Harvey Castro, MD, MBA.

Phantom Space

The advisor role at Phantom Space involves providing strategic guidance and leveraging expertise in areas like AI, healthcare, and technology innovation to support the company’s mission of advancing space commerce. Advisors are expected to collaborate with the leadership team, offering insights that help drive the company's goals of creating efficient, cost-effective space solutions and maintaining a leading edge in technology.

 𝐀𝐝𝐯𝐢𝐬𝐨𝐫 𝐚𝐭 𝐏𝐡𝐚𝐧𝐭𝐨𝐦 𝐒𝐩𝐚𝐜𝐞

Ministry of Health (Singapore)

t I am honored to announce my recent appointment as a member of the Health Regulatory Advisory Panel (MOH RAP) at the Singapore Ministry of Health, serving from 1 April 2024 to 31 March 2026. This role empowers me to contribute to shaping regulatory approaches in healthcare, focusing on innovation, risk management, and the integration of new technologies and care models in a rapidly evolving landscape. With a deep commitment to enhancing healthcare delivery and outcomes, my work with MOH RAP will center on advising on regulatory policy, operations, and transformation, particularly at the intersection of healthcare services, technologies, and professional practices. Additionally, I will engage in international networks to further Singapore’s position and interests in global healthcare regulation. I am eager to leverage this opportunity to make a meaningful impact on locally and globally healthcare systems.

Health Regulatory Advisory Panel (MOH RAP)

Texas Medical Association

I am honored to accept the invitation to serve as a consultant to the Texas Medical Association (TMA) Committee on Health Information Technology as an AI expert, beginning immediately and extending through TexMed 2025. In this role, I will be focusing on advancing and integrating AI applications in healthcare, ensuring physician interests are represented and protected in this rapidly evolving field.

With TMA representing over 55,000 physicians in Texas, my work will involve developing strategies and policies that harness the power of AI to improve patient care and healthcare delivery. I will be collaborating with the committee to address the ethical and practical challenges of AI in healthcare, advocating for best practices, and guiding the implementation of innovative AI solutions that support physicians and enhance healthcare outcomes.

My expertise in AI and healthcare will be invaluable as we work together to navigate the complexities of AI integration, ensuring it serves the needs of both patients and healthcare providers. I look forward to contributing to TMA's mission and driving the AI healthcare revolution forward.

Information Technology Consultant

Aexa Tech

Partner & Medical AI Advisor at Aexa Tech

In this capacity, I work with a forward-thinking team dedicated to integrating cutting-edge AI solutions into healthcare. As part of Aexa Tech’s mission, I:

Develop AI-Enhanced Medical Applications
Oversee the design and implementation of AI-driven tools that aim to improve patient outcomes and streamline clinical workflows.

Collaborate on Holographic & Mixed Reality Projects
Contribute to the development of Aexa Tech’s flagship products such as HoloConnect and HoloWizard, which revolutionize remote consultations, training, and patient engagement through advanced holographic communication.

Bridge AI and Healthcare
Ensure that our innovative solutions meet regulatory standards, adhere to the latest medical guidelines, and effectively address healthcare challenges across diverse specialties.

Lead Strategic Partnerships
Engage with industry partners to expand the adoption of holographic and AI-powered solutions in clinical settings, elevating Aexa Tech’s impact on global healthcare.

Key Achievement:
Spearheaded early prototypes that leverage AI and mixed reality for telemedicine consultations, paving the way for more immersive and efficient patient-care interactions.

Partner & Medical AI Advisor

American Society for AI

Private, invitation-only nonprofit that convenes senior AI leaders twice a year for closed-door, off-the-record summits. Membership is strictly limited and awarded only after nomination, a rigorous Board interview, and majority vote.
 The Society accepts no sponsors or donations and bars press, recordings, or livestreams to preserve candid dialogue.

 Within ASFAI, the title “Distinguished Member” is reserved for select invitees and is referenced in official releases and events.

Twice-annual summits bring together C-suite innovators, policymakers, academics, clinicians, and investors to explore the breakthrough applications, ethics, and policy at the forefront of AI.
 External analysts consistently rank ASFAI the most prestigious AI association in the United States, thanks to its tight gate-keeping model and impact-focused agenda.

Guided by the mission “to create a better world with AI,” the Society equips members with cross-sector insights that translate into responsible, human-centered innovation.

Distinguish member 

The American Journal of Healthcare Strategy

Board Member | The American Journal of Healthcare Strategy (AJHCS)

As a physician-entrepreneur and AI futurist, I guide AJHCS in transforming cutting-edge insights into actionable strategies for health system leaders worldwide. My board work centers on three pillars: governance, innovation, and impact, so that every article, podcast, and policy brief drives measurable improvement in care delivery.

Board Member

Helpp.ai

Helpp.ai revolutionizes healthcare with AI-driven fall prevention and workflow automation, targeting the significant challenge of patient falls—a problem costing US healthcare $34 billion annually. Our platform leverages ambient intelligence to provide early warnings, reducing falls and enhancing patient safety.

Beyond fall prevention, helpp.ai automates healthcare workflows, alleviating practitioner burnout and improving care quality. It adapts to changing patient needs, optimizes care delivery, and supports clinical decision-making with timely insights. This integration of technology into healthcare not only improves patient outcomes but also operational efficiency.

Designed to seamlessly integrate into existing systems, helpp.ai offers a scalable solution for improving patient outcomes and efficiency in hospitals and care facilities. Our technology streamlines clinical decisions, ensures compliance with healthcare standards, and allows healthcare professionals to focus on delivering exceptional care.

Join us in advancing healthcare, where technology and compassion meet to create safer, more efficient care environments. Explore helpp.ai’s potential to transform your healthcare delivery through innovative fall prevention, workflow optimization, and enhanced decision-making. Together, we can anticipate and prevent healthcare challenges, setting new standards of care.

#HelppAI #FallPrevention #HealthcareInnovation #AI #PatientSafety #WorkflowAutomation

Chief Medical AI Officer

OpenAI

I am excited to publicly share the new OpenAI Forum, a place to discuss, learn, and shape AI!

The forum features online and in-person events along with paid activities that directly impact OpenAI models: https://forum.openai.com/

The Forum events include in-person meet-ups highlighting technical talks, dinner mixers at OpenAI, educational webinars and expert roundtable conversations, and plenty of opportunities for members (including OpenAI researchers) to network and cross-pollinate ideas. Forum programming will be co-created by community members and facilitated by OpenAI.

The OpenAI Forum community aims to build an ecosystem of collaborators that help us achieve our mission of building AGI that benefits all of humanity.

OpenAI Forum Member

Mary Ann Liebert, Inc.

Transforming Oncology with AI: As a member of the AI in Precision Oncology Editorial Board, I am at the forefront of integrating artificial intelligence into cancer care. Our journal is the pioneering peer-reviewed platform dedicated to exploiting AI's potential in clinical and precision oncology. From groundbreaking AI algorithms in cancer detection to personalized therapy innovations, we are driving advancements that promise to revolutionize oncology care. Join us in exploring how AI is shaping the future of healthcare, making diagnosis and treatment more precise, personalized, and impactful. 🚀 #AIInHealthcare #PrecisionOncology #FutureOfMedicine

Editorial Board Member

PONS

The Medical Advisory Board of PONS is a team of highly skilled medical professionals who provide expert guidance and advice on the company's products and services. Comprising of experienced physicians, researchers, and specialists, the board plays a vital role in ensuring that the startup's technology is safe, effective, and in compliance with regulatory standards. Leveraging their expertise in various medical domains, the board members work closely with PONS's management to design and develop innovative solutions that can address the most pressing challenges in healthcare today. Their insights and recommendations help PONS to remain at the forefront of medical innovation, delivering cutting-edge products that can improve patient outcomes and quality of life.

