As a developer, for a long time, I thought learning a new technology meant doing courses, tutorials, or reading documentation. With each new trend that popped up, I saved the videos, bookmarked the articles, and meticulously crafted lengthy learning plans. While this certainly led to acquiring knowledge, it rarely translated into building something concrete.
Recently, however, I decided to join an AI cohort and my entire perspective on learning was flipped upside down. It wasn't just about consuming knowledge anymore; it was about building, sharing publicly, and applying concepts to real projects. This was the start of a new journey for me – one that goes beyond just learning about AI and ventures into building with AI.
The Tutorial Trap
For the longest time, I was stuck in what is known as the “tutorial trap.” I’d complete a course, feel productive, but when it came to actually building something on my own, I often struggled.
The issue wasn't a lack of information. In fact, the volume of AI resources is greater than ever before. Instead, the real challenge was turning knowledge into skill.
I quickly realized that watching someone build an application is a very different beast from building one yourself. Tutorials tend to show the happy path, but real projects expose you to problems, bugs, and decisions you need to make on your own.
A Different Learning Approach
The AI cohort took a radically different approach. Rather than spend weeks learning every single concept before beginning, we were actively encouraged to start building right away.
This felt unnatural to me at first; I wanted to understand more before I acted. But it quickly became evident that building reveals knowledge gaps far faster than passive learning.
When you start building, questions inevitably arise:
- How should I structure the application?
- What is the right AI model to use?
- How can I improve the user experience?
- What if something goes wrong?
Finding the answers to these questions as I built helped me learn exponentially more than just passively consuming information.
Learning in Public
One of the most impactful lessons I learned was the concept of “learning in public.”
Sharing my progress online at first felt vulnerable. I worried that my projects needed to be perfect before anyone else saw them. However, I soon realized that progress is far more important than perfection.
By sharing my journey, I achieved several things:
- Accountability: I had a reason to stay consistent.
- Feedback: Other developers offered valuable insights and suggestions.
- Connection: I connected with individuals who shared similar interests and learning goals.
- Record of Progress: I created a visible history of my development over time.
Learning in public transformed my solitary study into a collaborative and interactive experience.
Building Real Projects
Another crucial shift was the emphasis on building real projects instead of isolated exercises.
When working on a project, every concept has a clear purpose. I didn't learn a tool just because it was trendy; I learned it because it solved a specific problem in my application.
Building projects also made the learning process more exciting. Every feature I added, every bug I fixed, felt like tangible progress.
More importantly, building projects created undeniable proof of my learning. Instead of saying I was studying AI, I could point to actual applications I had built and explain my design choices.
The Power of Consistency
A surprising lesson I learned was the importance of consistency. Before the cohort, I often relied on motivation, having bursts of intense study followed by periods of inactivity. The cohort, however, emphasized small, consistent actions. Whether it was coding a feature, writing a post about my learnings, or documenting a lesson, regular, incremental progress built momentum.
Over time, these small steps compounded into significant growth and development, something motivation alone couldn't achieve. Consistency might not seem impressive in the short term, but it delivers remarkable results in the long run.
Key Lessons Learned
Reflecting on this journey, several key lessons stand out:
- Building accelerates learning: Acting on knowledge teaches more effectively than passive consumption.
- Real projects expose real gaps: Building applications reveals the limitations in your understanding.
- Sharing creates accountability: Publicly documenting your progress keeps you motivated and connected.
- Consistency trumps motivation: Small, regular efforts yield far greater results.
- Progress over perfection: Focus on making headway, not on achieving flawlessness.
- Learning plus action equals growth: Knowledge is amplified when put into practice.
These principles are not limited to AI; they can be applied to learning any new technology.
What’s Next?
This journey is just the beginning. There’s so much more to explore in the world of AI, including agents, automation, and the latest development tools.
My goal, however, is no longer to simply collect more courses or articles. Instead, I want to continue building, sharing my journey, and improving through active application.
The future belongs to those who build, and that’s exactly where I want to be.
Final Thoughts
Joining an AI cohort taught me something profound: learning is merely the first step. True growth happens when you combine knowledge with action.
If you’re learning AI or any other technology, my advice is straightforward: start building, even when you don’t feel fully ready. Your first project may not be perfect, but it will teach you infinitely more than another tutorial.
For me, this is more than just another learning experience; it’s the start of a brand new adventure – one defined by building, sharing, and continuous improvement.
