training behavior, and system design.
I also plan to share more hands-on experiments, technical deep dives, and practical insights drawn from my own work and research. Alongside that, I’ll continue publishing detailed guides and technical resources through my platform, EmiTechLogic, where I explore Python, AI systems, and machine learning concepts in depth.
What’s your opinion on HackerNoon as a platform for writers?
HackerNoon is a fantastic open platform that validates technical expertise and gives independent researchers and engineers a voice.
It’s a great place for sharing authentic, hands-on technical knowledge, and for connecting with a global community interested in technology, software engineering, and innovation.
Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words?
Thank you for the opportunity to be part of the Meet the Writer series. Writing is one of the best ways to refine ideas and share knowledge with the community.
My advice to anyone who enjoys technology is simple: keep experimenting and keep documenting what you learn. Some of the most valuable insights come from real problems, failed attempts, and the process of figuring things out.