Medical Advisory Board of PONS 

ChatGPT Healthcare Advisor • Physician with Executive MBA • AI in Healthcare Author Ex-CEO & Physician, aiding in AI's healthcare integration. Empowering physicians, patients, authoring the AI in healthcare revolution.

Confused on AI implementation? We help 👋 | Top AI Voice | Founder of Everyday AI, a Top 10 Tech Podcast | AI Strategist | AI Consultant | AI Keynote Speaker | Helping companies leverage GenAI

At 2 a.m. in the ER, I rely on seconds; soon I’ll depend on satellites. I’m Dr. Harvey Castro, the physician-futurist fusing AI, clinical data, and orbital sensors to predict illness before symptoms strike.

For 20 years, I treated crisis after it walked through the doors. Today I’m flipping that script: harnessing generative AI, ambient wearables, and space-borne imaging to keep patients out of the ER entirely.

Impact so far
• 40+ emergency departments using AI triage blueprints → 18 % faster door-to-doctor time  
• Tackling the $34 B fall-injury problem as Chief Medical AI Officer at Helpp.ai  
• Advisory work for the Singapore Ministry of Health & Texas Medical Association policy shaping for 21 million citizens  
• 4× TEDx & CES main-stage speaker; research cited in JAMA and MIT Tech Review  
• Co-architecting Phantom Space medical-payload program, turning lunar telemetry into oncology insights.

How can we work together
🔹 Bespoke AI-transformation projects let’s deploy tech that moves your company’s metrics.
🔹 Keynotes & board briefings on orbit-to-operating-room innovation  
🔹 Executive advisory retainers from proof-of-concept to FDA clearance

Ready to explore orbit-to-operating-room innovation?  
DM me here.

Let’s turn satellites, sensors, and software into healthier humans.

Chief Executive Officer

Medical Intelligence Ops, where we are pioneering the integration of Large Language Models (LLMs) into healthcare. Our physician-led team is committed to transforming patient care and hospital operations through intelligent solutions.

I’m Speaking at MEDICA - Leading International Trade Fair  2025 in #Düsseldorf

One of the biggest global stages in medical innovation and this year, AI is at the center of the conversation.

I’m grateful to be part of three sessions where we’ll explore how AI is reshaping clinical work, connected care, and our shared expectations for safety and trust in modern medicine.

📅 My Sessions at MEDICA 2025

TUESDAY, November 18

🧠 1. Panel — AI & Big Data in Healthcare
🕒 11:15 AM – 11:50 AM CET
Topic: “AI as a Healthcare Companion — How AI Supports Human Expertise and Decision-Making.”
Joining incredible leaders from the German Center for AI and Microsoft to discuss how AI can empower clinicians instead of overwhelm them.

🏆 2. Jury Member — MEDICA Start-Up Competition
🕝 2:30 PM – 6:00 PM CET
Excited to help evaluate groundbreaking digital health solutions pushing medicine forward.

WEDNESDAY, November 19

🎙 3. Keynote — Connected Medical Things
🕐 1:05 PM – 1:20 PM CET
Topic: “The Good, the Bad, and the Ugly of AI in Healthcare.”
A rapid-fire look at where AI is saving time, saving lives  and where vigilance is still urgently needed.

🌍 Why This Matters

AI in medicine is accelerating faster than our policies, workflows, and comfort levels.
Events like MEDICA give us a chance to slow down, challenge assumptions, and talk honestly about safety, bias, access, and the role of human judgment in an AI-first era.

I’m honored to contribute to this global dialogue and to bring back insights for clinicians, innovators, and leaders navigating this transformation.

If you’ll be in Düsseldorf, I’d love to connect. I am on WhatsApp or IM me here 
If not, I’ll share takeaways from the week here.

#AI #HealthcareInnovation #DigitalHealth #MedTech #DrGPT #ArtificialIntelligence #ConnectedCare #MEDICA2025 #DigitalTwins #HealthEquity #PhantomSpace #MedicalAI

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Using AI to Improve Healthcare: A KAIZUNO-Inspired Leadership Path

(Credit to the authors of #KAIZUNO and special thanks to Murat Aydin  for this powerful framework.)

Improving healthcare with AI isn’t just a technology challenge  it’s a leadership journey. The KAIZUNO system highlights 5 steps that align perfectly with what we must do as clinicians, innovators, and leaders shaping the future of medicine.

Here’s how I apply these steps as DrGPT and how you can use them in your own mission to transform healthcare with AI:

1️⃣ Strive for Success Start with Purpose

AI should exist for one reason: improving patient outcomes and empowering clinicians.
Tip: Identify one clinical pain point and define a simple, meaningful AI goal to address it.

2️⃣ Thrive Through Relationships  Build the Right Ecosystem

AI healthcare transformation requires collaboration — clinicians, technologists, policymakers, patients.
Tip: Form one new partnership this week (a hospital lead, a data scientist, a startup founder). Growth happens in teams.

3️⃣ Embrace Balanced Planning  Think Big, Plan Realistically

Bold vision must meet operational reality.
Tip: Before any AI project, outline the risks, data needs, workflows, and expected ROI. Balance ambition with feasibility.

4️⃣ Pursue Practical Implementation Make AI Useful at the Bedside

Technology only matters when it translates into real clinical value.
Tip: Start with one high-impact use case  documentation, triage, early warning, imaging and measure outcomes.


5️⃣ Secure Sustainable Success  Build Trust and Long-Term Impact

AI adoption isn’t a one-time event; it’s a continuous evolution.
Tip: Collect feedback, share wins, and constantly refine. Transparency builds trust, and trust scales adoption.

🧠 Our Mission

As DrGPT, my goal is simple:
Use AI responsibly to improve healthcare, support clinicians, and enhance patient care worldwide.

KAIZUNO reminds us that sustainable transformation doesn’t require complexity  just disciplined leadership.

Thank you to the KAIZUNO authors for this powerful framework — and special acknowledgment to Murat Aydin  for his leadership and contribution to operational excellence.

Let’s keep building the future of medicine together.
#DrGPT #FutureOfMedicine #HealthcareAI #Leadership #KAIZUNO

Some lines stay with you long after you say them.

In this clip from my #TEDx talk, “How AI, Satellites, and Edge Tech Keep Your Doctor Close,” I share a truth that has haunted me throughout my medical career:

Where you live will determine whether you live.

I’ve seen it at 3 a.m. in rural Texas, where a patient had no cell signal and no way to call for help.

I’ve seen it in communities where the nearest hospital is 70 miles away.

And I’ve seen how everything changes when technology steps in when a simple patch on someone’s chest can reach a satellite… which reaches a medical team… which saves a life.

My mission is simple:
No one should die because help was too far away.
If we can use AI, satellite communication, and edge technology to reach the unreachable, we can change the future of medicine one life, one moment at a time.

If this message resonates, I’d be honored if you watched my full TEDx talk on YouTube link in the comments and shared your thoughts in the comment’s of YouTube please. Your feedback truly means the world to me. ❤️

With gratitude,
Dr. Harvey Castro (“DrGPT”)
TedX Southlake thank you! 
#HealthcareAI #FutureOfMedicine #HealthEquity #DigitalHealth #SavingLives #ProactiveCare #TEDx #TEDxTalk

Every groundbreaking idea starts the same way: with a moment that feels impossible… until it isn’t.

I was looking at an image of Lionel Messi standing in a roaring Barcelona stadium with me..  lights blazing, crowd alive, history in motion.

For a second, it felt like a real moment.

And then it hit me:
Artificial intelligence didn’t just recreate a picture.
It recreated a feeling.

In medicine, this is the crossroads we’re standing in right now.

AI isn’t just producing data.
It’s producing confidence sometimes accurate, sometimes not.
It’s generating clinical recommendations that look polished, persuasive, and intuitive.

But the future of healthcare won’t be shaped by AI that feels real.
It will be shaped by AI that earns trust  through validation, transparency, and true clinical partnership.

Because the next era of medicine isn’t about replacing human judgment.
It’s about elevating it.

