I'm Ivan Escribano, a Software Engineer and AI Engineer with 8+ years in tech and 5+ years building web applications.

Currently, I'm CTO and Co-Founder at WeUp, a SaaS platform that helps freelancers, agencies, and companies manage their LinkedIn content using artificial intelligence. We've reached +500 users across Spain, LATAM, and the US, building the entire infrastructure from scratch on Azure Cloud.

My daily work combines Full Stack development (React, Next.js, Node.js, TypeScript, Python) with AI integration: RAG systems, vector databases, prompt engineering, and agent creation with Langchain.

Before WeUp, I worked as Senior Full Stack Engineer at Junoverse (Swiss longevity startup) and at NTT DATA building applications for major Spanish banks, where I learned to build software that impacts millions of users.

My career reflects constant growth: from IT support to developer, from junior to senior, from employee to CTO. I truly believe we're living the decade where smart AI integration into real products will make the difference.

Looking for a technical profile with product vision and experience leading projects end-to-end?

I'm open to new opportunities. Let's talk.

10 years ago 

Silicon Valley predicted "vibe coding"

They sold it as comedy.
An AI that deletes all the code to "fix bugs".

It was just:
- Funny.
- Fiction. 
 -Comedy. 


But yesterday, reality hit me.  

I opened LinkedIn and saw tons of:
→ "Software engineers are dead"
→ "Build a app in 45mins without coding"
→ "Make $1K/month just by writing prompts"

And suddenly, the joke wasn't funny.

Look, I'm not anti AI. I use it every day.
But using a tool is one thing.
Trusting it blindly is another.

It's like:
Fixing your pipes with a Youtube tutorial.
↳ You don't need a plumber anymore, right?
↳ Until you come home to a flood.


So here's where I stand:

I'll keep using AI. Every day.
But never without understanding it.

Don't let the noise fool you:
"Software engineers are useless."
"No need to learn to code anymore."

Blablabla... Nonsense.

The game has changed. True.
You need to adapt. Also true.

But there's a difference:
→ Adapting by learning.
→ Adapting by outsourcing your brain.

Which path are you taking?

PS: Highly recommend watching Silicon Valley.

Ivan Escribano

I help entrepreneurs and small business owners create quality social content… without wasting 20 hours a week.

Why does WeUp exist?

Cause I’ve been there:
→ Worked at big corps (La Caixa, Ford) and startups.
→ Was a waiter/builder to pay for my studies.
→ Tried creating content for side hustles… failed ‘cause I had zero time.

In 2024, I stopped job-hopping to build the tool I needed:
→ No generic content.
→ No 50-hour courses.
→ Just expert-backed strategies.

Today, WeUp’s helped:
→ 100+ businesses nail their content.
→ Automate 500+ posts on LinkedIn.
→ Boost personal brands by 40% engagement.

Our vibe:
→ Simplicity: 3 clicks to make a post.
→ Speed: Ideas in 10 seconds.
→ Quality: Content that makes you look pro.

Wanna try? Test-drive WeUp free (no card needed). https://www.weup.ai/

Software Engineer @Weup | AI Engineer | React, Next.js, TypeScript, Node.js & Python Specialist | Building on Azure cloud

Hard truth:

Learn AI Engineering by cheating.

Cheat the learning curve.
Cheat the "proper path."
Cheat the expectations.

"But I need to learn first...." 
No. Build while you learn. 

"But I'm not ready...."
You'll never be. Start anyway.

"But what if it breaks?" 
It will. That's the curriculum.

Here's your cheat code:

1. Programming → Build an API.
2. LLMs → Call one. Today.
3. Prompts → Write 10 of them.
4. Data → Set up a vector DB.
5. RAG → Build one. It'll suck.
6. Frameworks → Pick one. Go deep.
7. Agents → Make it autonomous.
8. Reliability → Break it. Fix it.
9. Protocols → Learn MCP.
10. Production → Deploy something.

Stop waiting for the perfect moment.

The perfect course.
The perfect mentor.
They don't exist.

Start today or keep making excuses.

♻️ Repost if someone needs to see this.
📌 And save it for when you're ready to start.

#AIEngineering

No high school diploma.
No coding background.
Now: AI researcher at OpenAI.

Gabriel Petersson story breaks every rule.

His method?
The opposite of traditional learning.

Traditional path:
→ Study math for 2 years
→ Then statistics
→ Then Python
→ Finally try to build something

Gabriel's path:
→ Try to build it directly
→ Get stuck ("what's a derivative?")
→ Ask ChatGPT: "Explain it like I'm 12"
→ Fill the gaps as they appear

No waiting to "be ready."
Start. Get stuck. Ask. Move forward.

His philosophy:
"You can do PhD-level work without the PhD."

Not every company believes this.
But the best ones want results, not diplomas.

Full podcast in comments. 👇

Credits to Gabriel Petersson & Sigil Wen

sora research at OpenAI

Thiel Fellow | Extraordinary.com | O-1 evangelist

Jensen nails it.
2010-2020: research era
2020-2030: implementation era

But here's what's crazy:
Most companies are still stuck in 2010.
They want ML Engineers to "build their own model".

Let's be serious...

You're competing against:
➔ OpenAI
➔ Google
➔ Anthropic

Companies burning BILLIONS every year.

And you think your team can do better?
Come on...

AI didn't appear in 2022.
ChatGPT just put it in normal people's hands.

That changed everything.

The value now isn't in creation.

It's in application:
➔ Connecting AI with real products
➔ Solving specific problems
➔ Delivering actual impact

That's AI Engineering.

Is your company ready for this decade?
Or still stuck in the last one?

📌 Save this if you're building AI products.
♻️ Repost if you think companies need to see this.

Credits to Cleo Abrahams amazing podcast!!

NVIDIA AI 
#AIEngineer

NVIDIA AI

Design Patterns made simple.

8 patterns. Visual. Practical. Free repo.

Most tutorials overcomplicate them.

Walls of theory.
Abstract UML diagrams.
Examples that make no sense.

And then you wonder: 
"When would I ever use this?"

Here's the reality:

Most days, you don't need patterns.
Your code works fine without them.

But then you hit THAT problem.

The one where everything gets messy.
The one where you copy-paste the same logic
The one where adding a feature breaks three other.

That's when patterns matter.
Not as rules to follow blindly.
As tools to reach for when needed.

So I built something different:
→ Visual diagrams
→ Short explanations
→ Real-world analogies
→ Exercises you can finish in minutes

The goal?
Build a mental library.

So when you hit a problem, you think:
"Wait, there's a pattern for this."

🔗 Github repo in comments
⭐ Star if this helps.
♻️ Repost for a dev who needs this.

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