u/Ok-Artist-5044

Hey everyone 👋

I’ve been seeing a lot of people asking “how do I start with AI?” and honestly, I was in the same place not too long ago — overwhelmed by buzzwords like LLMs, transformers, agents, etc.

So I decided to build something simple:

👉 a beginner-friendly AI playlist that explains concepts in a clear + practical way (no heavy math overload, no fluff)

What you’ll find in it:

Basics of AI, ML, and how they actually differ

Core concepts explained in short videos

Real-world intuition (not just theory)

Topics like quantization, LLMs, and modern AI trends broken down simply

If you’re just starting out or feeling stuck, this might help you get momentum.

🔗 Playlist: https://youtube.com/playlist?list=PL8LMoHBOq\_HNLeZ0KWLSKFHBCJ8jp0PKk&si=P3lzZiB8dh5viu\_T

Would genuinely love feedback or suggestions on what I should cover next 🙌

Hey folks,

I’ve been experimenting with different LLM setups for coding and wanted to understand how people are practically using Claude Opus (4.x) within VS Code.

From what I see:

GitHub Copilot doesn’t allow plugging in external models

So options seem to be things like Continue, CodeGPT, or switching to something like Cursor IDE

I’m particularly curious about:

What setup are you using? (OpenRouter vs direct Anthropic API)

How’s the real-world experience vs Copilot?

Is Opus actually worth the cost for day-to-day dev work?

Are you using it more for agent-style workflows / repo reasoning rather than autocomplete?

Would love to hear what’s working (or not working) for you all.

Hi everyone,

Playlist link: - https://youtube.com/playlist?list=PL8LMoHBOq\_HNLeZ0KWLSKFHBCJ8jp0PKk&si=2bNR33wqpKiriXZ4

I’ve been learning and working in AI/DevOps space, and noticed that many beginners struggle to understand core AI concepts like LLMs, Transformers, Vector Databases, RAG etc. because most content is either too academic or too long.

So I created a short playlist where each concept is explained in 60–120 seconds in simple language.

The idea is:

Learn the fundamentals quickly → then go deeper where needed.

Playlist covers:

• Large Language Models (LLM) explained simply

• Vector Databases explained in 60 seconds

• AI vs Machine Learning vs Deep Learning

• Attention mechanism explained visually

• Transformers architecture simplified

• How Multi-Modal AI works

• Inside the mind of modern AI systems

Who this is for:

Beginners starting AI journey

Developers moving into AI engineering

Anyone curious about how ChatGPT-like systems actually work

Students preparing for AI interviews

Goal: build a clear mental model of AI stack

quickly.

I’d genuinely appreciate feedback:

What topic should I cover next?

Is the pace too fast?

Any concept you want simplified?

If this helps even a little, I’ll keep adding more topics like:

RAG, embeddings, fine-tuning, AI agents, MCP, etc.

Thanks 🙌