And that future belongs to the leaders willing to ask the hard questions:

Are we embracing AI because it’s compelling… or because it’s correct?
Are we adopting what looks advanced… or what truly protects patients?
Are we building tools that imitate insight… or tools that improve it?

Innovation begins when we stop accepting what feels real
and start demanding what is real.

And yes this Messi moment?
It’s an AI picture.

#HealthcareAI #AIinMedicine #DigitalHealth #FutureOfHealthcare #ClinicalAI #MedTech #InnovationLeadership #DrGPT #ThoughtLeadership #AIFuture

Honored to be included in the Top 100 Most Influential Men of 2025. My sincere thanks to everyone who nominated, supported, and uplifted the incredible individuals on this list. Congratulations to all who were recognized your impact matters.

I’m humbled and grateful for this honor.

If you’re new to my work, I’d love for you to follow my journey. My mission is clear: use AI responsibly to improve healthcare, support clinicians, and elevate humanity through innovation and compassion.

I share this vision in my #TEDx talks, which I’ll link in the comments with my fifth talk coming to YouTube this January.

Thank you again for this recognition. Let’s continue building a future where technology serves people, not the other way around.
Special thank you to Sibel Terhaar ! Very honored. 

#DrGPT #AI #HealthcareInnovation #FutureOfMedicine #Leadership #Gratitude

Founder & CEO at LeadershipUp Magazine | Top 50 Most Influential Leaders of 2025 | Founder of Chasing Kindness | Best Selling Author

The Top 100 Most Influential Men of 2025 has officially been announced! 👏🎉🎉

Today, we’re celebrating International Men’s Day by revealing the Top 100 list — selected from over 9,000 nominees.

These are your choices… 💕

But honestly, I’ll admit—I cheated on about five percent of it. 😜
I had to sneak in a few of my personal favorites. I’m only human. 😄

This day is about honoring the men who showed up, stayed strong, supported others, grew, evolved, and kept their character steady even when life pushed hard. ❤️ I adore kind people, yes respect kind men a lot.👏

I can’t tag all 100 names here, but every single one will be listed in the comments, so no one is overlooked.

And yes… I’m fair.
We celebrated Women’s Day this year, and we’ll celebrate again in 2026. Balance always. 💕

Since some of my followers are in Australia and India, we’re posting this early. And those in Europe and the USA will wake up happy 😊

Tag the men who deserve to be seen.
Let the fun begin — big energy, big appreciation, big love.

Happy International Men’s Day, gentlemen. 🙌

#happyinternationalmensday #top100 #men #kindness #motivation #inspiration

I will mention rest on the comments.

Matt Higgins  
Gary Vaynerchuk  
Patrick Schwarzenegger 
Adam Danyal 
Jasmin Alić 
Cory Warfield 
Aidan Kehoe 
Chef Duane Keller 
Sören Müller 
Harvey Castro, MD, MBA. 
Professor Gary Martin FAIM 
Scott Isaacson 
Stanford Routt 
Linus Kujava 

Mark Cuban 
Frank Powers, PMP MBA 
Hollie Price 
Murat Aydin 
David Fessell, MD 
Chris Do 
Bobby Umar 
Pete Schramm 
Hanif Ajari 
Richard DiPilla 
❤️‍🔥 Dennis Pitocco  
Brett Scott Brody 
Dennis Berry 
Bo Porter 

Emmanuel Ugo Enemuoh 
Daymond John 
Pavan Sampath 
Sean Kelly 
Robert Alexander 
Alen Voskanian, MD, MBA, FAAHPM, FACHE 
Kevin Davis 
Matthew Baumgartner, MBA 
Paul L. Gunn Jr 
Mangesh Pawar 
Joseph Irving D'aranjo 
Craig Wasilchak 

The rest will be in comments.

Top 100 Most Influential Men of 2025

Two physicians. One mission.
Pushing the boundaries of what AI can do for human health.

At #MEDICA, I had the chance to connect with Daniel Kraft, MD :one of the most forward-thinking voices in digital health. Our conversation reinforced something I see every day in the ER: no single clinician, researcher, or technologist can shape the future of healthcare alone.

Here’s the insight that hit me hardest:
AI isn’t transformative because it’s powerful.
It’s transformative because it forces us to collaborate in ways medicine never has before.

A few themes that stood out from our discussion:
• AI must augment clinical judgment not replace it.
• Tools that fit workflow will win over tools that simply impress.
• Trust and transparency will define which AI systems actually scale.
• Real breakthroughs happen when tech innovators and frontline clinicians share the table.

Moments like this remind me why AI + healthcare is truly a team sport and why these conversations matter more than ever.

What’s one collaboration or conversation that recently expanded your thinking about the future of medicine?

#FutureOfMedicine #AIFirst #HealthcareInnovation #DigitalHealth #MedTech #DrGPT

Tomorrow, I step onto the TEDx Southlake stage—feeling equal parts humbled and inspired.

This isn’t just another talk.
It’s a call to reimagine the future of healthcare—one where AI and space innovation come together to help humanity thrive.

As Chief AI Officer at Phantom Space, I’ve seen how satellite systems, remote sensing, and edge AI can do more than explore the stars—they can save lives here on Earth.

Tomorrow, I’ll share how these technologies can transform healthcare access, predict disease before symptoms appear, and connect care to the most remote corners of our planet (and beyond).

My hope?
That this talk sparks a collective movement—toward smarter, fairer, more connected care for all.

Wish me luck.
The future of health is closer than we think.

#TEDxSouthlake #AIinHealthcare #PhantomSpace #SpaceTech #FutureOfHealth #HealthcareInnovation #BetterCare #Inspiration #TomorrowStartsToday

Grateful. Grounded. Inspired.

This week, I stepped onto the #TEDx stage for the fifth time a reminder that this journey isn’t about recognition.
It’s about responsibility.

In my talk, I shared Maria’s story  a woman who woke up at 3 a.m. with chest pain, no cell signal, and the nearest hospital 70 miles away.
But she wasn’t alone.

A small AI-powered patch on her chest connected to a satellite orbiting above, alerting her doctor to a dangerous rhythm.

That simple connection saved her life.
From the emergency room to the edge of space, my mission stays the same:
Use AI to collapse the distance between patients and care.

Phantom Space,we’re proving that healthcare doesn’t end on Earth it expands beyond it.

Because AI isn’t replacing doctors.
It’s extending them helping us listen farther, see sooner, and care deeper.
Here’s to a future where technology and compassion rise together. 🌍🚀❤️
Thank you TEDxSouthlake! Great team! Great line up of speakers.  

#TEDx #DrGPT #AIinHealthcare #PhantomSpace #SpaceMedicine #HealthcareInnovation #FutureOfHealth #MissionDriven #Leadership #Gratitude

The Future of #Medicine Isn’t #AI It’s AI-Empowered Clinicians

Every time I’m on stage from #TEDx to global conferences the same question comes up:

“Will AI replace doctors?”

But at 3 AM in the ER, surrounded by uncertainty and life-or-death decisions, the answer is unmistakably clear:

AI can process data.
Only humans can deliver compassion, judgment, and hope.

At #MEDICA 2025, I’m sharing one core idea:

🧠 AI is the new stethoscope powerful, precise, and useless without human hands to guide it.

Here’s where the real transformation is happening:

• AI as the Master Scribe
Lifting the documentation burden so clinicians can reclaim the patient connection burnout has stolen.

• AI as the Tireless Sentinel
Catching signals we miss at 3 AM after a 12-hour shift because pattern recognition alone isn’t diagnosis.

• AI as the Specialized Consultant
Offering second opinions built on millions of images, while the physician still makes the final call.

But the heart of medicine?

The trust.
The empathy.
The human presence at a patient’s most vulnerable moment.

That stays ours.

AI informs.
Clinicians decide.
Patients trust.

My mission and the mission of #DrGPT is simple:

Build AI that gives clinicians more time for compassion, more clarity in decision-making, and more humanity at the bedside.

