Eduardo Ordax

Top 10 YouTube Channels to Learn AI from Scratch! 🚀

I’ve always been a huge fan of YouTube as a learning platform. It gives you direct access to brilliant minds who can help boost your AI skills—whether you’re just starting, leveling up, or aiming to master the field.

Here are 10 must-follow YouTube channels for learning AI, no matter where you are on your journey:

🎓 1) Andrej Karpathy – Deep yet accessible lectures on deep learning, LLMs, and an intro course on neural networks. https://lnkd.in/dVA5bGyZ
📊 2) 3Blue1Brown – Stunning visualizations that make abstract mathematical concepts intuitive. https://lnkd.in/dQsiH2Xu
🎙️ 3) Lex Fridman – In-depth conversations with AI leaders, offering a broader perspective on the field. https://lnkd.in/dRJ6DVbE
🤖 4) Machine Learning Street Talk – Technical deep dives and discussions with top AI researchers. https://lnkd.in/dNceZibz
📚 5) StatQuest with Joshua Starmer PhD – Beginner-friendly explainers on machine learning and statistics. https://lnkd.in/dTjRCxYP
🍉 6) Serrano Academy (Luis Serrano) – Clear and accessible content on ML, deep learning, and AI advancements.https://https://lnkd.in/d8fhq7Nq
💻 7) Jeremy Howard – Practical deep learning courses and AI-powered web app tutorials. https://lnkd.in/dkwh53ss
🛠️ 8) Hamel Husain – Hands-on lessons in LLMs, RAG, fine-tuning, and AI evaluations. https://lnkd.in/dj8EEzPm
🚀 9) Jason Liu – Expert-led lectures on RAG and AI freelancing tips for ML developers. https://lnkd.in/dbCCXZhb
⚙️ 10) Dave Ebbelaar – Practical guides on building AI systems and real-world applications. https://lnkd.in/dThXHtpb

If you're looking to dive into AI, these channels are absolute gems! Did I miss any of your favorites?

Kudos to Shaw Talebi for the recommendations!

#AI #MachineLearning #DeepLearning #YouTube #LearningAI

It's only a matter of time before AI replaces software engineers... Or not? 😂😂😂

#AI #claude4

Why you should not use LLMs for any specific use case? 🧠🤖👨🏽‍💼


Many times customers from different parts of the world ask me about what others are doing with Generative AI. They want to learn about good practices and how to avoid the most common pitfalls. Sometimes my felling, and my answer too is: “Everyone, is using LLMs, everywhere”. Unfortunately this is the biggest mistake.

While LLMs like GPT, Claude or Gemini have impressive capabilities, they may not always be the best fit for every AI use case.

Here’s why 👇

🔸Traditional ML Solutions: If the use case can be solved with traditional machine learning, opt for those instead. Simpler and more efficient. 🛠️
🔸 High Costs 💸: Significant computational and monetary expenses.
🔸Latency Issues ⏱️: Longer response times, not ideal for real-time applications.
🔸Scalability Challenges 📉: Complex and resource-intensive deployment and maintenance.
🔸Environmental Impact 🌍: High energy consumption and carbon footprint.
🔸Overkill for Simple Tasks 🛠️: Unnecessary complexity and resource allocation for straightforward tasks.
🔸Ethical and Bias Concerns ⚖️: Risk of propagating biases and ethical issues.
🔸Data Privacy and Security 🔒: Potential risks in handling sensitive information.

#ai #genai #llm

Waterfall Vs Agile Vs AI Vs Vibe Coding 👇

(This post really deserved a second part)

𝗪𝗮𝘁𝗲𝗿𝗳𝗮𝗹𝗹
Client: “We want a chatbot”
Vendor: “Sure, let’s start with the front end.”

𝗔𝗴𝗶𝗹𝗲
Client: “We want a chatbot”
Vendor: “Cool, let’s build an MVP.”

𝗔𝗜
Client: “We want a chatbot”
Vendor: “Absolutely. Let’s build the next GenAI game-changing and mind-blowing assistant”.

𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴
Client: “We want a chatbot”
Vendor: “Cool… I just lit an incense stick, opened Lovable, asked ChatGPT for ‘startupy prompts’ and manifested a roadmap. The code? Well… it’s more of a feeling. We’ll know when it’s done.”
▪️No Git, just vibes.
▪️CI/CD pipeline? That’s old school
▪️Main dependency: what it’s that?
▪️Testing??
▪️Production deploy?

Revolution is here!!!!

#ai #genai #vibe #humor #meme

The hidden tax of data engineering: CSV files.

It’s 2025, and yet, so much of a data engineer’s time is still spent wrangling data that lives in… exported CSV files.

Whether it’s sharing data across teams, integrating with legacy systems, or just handling “quick” one-off requests, these files become small islands of information floating outside of controlled environments.

They often lack schema validation, version control, or proper lineage, turning simple tasks into hours (or days) of detective work.

Data engineers automate pipelines, build robust data lakes, and preach about data governance…

But then reality hits:

“Hey, I’ve got this latest export in a CSV, can you load it into the system?”

It’s a reminder that even in an age of advanced AI, a big part of the job is still about bridging the gap between best practices and the practicalities of how data actually moves inside organizations.

#DataEngineering #data #CSV #ai #ml

🚀 Why Your Company Needs AI Engineers NOW!

Generative AI and LLMs are revolutionizing how we solve business problems. With models like GPT or Claude and a bit of prompt engineering, anyone can prototype solutions in days! 💡

But here's the point: getting from prototype to production isn't as simple as it seems. You still need to optimize, version, and deploy those prompts into production. And let’s not forget about monitoring API calls, latency, performance, and everything else that comes with scaling AI.

This is where the AI Engineer steps in. While prompt engineers focus on experimentation, AI engineers are the ones who ensure your AI solutions are production-ready and scalable across the organization. 🚀

Is your company ready to take AI seriously?

#AI #GenerativeAI #FutureOfWork #tech #claude

The AI ​​community has spoken, and here's their take on Llama4 👇

Are model providers pushing too much on benchmarks instead of real capabilities?

#ai #meta #llama4 #genai

Like 👍🏻 / Comment 💬 / Repost ♻️

Anthropic: the one without all the drama!

I must say the more I use Claude, the more I love it! And this is not just me, it’s also customers around the world who are moving away from OpenAI into Anthropic. Reasons: better models, more reliable and one of the key reasons, their unique approach to AI safety.

Not to mention the new marketing campaign, it’s just so funny.

Exciting times ahead!!!

#ai #anthropic #genai #llm

This is huge! AWS x Databricks

Amazon Web Services (AWS) and startup Databricks struck a five-year deal that could cut costs for businesses seeking to build their own artificial-intelligence capabilities.

Databricks will use Amazon’s Trainium AI chips to power services for building AI Systems.

🔗Link to the article: https://lnkd.in/deFb6taH

#ai #genai #llm

The Rise of “AI Experts” — And Why I Still Call Myself an Outsider

Lately, I’ve been noticing a familiar pattern, impostor syndrome is everywhere. But this time, it’s not just internal. It’s playing out in the AI space, where it’s getting harder to tell what’s real and what’s just… well, branding.

I see people post daily about AI, riding the hype, wearing the “expert” badge proudly. But when it comes to actually speaking in public, answering hard questions, or getting into the technical weeds?

Silence.

We throw around the term expert so casually that it’s started to lose meaning.

Here’s the usual evolution:
✔️Expert on U.S. tariffs
✔️Expert on geopolitics
✔️Expert on the Pope
✔️And AI expert too….

It’s always the same playbook.

That’s why I’ve never called myself an expert and probably never will. I prefer to stay an outsider, learning, questioning, and sharing what I do know without pretending to know it all.

Because in a field changing this fast, claiming expertise too loudly might just be the first sign you’ve stopped learning.

#AI #experts