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.

need some help. I've exhausted all patience with this and kaggle's hint and solution features are not working. Why won't this function run?

Suggestion required, AI generated image text is overlapped or incorrect. Which tool will generate correct image? example of incorrect image herewith. Pls suggest

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Everyone’s talking about AI agents. But the truth? Most demos you see online are just that—demos. Even big players like Apple and Amazon struggle to make their AI features work in the real world due to issues like hallucinations and unreliable outputs. In this week’s video, I break down the differences between simple workflows and true AI agents and share practical strategies for building reliable AI systems, including: - How to use workflow patterns like prompt chaining and routing to solve real problems effectively - Why agent frameworks might not be the solution you think they are - The #1 thing you need to scale AI systems successfully (hint: it’s not a new tool) Learn how to move beyond the hype and build AI systems that actually work.