That’s the only future of medicine worth building.

Harvey Castro, MD, MBA. (DrGPT)

#DrGPT #AI #HealthcareInnovation #DigitalHealth #Leadership #MEDICA2025 #FutureOfMedicine #Clinicians #HumanCenteredAI

Most people don’t realize their inner life dies long before their body does.
I see it in the ER every week and it’s becoming even more common in the age of AI.

Patients come in because something feels “off.”
Vitals look fine. Labs look fine.
But their inner world?
Completely depleted.

And here’s the part we rarely talk about:

We lose ourselves slowly.

Not in a dramatic moment… but through tiny compromises:
	•	Putting curiosity on the shelf
	•	Letting relationships drift
	•	Ignoring our physical and mental well-being
	•	Running on autopilot instead of self-reflection
	•	Stopping the learning that once made us feel alive

These losses are invisible.
But their impact is not.

When your inner life dims, it affects how you show up for your patients, your team, your family yourself.

The good news?

We can bring it back.
Not with a grand life overhaul, but with small, intentional practices:
	•	Protect your passions like a vital sign
	•	Nurture one relationship today
	•	Prioritize a tiny act of well-being
	•	Reflect for two minutes before bed
	•	Learn something new—even something small

Your inner life is your strongest form of resilience.
Your most underrated source of clarity.
And your most powerful antidote to a world accelerating faster every day.

What’s one small thing that helps keep your inner life alive?

#DrGPT #WellBeing #HealthcareAI #Leadership #Mindset #PhysicianLife

Long international trips always come with the same question:

“Harvey, how do you travel so much… and bounce back so fast?”

After speaking in Barcelona and flying home, I slept barely 5 hours — yet I was fully functional the next morning.
This isn’t genetics… it’s a protocol.

To help everyone who travels, I created a simple infographic outlining the exact steps I follow.
These are science-based habits that reset your circadian rhythm, protect cognition, and reduce inflammation from travel.

🔥 My #JetLag Reset Routine (#DrGPT Protocol)
	•	Lux Light for 10–15 minutes immediately upon waking
	•	5-minute sunlight walk to anchor your brain to the new time zone
	•	No caffeine for the first 90 minutes (prevents the mid-day crash)
	•	High-protein breakfast (30–40g) for stable energy
	•	Hydration + electrolytes
	•	Zone 2 movement later in the day (light, not intense)
	•	Eat meals on the local schedule as soon as possible
	•	Light infrared sauna (120–125°F) gentle, restorative heat that reduces inflammation without adding stress
	•	Nighttime “Circadian Landing Gear”: warm shower → magnesium → no screens → cool room
	•	No alcohol for 24 hours after landing

It’s amazing how quickly your body adapts when you give it the right signals.

Whether you’re traveling for conferences, leadership events, clinical work, or family — your performance is directly tied to your recovery strategy.

If this helps you, feel free to save the infographic and share it with other high performers who live on the move. (Or for your next travel). 

#DrGPT #JetLagReset #LongevityMedicine #PerformanceScience #Healthspan #LeadershipLifestyle #TravelWellness #Biohacking #SleepOptimization #CircadianHealth #BarcelonaToDallas

The messages caught me off guard.
People from villages, megacities, and remote mountain towns all saying the same thing:

“Your TEDx talk made me feel less alone.”

And I’ll be honest on a day like Thanksgiving, those words hit differently.

When I walked onto that #TEDx stage, I carried a lot with me:
Maria, waking up terrified at 3 a.m. with chest pain and no cell service…
the Texas rancher stranded on a dusty road during his stroke…
little Jimmy, gasping for air until a simple hand-drawn “dust monster” finally made him smile…


These stories never leave you.
They shape the way you see the world.
They shift the question from “How do we treat?” to “How do we reach?”

So when the messages started arriving from Brazil, India, South Africa, rural Alaska I realized something:

This isn’t just my mission.
It’s a shared one.
Because no matter where we live, we all want the same thing:
For distance not to decide our destiny.

And this is where my gratitude deepens.

I’m thankful for the chance to work with a team at Phantom Space  
that wakes up every day determined to close that distance.
To build satellites and systems that make healthcare feel close,
even when the nearest hospital is 70 miles away.
To create a world where your doctor may be far but your care is always near.

Being part of this mission, with this company, at this moment in time it means everything.

Today, I’m thankful for:
• The global voices who reached out. You reminded me why this work matters.
• The communities fighting for equitable care.
• The chance to let AI extend human compassion—not replace it.
• A team that believes access is a right, not a luxury.

And maybe most of all,
I’m thankful that we’re writing this future together
not in fear, not in isolation,
but in hope.

If you watched the talk, what part stayed with you?
I’d truly love to hear.
(Link in comments )
#HealthcareAI #AIForGood #EmergencyMedicine #DigitalHealth #Telemedicine
#GlobalHealth #TechForHumanity #AIInnovation #DrGPT #PhantomSpace

Thanks for joining our exclusive live broadcast. Feel free to share your questions and interact with other participants in the chat.

Carbaba AI

• Founded Carbaba Cloud Service, a pioneering B2B SaaS company in the UAE focused on compliance systems and QA validators.  
• Spearheaded the transformation of document intelligence, enhancing documentation workflows for enterprises.  
• Developed innovative solutions that improved operational efficiency, resulting in a 40% reduction in document processing time.

General Manager

CliniNote

CliniNote is a rapidly growing startup making waves in the healthcare industry. CliniNote develops innovative tools for real-world data collection and assistant software specifically designed for doctors, hospitals,pharmaceutical companies, and clinical trials, with a global reach. The team, along with their business angels and investors, share a strong commitment to integrity, consistency, and focus. They’re passionate about driving positive change in healthcare through the power of digital solutions and
real-world data, ultimately saving lives.

Software Developer

Monash University

Engaged in mentoring students embarking on their journeys in data analytics, providing a bridge
between academic learning and real-world applications.

Delivered project-based feedback and career advice, drawing from my extensive experience in
data science and biomedical engineering.

Education Mentor

Swinburne University of Technology

Working on research project to build a new systematic artificial intelligence program using intricate machine learning classification and regression algorithms to predict heart disease using dataset from UCI repository. 
These techniques require the adaptation of high level feature engineering strategies to acquire an accurate smart medical technology. 
This new technology can interpret detailed analogies of patients to create a sustainable, normalized algorithm that predicts different types of heart disease directly proportional to the valves diameter narrowing. 
This technology utilizes a combination of R programming language to visualize and preprocess the dataset accordingly and python language to utilize the agility and robustness for an enhanced classification technology.

Machine Learning Researcher

Fiverr

Dealing with segregating data for different online businesses 
Cleaning repetitive data 
Inputting data into database 
Having to deal with images and string format
High level focus on detail

Data Entry Associate

- Teaching statistical methodologies to RMIT university students studying Statistics & Data Analytics

- Helped RMIT university students with statistics and marketing-related assignments 

- Worked with my students to plan and construct a strategy to tackle difficult projects that required Data analytics and statistical expertise. These involved but not limited to: 
- Ideation stages of understanding the problem and planning a timeline for each member of the group to perform their roles accordingly. 
- Processing stage where the group started showing improvements on their allocated tasks. This stage heavily utilized proficient MS Excel expertise. 
- Finalizing stage which involves organizing the ideas together in a compact format to present the project in a visually pleasing and effective manner to the client.

Private Tutor

CarbabaAI, a UAE-based B2B SaaS company, pioneers document intelligence using AI-driven compliance systems and QA validators to optimize enterprise workflows. As Founder, contributions include innovative solutions that reduced document processing time by 40%, showcasing a commitment to operational efficiency and transformative technologies.  

