I spend a lot of time working with AI tools, improving my prompting, and now focusing on enhancing my Python skills for AI development – that's why I'm here! But I wanted to share a perspective from a different part of my background that's universally helpful: getting (and staying) motivated. My background includes working with children with special needs, where fostering motivation was a central challenge. It taught me a powerful lesson: We have motivation backward. We think motivation leads to work, but usually, work leads to motivation. How often do we put off a task (like starting a complex coding project or learning a new AI framework) because we "don't feel motivated"? I learned that waiting for that feeling is often futile. Motivation isn't something you find; it's something you build. When you start engaging with the task – even in the smallest way, like setting up your Python environment, writing a simple file-reading script, or just sketching out your AI agent's logic – you become invested. That investment is the seed of genuine, internal motivation. The act of doing creates the drive to do more. If you're struggling to start that AI agent project or tackle a tricky Python concept because it feels overwhelming, my advice is simple: Just begin. Write one line of code, outline a small function, read one section of documentation. Start small. The motivation often follows the action. TL;DR: Work feeds motivation, not the other way around. I am curious to hear how others handle motivation slumps! Let's chat below.

I'm working my way through the lessons and examples from the courses, learning a lot! And I was just thinking that at the time I'm writing this, and in my personal opinion, Google Gemini seems to be ahead of OpenAI in terms of cost effective and performance. Their latest models deliver incredible well at a fraction of the cost. So after cloning the cookbook repository to follow along with Dave's lessons, I started to change the code to use Gemini API using their Python SDK. It has been really useful for me for grasping even deeper all the knowledge because I had to rewrite the example code following a different API documentation to essentially achieve the same thing, and both are similar at they core but with small differences that made me have to think more carefully about each step. You could use OpenAI compatible bridge with Gemini, but I'd figured that doing the switch would be educational to say the least.

There is this research from ChromaDB that talks about the different strategies for chunking text without loosing valuable context for optimal RAG systems, but I'd recommend checking out Adam Lucek's YouTube channel where he demonstrates with practical examples these strategies. He also made available a Github repository (QuicKB) where he puts in practice all this. I think it's a really valuable resource. Don't miss the link to the video below! Cheers! 🙂

I hit the level 2 and completed the Fundamentals section looking forward to hit the level 3 to unlock the second section and complete the course to share the knowledge and use it in my career journey

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