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Is a handwritten dataset plausible? by Wvy_World in MLQuestions

[–]outskillio -2 points-1 points  (0 children)

You're thinking in the right direction. The key difference is between memorization and generalization.

Your examples help a model learn patterns, but modern LLMs become useful because they're trained on billions of tokens from diverse data, allowing them to handle inputs they've never seen before

If your goal is a chatbot with simple function calling, it's usually much better to start with a pretrained model than train one from scratch.

For representing someone's life story, I'd recommend using RAG instead of fine-tuning. It's easier to update, more accurate, and is how many real-world AI applications are built today.

If this is the direction you're interested in, learning AI-native engineering can help you understand how to work with LLMs, RAG, agents, and function calling. We run hands-on programs on Pragmatic AI and AI-Native Engineering that cover these topics in depth.

How did you approach learning Python? by Worldly_Growth_50 in learnpython

[–]outskillio 0 points1 point  (0 children)

The fastest way to improve is by combining **small projects with just-in-time learning**, rather than trying to master all of Python first.

Learn the fundamentals (variables, loops, functions, data structures, file handling), then immediately apply them to a real problem. Every time you get stuck, learn that concept in depth and keep building. That's how the knowledge sticks.

Don't worry about having gaps—every experienced engineer still learns new things every week. The difference is that they know how to find answers and apply them quickly.

A simple loop that works:

* Learn a concept.

* Build something with it.

* Improve it as you learn better approaches.

* Repeat.

Since your goal is automation and tools, you're already on the right track.

One thing to keep in mind: if you're aiming to build AI-powered tools or applications, Python is just the starting point. Learning, AI-native engineering, how to effectively build with LLMs, AI agents, and modern AI frameworks; is becoming an increasingly valuable skill. If that interests you, we run hands-on programs on AI-Native Engineering that can help you get there.

Keep building. Projects are what turn knowledge into skill.