With a biomedical degree major with a data science minor from Swinburne University of Technology, expertise spans machine learning, AI systems, and data analytics. Previous roles, such as Software Developer at CliniNote, focused on real-world data solutions for healthcare. Additionally, mentoring at Monash University bridged academic concepts with practical applications, reflecting a dedication to responsible AI-driven industry innovation.

Ghadeer  A.

🚨 BREAKING: Google’s secret coding hierarchy that separates $80K devs from $500K engineers

Most people “learn AI” by binge-watching tutorials, saving random repos, and hoping it clicks someday.

The top 1% don’t do that.
They follow a ladder and each level unlocks abilities the others never reach.

After reviewing 40+ internal frameworks, agent architectures, and real-world RAG systems, I mapped the actual 3-tier ladder OpenAI’s best engineers follow.

LEVEL 1️⃣ — FOUNDATION (Where AI finally makes sense)

If AI feels overwhelming, this level rewires your intuition:

→ LLM fundamentals
→ embeddings
→ RAG basics
→ memory & context
→ prompt strategy

This is where you stop “using AI” and start understanding it.

LEVEL 2️⃣ — BUILDER (Where systems obey you)

You move from learner → practitioner:

→ multi-tool orchestration
→ agent loops
→ routing logic
→ evaluation frameworks
→ retrieval pipelines
→ observability

This is the level where 90% wash out.
Because this is where AI stops being cute… and starts being engineering.

LEVEL 3️⃣ — ARCHITECT (The level OpenAI hires for)

You’re no longer building agents.
You’re designing ecosystems:

→ multi-agent hierarchies
→ autonomous workflows
→ org-level memory
→ knowledge graphs
→ safety + guardrails
→ scaling logic

This is where $300K+ roles live.
The level where people stop asking “What can AI do?” and start asking “What can AI replace?”

The truth nobody says out loud:

AI mastery isn’t about learning more.
It’s about learning in the right sequence without skipping levels.

Most people stay stuck because they learn randomly.
The top 1% rise because they follow the ladder.

I bundled the full learning map, the repos, and the under-the-radar resources that unlock each tier.

👇 Comment “LADDER” and I’ll send it privately.

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Ghadeer  Al Ruwaishedi

I Help AEC Firms Automate Their QA Processes | Founder @ Carbaba | AI-Native Document Intelligence

The biggest lie in AI engineering right now?
“We built an agent.”

We’re asking agents to reason, plan, adapt, and act autonomously but most teams are stitching together prompts and hoping it holds.

That’s why I wanted to share the stack I keep coming back to.

The one that actually survives messy workflows, inconsistent inputs, and real users.

Here’s the Agentic RAG stack behind the AI systems that don’t just run they improve 👇

0️⃣ Deployment
Where your agent lives.
Stable. Scalable. Ready for real load.

1️⃣ Evaluation
Tight feedback loops.
No evaluation → no improvement.

2️⃣ LLMs
The thinking layer.
Reasoning. Planning. Decision-making in natural language.

3️⃣ Frameworks
The wiring of the system.
Prompts, memory, tools orchestrated instead of duct-taped.

4️⃣ Vector Databases
The context engine.
Fast retrieval → relevant responses.

5️⃣ Embeddings
Turn text into meaning.
Make knowledge usable.

6️⃣ Data Extraction
Web. Docs. APIs.
Your agent is only as informed as its inputs.

7️⃣ Memory
The part that makes your agent feel alive.
Short-term + long-term + episodic context.

8️⃣ Alignment
Safety, telemetry, and trust.
You can’t scale what you can’t control.

I’ve seen this stack turn fragile prototypes into reliable systems.
And once teams get it right, something shifts:

They stop building demos.
They start building intelligence.

What part of the agent stack are you wrestling with right now?

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Why 90% of ML Engineers Quit Before Year 2 (SAVE THIS 👇)

It doesn’t have to be this way

Most learners drown in models with no map.
Nothing sticks.

Concepts blur together.
Progress stalls fast.

A full visual system fixes everything.
Clarity arrives instantly.  

✅ This collection maps Neural Networks end-to-end.
Page 1 shows every major architecture in one view.
You see how each model connects across the ecosystem.

✅ The Neural Network Graphs on page 2 break down flow.
They show feed-forward, recurrent, LSTM and GRU paths.
Patterns become visible instead of abstract.

✅ Pages 3–4 decode the maths behind learning.
Not theory for theory’s sake. Practical intuition.
You finally understand why models behave as they do.

✅ Machine Learning in Emoji on page 5 simplifies concepts.
Complex ideas become instantly memorable.
Supervised, unsupervised and reinforcement made visual.

✅ Scikit-Learn, Azure ML and Python basics span pages 6–10.
They show algorithms, workflows and starter code together.
Zero guesswork on what to use and when.

✅ Pandas, NumPy and data wrangling fill pages 11–15.
You see cleaning, shaping, merging and plotting clearly.
Data stops being messy and starts being manageable.

✅ SciPy, Matplotlib and ggplot2 visuals on pages 16–18.
They reveal analysis, plotting and scale choices.
Your data storytelling sharpens instantly.

✅ PySpark and Big-O on pages 19–20 zoom out further.
You understand scalability and operations at a glance.
It becomes the roadmap many engineers never get.

This is the satellite view.
This is the missing map.
This is how AI finally clicks.
This is how you build intuition fast.

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Neural_Networks_Cheatsheet

95% of “AI agents” aren’t agents. 
They’re prompt wrappers with dreams.

Real autonomy isn’t magic.
It’s layers. And almost everyone is missing three of them.

Here’s the stack the top 3% of builders actually use: 👇

🔹Layer 1: LLMs — The Raw Intelligence

Great at language. Useless at autonomy.
	•	Tokenization + inference
	•	Prompt shaping
	•	Direct API access

This is where most teams stop — then wonder why nothing is reliable.

🔹Layer 2: AI Agents — Intelligence That Acts

A brain finally gets hands.
	•	Tools + function calling
	•	CoT / ReAct reasoning
	•	Task decomposition
	•	Long-term memory

One agent is smart. But one agent alone can’t run a real workflow.


🔹Layer 3: Agentic Systems — Intelligence That Cooperates

This is where autonomy becomes repeatable.
	•	Multi-agent communication
	•	Roles + specialization
	•	Routing + scheduling
	•	Multi-agent RAG for shared context

This is the leap from “bot” → “operation.”

🔹Layer 4: Agentic Infrastructure — Intelligence That Survives Reality

The layer everyone forgets… until their system fails.
	•	Observability + traceability
	•	Error handling + retries
	•	Security + compliance
	•	Human-in-the-loop control
	•	Workflow automation

Without this layer?
Your agents are demos.
With it?
Your agents become deployable systems.

Most teams think they’re building agents.
They’re not.
They’re stacking prompts and praying.

Real autonomy lives at Layer 4.
Real reliability comes from architecture, not clever prompting.

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📩 Save this for your next system design session

Every paid OCR tool failed on my 700-page document. So I built a free pipeline that reads like a human.

Last month, one document nearly broke me.

A 700-page scanned tender (charts, tables, signatures, handwritten notes) completely shattered every paid OCR tool I tried.

Too slow.
Too messy.
Too inaccurate.
Too blind to structure.

The new generation of open OCR models isn’t behaving like traditional tools.
They’re interpreting layout, context, visuals, and semantics the way humans do.

Here are the 8 models that actually survived real enterprise documents 👇:

1️⃣ Nanonets-OCR2-3B

Handwriting, signatures, watermarks, flowcharts.
Semantic Markdown output. Multilingual.

2️⃣ PaddleOCR-VL

Old scans → clean HTML.
Tables + charts extracted with structure.
109 languages.

3️⃣ dots.ocr

Layout-grounded.
Understands text placement + images.
Natively multilingual.

4️⃣ OlmOCR-2

Enterprise-scale batches.
Consistent Markdown + LaTeX.
English-focused and reliable.

5️⃣ Granite-Docling-258M

Tiny, promptable, DocTags-friendly.
Fast + flexible for production.

