@ParamSiddh: 1) I switched to AI Engineering 1 years ago! It was the best career move I ever made. If you want to start today, here'…

1. I switched to AI Engineering 1 years ago! It was the best career move I ever made.

If you want to start today, here’s a roadmap:

1. Master Python

While many are busy vibe coding, those with strong coding fundamentals will always stand out.

Python is the language AI community speaks, and Harvard’s CS50p is the best place to learn it.

2. AI with Python

Once you’re done with the fundamentals, it’s the right time to understand how Python is used in AI.

This 4 hours course by Andrew Ng is a great starting point.

3. Understanding LLMs

These three videos by @3Blue1Brown are arguably the best visual explainers of LLMs and their internal workings.

1. How LLMs work
2. Transformers Deep-dive
3. Attention in transformers
4. How LLMs store facts

4. LLM research

Now that you understand what LLMs are, it’s time to learn how to build them yourself.

This is the greatest series by the greatest teacher in the world.

Neural nets zero-to-hero by Andrej Karpathy

5. AI Agents

Before jumping into the AI agent hype, everyone should read Anthropic AI’s guide on building effective agents.

“To build an agent, you don’t need complex frameworks or libraries, but rather composable patterns”

7. Applied AI

I don’t recommend chasing frameworks, but I took this course on CrewAI when I started.

It’s clear, practical and teaches you to think of agents like humans working together

Plus, the founder @joaomdmoura is an excellent teacher.

8. AI Protocols (MCP)

Now that you understand what agents are, it’s time to connect them to external tools, APIs, and databases.

My co-founder and I published this hands-on guide on MCP with 10+ projects.

It’s free and downloaded over 40,000 times.

9. Project-based learning

This GitHub repo contains a 75+ projects on AI Engineering.

Everything is 100% open-source, covering

• LLMs and RAGs • Real-world AI agent applications • Examples to implement, adapt, and scale in your projects •

Books

Every AI engineer building real-world applications should read this book.

@chipro is a remarkable teacher and her book is one of the best on AI Engineering.

So, you don’t have to read 10 books, this one should get the job done!

To summarize, here’s what we covered:

• Programming (Python)
• LLM fundamentals
• Building LLMs/ LLM research
• AI agents and applied AI
• AI protocols
• AI engineering projects
• Book(s)

Never chase frameworks—they come and go. Master the fundamentals.

meko darrr lg rha

teko jhuth lg rha???

konse???

accha achha