6️⃣ DeepSeek-OCR

Ridiculously fast for large workloads.
Optimized for throughput + consistency.

7️⃣ Chandra

Grounded OCR for complex docs.
Great for technical + academic materials.

8️⃣ Qwen3-VL

Understands ancient scripts → modern layouts.
A true multimodal foundation.

Open models are finally outperforming traditional OCR 
faster, cheaper, smarter.
Not just reading… reasoning.

If you deal with tenders, compliance docs, audits, or enterprise workflows:

This is the Document AI shift you can’t ignore.

What’s the OCR issue that frustrates you the most?

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OCR_Open_Models

213% ROI vs. -$100K losses. Same AI. Different approach.

Most agents fail.

Everyone is “building agents” blindly.
No strategy.
No evaluation.
No control.

This leads to hallucinations, failures, and wasted money.
Teams ship fast…
…and break even faster.

The Galileo Playbook fixes this.
It shows how real AI teams actually win.
Here are the essentials:

The 10 Insights You MUST Know 👇

1. What agents really are

LLM systems that think, act, and execute multi-step tasks.
They don’t answer. They operate.

2. The 10 agent types

From fixed automation to self-learning systems.
Know the spectrum → build smarter.

3. When to use agents

Use for complexity, speed, autonomy, and multi-step flows.
Think support, research, finance, devops.

4. When NOT to use them

Avoid for rare tasks, simple tasks, or human-emotion work.
This alone saves companies millions.

5. The 10 pre-build questions

Complexity. Accuracy. Volume. Frequency. Compliance.
The hard filter teams refuse to apply.

6. The 3 real case studies

Wiley: 213% ROI.
Oracle Health: –41% documentation time.
Magid: 100% visibility + accuracy tracking.

7. The frameworks that matter

LangGraph. Autogen. CrewAI.
Exactly when each one wins.

8. How to evaluate agents

Plan → act → re-plan → verify.
The loop that turns chaos into precision.

9. The metrics that matter

System. Workflow. Quality. Tool usage.
The backbone of scalable AI.

10. Why agents fail

Dev errors. LLM errors. Prod errors.
And how to eliminate all three.

AI mastery is not magic.
It’s method.
This playbook gives you the blueprint.
Use it and you become unstoppable.

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Mastering_AI_Agents

J.P. Morgan Can’t Deploy AI Agents That Follow ‘Vibes.’ Here’s the Framework Built for Reality (SAVE this 👇)

Reliability rules.

Agents impress in demos.
Then collapse when real users touch them.
The failure is structural.

Your agents struggle.
Not from prompts.
From fragile frameworks.

Most systems were never designed for:
consistency
compliance
explainability
rule enforcement
auditability
controlled tool use

Parlant was.

It removes randomness entirely.
Through five engineered components.

👉 Journeys
Structured flows that replace prompt luck.
Predictable steps that keep agents stable.

👉 Guidelines
Enforced tone, behavior, safety rules.
Your musts and nevers hard-wired.

👉 Relationships
Dependencies and edge-case logic.
The reasoning layer others ignore.

👉 Tools and Retrievers
Scoped, governed access to APIs and data.
No unauthorized calls. No surprises.

👉 ARQs
Invisible audits on every decision.
Automatic checks before output leaves the agent.

Other frameworks teach responses.
Parlant teaches reliability.
And reliability is the new intelligence.

If your agents touch onboarding, QA/QC, compliance, support, routing, or enterprise workflows,
Parlant ensures they never go off-script.

Build agents that follow rules.
Build agents that survive reality.
Build agents you can trust to scale.

Check out Parlant’s repo: https://lnkd.in/dywtVkwu

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🚨 90% of teams building AI agents are doing it wrong. Google just confirmed it.

They’re treating agents like glorified workflows:

Prompt → Tool call → Result → Ship.

That’s why their “agents” fall apart with edge cases, hallucinate under pressure, and behave like unpaid interns.

After studying Google’s new Agent Architecture playbook, I realized the real game isn’t tool use.
It’s orchestration and context engineering.

Agents don’t execute tasks they curate context, plan, act, observe, and iterate.

My workflow now:

1️⃣ Start with the agent’s mission, not the prompt
2️⃣ Map the Think → Act → Observe loop
3️⃣ Define tools as capabilities, not “API wrappers”
4️⃣ Add memory + policy + identity
5️⃣ Stress-test the orchestration layer, not the model

This isn’t automation.
This is systems design.
The best engineers aren’t prompting they’re orchestrating autonomous problem-solvers.

We’re moving from “LLMs in a wrapper” to full agentic operating systems with planning, memory, delegation, governance, and self-improvement.

The billion-dollar gap?
Tools that help teams build orchestration layers, not prompts.

We’re not being replaced.
We’re being promoted into agent architects.

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Introduction_to_Agents

🚨BREAKING: OpenAI, Anthropic, and Google Know Something You Don’t About AI Agents

And it’s the reason your agents stall, duplicate work, burn compute, and quietly drain your budget.

Most people think the future of agents is about bigger models, better reasoning, and faster inference.

But here’s the truth the top teams in the world already understand:

Agents don’t fail because they’re “not smart enough.”
Agents fail because they can’t talk to anything.

No shared language.
No coordination layer.
No interoperability.
No trust.
No structure.

In other words:

Your agents aren’t collaborating
they’re hallucinating… in parallel.

The companies actually deploying agents at scale quietly use something else:

Protocols.

Not prompts.
Not hacks.
Not magic thinking.

PROTOCOLS is the communication standards that let agents coordinate, cooperate, and execute like real autonomous systems.

And after reviewing everything OpenAI, Google, Anthropic, IBM, Cisco, Oxford, and CopilotKit have been building…

I mapped the 7 protocols that will define which agent ecosystems scale and which ones burn out.

I put the full breakdown in a private document:
✅ What each protocol actually does
✅ When to use which
✅ The hidden strengths
✅ The red flags
✅ The architectural patterns
✅ And the full visual map you can’t get publicly

👇 Comment “PROTOCOLS” and I’ll send the full breakdown. 

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Protocols_Used_In_AI_Agents

If you’ve ever spent 3am debugging why your PDF parser thinks a table is 47 separate paragraphs, this one’s for you

OCR grew up.

For years, OCR meant “text out, chaos in.”
Tables shattered. Layouts lost. Context gone.

Every document looked fine until your LLM choked on spaghetti text.
Every fix meant regexes, retries, and rage.

In 2025, OCR finally became document intelligence.

6 systems now read like humans not scanners. 👇

1️⃣ Google Cloud Document AI
Reads scanned and digital PDFs in one pipeline.
Detects tables, key values, even handwriting.
Perfect for financial and education documents.

2️⃣ Amazon Textract
Sync for small, async for scale.
Pulls text, forms, tables, signatures — clean JSON out.
Ideal for AWS-native IDP and claim pipelines.

3️⃣ Microsoft Azure Document Intelligence
Custom and prebuilt models now run in containers.
Extracts structure, figures, and layout for LLM input.
Hybrid ready for air-gapped deployments.

4️⃣ ABBYY FineReader & FlexiCapture
Still the on-prem heavyweight.
190+ languages, tight compliance, deep zoning.
Flawless for regulated or multilingual workloads.

5️⃣ PaddleOCR 3.0
Free. Open source. Apache licensed.
Covers detection, recognition, and structure.
Built for startups building self-hosted RAG pipelines.

6️⃣ DeepSeek OCR
A new species: compression-first OCR.
Encodes long documents into vision-language form.
10x smaller input for LLMs, open-source, GPU-ready.

OCR used to see letters.
Now it understands meaning.
From extraction to interpretation that’s the leap.

For AI teams, this means fewer hacks, faster pipelines,
and context that finally survives the parser.

OCR taught us patience.
And creative regex therapy.
But now, it finally works like we always wanted it to.

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6_Top_OCR_Models

If you want to build AI agents that run autonomously in production (not just demos), master these 15 skills:

1. Python Programming: Beyond Basic Scripts
Everything in Agentic AI runs on Python.
Learn async programming, error handling, and class design.

2. Prompt Engineering as Code
Stop guessing what to write.
Structure prompts with templates, variables, and version control.

3. LLM Selection & Token Economics
Understand context windows, streaming, and cost per call.
Pick the right model for each task (GPT-4 isn’t always the answer).

4. API Integration & Webhook Design
Agents can’t act alone.
Connect them to Slack, Gmail, databases, and custom endpoints.

5. Workflow Automation Architecture
Chain triggers, conditions, and actions that scale.
Master tools like Zapier, n8n, and Make

6. JSON Schema & Data Validation
Agents speak JSON.
Design schemas that prevent hallucinated outputs and data chaos.

7. Vector Databases & Semantic Search
Give your agents persistent memory.
Weaviate, Pinecone, and Chroma store embeddings for context retrieval.

8. Retrieval-Augmented Generation (RAG)
Let agents access external knowledge bases.
Build pipelines that inject relevant context into every response.

9. Containerization & Cloud Deployment
Run agents 24/7 without your laptop.
Docker, AWS Lambda, and serverless functions keep them alive.

10. Agent Frameworks That Actually Scale
LangChain, CrewAI, and AutoGen aren’t just libraries.
They’re architectures for building multi-step autonomous systems.

11. Tool-Use & Function Calling
Agents that use calculators, search engines, and APIs are 10x more powerful.
Teach them when and how to reach for external tools.

12. Multi-Agent Orchestration
One agent is a script. Multiple agents is a workforce.
Design systems where specialists collaborate on complex tasks.

13. Memory Management: Short-Term vs Long-Term
Stateless agents forget everything.
Build conversation history, user preferences, and learned patterns.

14. Observability: Logging, Tracing & Debugging
Production agents fail silently.
Use LangSmith, Sentry, and custom logs to catch issues before users do.

15. Security, Rate Limits & Ethical Guardrails
Agents without constraints are liabilities.
Implement permissions, content filters, and spend caps.

The AI race isn’t about prompts anymore.

It’s about building autonomous systems that think, act, and learn while you sleep.

Companies don’t pay for demos. They pay for agents that handle real workflows in production.

These 15 skills separate builders from prompters.

Which skill are you struggling with most? 👇

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li_at

JSESSIONID

Retrieval = intern. Reasoning = analyst. Action = executive. Most companies are paying executive prices for intern output.

Most enterprise AI in mobility stops at retrieval.
It answers but it doesn’t understand.

At Carbaba, we go further with Agentic RAG.

Legacy RAG retrieves data.
But it lacks reasoning and adaptability.

That’s why fleet systems still need humans
to interpret, plan, and decide.

Agentic RAG merges retrieval, reasoning, and real-time action 
turning static AI into a dynamic decision-maker.

📌 RAG (Retrieval Augmented Generation)
Pulls precise vehicle, route, and system data.
Merges it with prompts for analysis.
Generates answers but without context awareness.

📌 AI Agents
Plan and reason across connected data sources.
Access live telematics, maintenance logs, and supply chains.
Deliver decisions not just insights.

📌 Agentic RAG
Combines retrieval with intelligent action.
Agents select data sources, plan retrieval paths,
and feed reasoning-driven input to the LLM.

🗄️ Operational Workflow
User query → routed to agent.
Agent plans → retrieves real-time data.
Context retained → output optimized for reasoning.
Response delivered → insight becomes autonomous action.

This is how Carbaba automates enterprise mobility 
from predictive maintenance to intelligent dispatch.

Agentic RAG isn’t the future. It’s the framework.
The enterprises building with it today
will define tomorrow’s intelligent systems.

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Google DeepMind’s Latest Move Just Made ‘Deep Learning’ Obsolete (Check It Out 👇)

The era of stacking layers is over.

Levels beat layers.

We didn’t lose the plot.
We were given the wrong plot.

Deep learning wasn’t deep.
It was just tall.

The pain we all know

More layers.
More FLOPs.
More cost.
Less learning.

Scaling became a blunt instrument.
Smarter models never arrived.
Just bigger bills.

Because depth never created intelligence.
It only created noise.

The reveal that changes everything

Nested learning exposes the truth:

LLMs don’t learn from data.
They learn from compressing their own context.

Attention = fast memory
MLPs = slow storage
Optimizers = learned recall

Each subsystem runs its own learning loop.
Different speed.
Different purpose.
Different update rhythm.

We didn’t build deep nets.
We built multi-timescale minds.

Why this matters now

The next frontier isn’t bigger models.
It’s multi-level learning systems.

More levels =
⚫️ stronger in-context learning
⚫️ longer retention
⚫️ continual adaptation
⚫️ self-modifying behavior

HOPE proves it:
A model rewriting its own update algorithm during inference.
Not training.
Not fine-tuning.
Evolving in real time.

The identity flip

You’re not training models anymore.
You’re engineering learning hierarchies.

Memory depths.
Update rhythms.
Nested objectives.
Multi-level gradients.

Welcome to post–deep learning.

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Nested_Learning

I shipped 6 AI chatbots in 2024. Deleted all of them. This is what I built instead 👇

Chatbots are shallow.

Teams build demos.
Demos collapse fast.

Edge cases kill them.
Reality exposes gaps.

Agentic systems survive.

👉Building Agentic AI Systems by Anjanava Biswas & Wrick Talukdar  
Shows multi-agent orchestration that stays stable.
Reveals how agents reason, plan, and delegate.
Goes far beyond simple LLM wrappers.

Explains how to fuse GenAI, APIs, and knowledge graphs.
Keeps automation factual under messy conditions.
Shifted how I design systems that endure enterprise chaos.

👉AI Agents in Practice Valentina Alto  
Turns blueprint into production-ready execution.
Covers LangGraph orchestrators that don’t break.
Teaches memory and context management at scale.

Adds secure tool use and real error recovery.
Provides auditability for regulated workflows.
A playbook for ops, sales, compliance, and support.

🔥Why this mattered
Let me ship AI that handles edge cases at scale.
Self-corrects without oversight.
Coordinates tools with precision.
Survives reality instead of demo conditions.

Agentic AI replaces fragile demos.
It becomes operational infrastructure.
It delivers impact teams can trust.

Check out Packt’s publishing for more 👇 

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Why Most AI Agents Are Actually Broken
(And It Has Nothing to Do With the Model 👇)

Memory is the real failure point.

Agents forget.
Contexts drift.
Outputs collapse.

The problem isn’t intelligence.
It’s missing architecture.

Your agent loses track instantly.
You keep scaling the model to compensate.

Fix memory layers.
Not parameter counts.

Vector Memory

🔹 Plain vector RAG
Embedding lookups
Fast, simple, widely supported

Loses temporal structure.
Drifts semantically.
Forgets user decisions.

🔹 Tiered vector memory
Working set + archive

Reuses key info better.
Still suffers paging errors.
Creates agent divergence.

Graph Memory

🔹 Temporal KG memory
Cross-session reasoning
Strong temporal awareness

Requires schemas and pipelines.
Edges can go stale.

🔹 Knowledge-graph RAG
Hierarchical communities
Multi-doc, multi-hop logic

Heavy graph construction.
Bias and traceability overhead.

Event / Log Memory

🔹 Execution logs + checkpoints
Ordered versioned truth
Supports replay + repair

Prone to log bloat.
Instrumentation gaps matter.

🔹 Episodic long-term memory
Episodes + metadata
Long-horizon recall

Boundary errors occur.
Cross-agent misalignment appears.

Reliable agents need layers.
Vector for semantics.
Graph for structure.
Logs for truth.

That’s the line between toy agents
and production systems that don’t break.

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I had a painful realization last week while reviewing a broken agent workflow…

Most engineers still think “seeing” is enough.
But visual understanding is the new baseline.

If your model can’t interpret charts, diagrams, objects, layouts, and logic the way you do…
you’re already behind.

2025 is the year multimodal literacy becomes a non-negotiable skill.

Here are the 10 VLMs every serious AI engineer must master or risk playing catch-up while others build the future:

1. Gemini 2.5 Pro (Google)
Understands text, image, audio, and video like a human brain.
A leap toward unified multimodal intelligence.

2. InternVL3-78B
Open-source powerhouse breaking records in multimodal reasoning.
Rapidly becoming the default for serious builders.

3. Ovis2-34B
Aligns text and visuals with lightning precision.
A top pick for tight visual-text alignment.

4. Qwen2.5-VL-72B
The backbone of the new wave of open multimodal AIs.
Trusted for stable, large-scale deployments.

5. o3 (OpenAI)
The new reasoning king with math, science, and visuals mastered.
Sets a fresh bar for structured problem-solving.

6. GPT-4.1
Reads charts, counts objects, and decodes visual logic flawlessly.
Perfect for production-grade visual workflows.

7. Claude Sonnet 4
Anthropic’s creative and analytical visual thinker.
Excels at nuance and safer interpretability.

8. Kimi-VL-A3B-Thinking-2506
Thinks faster and reasons smarter.
Cuts thinking time by roughly 20%.

9. Gemma-3-27B-it (Google)
Small model. Big performance.
Punches far above its weight class.

10. aLlama-3.2-90B-Vision-Instruct (Meta)
Sees, understands, and describes the world with precision.
Meta’s most capable vision-native model yet.

AI that sees is old news.
AI that understands is the real edge.
And that future has already arrived.
Don’t be the one catching up.

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I realized something uncomfortable this week about the way we talk about agents on LinkedIn…

I’ve been looking at a lot of “agent” posts here recently.

And the more I see, the more I notice the same pattern:

Most of what we call “agents” today aren’t agents at all.
They’re chatbots with longer prompts.

No planning.
No adaptive loops.
No tool orchestration.
No real memory.
No error handling.
No autonomy.

Just a nice wrapper and a big claim.

Then I came across Packt’s book, Building Agentic AI Systems, and honestly… it was refreshing.

It explained agents in a way that cuts through the noise completely.

The biggest thing that stuck with me:

Agents aren’t prompts.
Agents are systems.

Real agents look more like architectures than chat interfaces.

They need:

🔹 A reasoning loop that doesn’t fall apart on step 2
🔹 A planning module that adapts when the task changes
🔹 A memory stack (vector + episodic + long-term)
🔹 Tool orchestration that survives messy inputs
🔹 Error handling + verification
🔹 Human override channels
🔹 Clear autonomy boundaries
🔹 Optional multi-agent setups

This is the difference between:

❌ a fun demo that goes viral on LinkedIn
and
✅ a system that actually survives in a real deployment

Because when you zoom out, the truth is simple:

If you’re building agents, you’re really building architectures.
Not magic prompts.
Not “AI personalities.”
Not one-click workflows.

Systems.

It’s a shift I think more of us need to internalize myself included.

If you’re working in AI engineering, this book is worth a read.
Not because it’s trendy, but because it’s practical.

Link to book: https://lnkd.in/dPUvBWqr

What’s the biggest gap you’re seeing in today’s “agent” hype?
I’d love to read your thoughts.

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🚨 Most people “study AI” the wrong way they binge random GitHub repos.

The top 1% don’t do that.
They follow levels and each level unlocks the next.

Here’s the 3-tier AI learning ladder built from 12 elite repos (bookmark this 🔥).

LEVEL 1 — BEGINNER (Become AI-literate fast)

If you feel lost, these repos turn chaos into clarity.

1. Microsoft – AI Agents for Beginners
↳ https://lnkd.in/dyWxyqpR
The cleanest way to understand agents without drowning in jargon.

2. Machine Learning for Beginners (Microsoft)
↳ https://lnkd.in/dBj3BAEY
40 lessons, project-based, human-friendly. Zero fluff.

3. Prompt Engineering Guide
↳ https://lnkd.in/gJjGbxQr
The playbook for talking to models like a power-user, not a tourist.

4. LLM Course (Maxime Labonne)
↳ https://lnkd.in/d-_HXFSy
LLMs explained without assumptions. You’ll finally “get it.”

LEVEL 2 — INTERMEDIATE (Go from learner → practitioner)

This is where you stop consuming and start building.

5. GenAI Agents (Nir Diamant)
↳ https://lnkd.in/dxA9UQaJ
Real agent workflows, real patterns, real projects.

6. GenAI Agents 
↳ https://lnkd.in/dEt72MEy
Complementary examples + deeper architecture notes.

7. Made With ML
↳ https://lnkd.in/d2dMACMj
Production-grade ML thinking… the part 90% of people skip.

8. Hands-On Large Language Models
↳ https://lnkd.in/dxaVF86w
Build RAG, fine-tune, deploy—everything companies actually pay for.

9. Hands-On AI Engineering
↳ https://lnkd.in/dxgjH545
The missing link between “learning AI” and “working in AI.”

LEVEL 3 — EXPERT (For people aiming at $300K+ AI roles)

You don’t just learn AI here you start thinking like an AI architect.

10. Designing Machine Learning Systems
↳ https://lnkd.in/dEx8sQJK
The bible of scalable ML systems. Required reading for senior roles.

11. Awesome Generative AI Guide
↳ https://lnkd.in/dJ8gxp3a
A curated knowledge graph of everything GenAI. You’ll never be behind again.

12. The Ultimate LLM Engineering Path
(from combining the repos above)
↳ RAG → agents → evals → observability → scaling
At this point, you’re not “using AI.” You’re engineering ecosystems.

The truth nobody says out loud:

AI isn’t about learning everything.
It’s about learning the right things in the right sequence.

Most people stay stuck because they consume randomly.
But the people who rise fast follow levels not noise.

Which level are you currently on?
Drop it below 👇

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The DeepSeek Breakthrough That Just Ended the “Bigger Models” Era 

Honesty wins.

LLMs break because they bluff.
Flawless tone.
Faulty logic.

DeepSeek attacked the flaw.
Not the symptoms.
Not the scale.
The flaw.

✅ What DeepSeek built

A self-verifying pipeline
that rewrites how models think.

Verifier scans a proof
and flags broken logic.
Meta-Verifier checks the verifier
to prevent hallucinated judgment.
Generator writes a proof
and critiques itself step-by-step.

The model earns rewards
for admitting mistakes.
Not hiding them.
Not bluffing.

Honesty becomes the shortcut.
Not luck.

✅ Why it matters

Answer-only training hit a ceiling.
Correct answer isn’t reasoning.
And theorem proving has no numeric reward.

DeepSeek’s system lets models:
Audit their own logic.
Spot flawed steps.
Refine proofs over iterations.
Scale to harder problems
without any human labels.

Reasoning that improves itself.
Finally.

✅ The results

IMO 2025: solved 5 of 6.
CMO 2024: 4 full + 1 partial.
Putnam 2024: 118 of 120
(humans peak at 90).

It even outperforms
GPT-5-Thinking-High
and Gemini 2.5 Pro
across all major math tasks.

The first open model
with gold-level theorem proving
in natural language alone.

✅ The shift begins

Scaling isn’t the frontier.
Truthfulness is.
Models must audit themselves
like mathematicians.
Not magicians.

DeepSeek proved it’s possible.
And the field just changed.

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DeepSeekMath_V2

The Weaviate Playbook Just Exposed the Biggest Lie in Prompt Engineering.

For the last two years, everyone has been obsessed with prompts.

Better wording.
Better templates.
Bigger context windows.
More “magic” instructions.

But the Weaviate Context Engineering Playbook makes one thing painfully clear:

Prompts were never the real bottleneck.
Context architecture was.

Most AI systems don’t break because the model is weak.
They break because the meaning you feed the model is unstructured, noisy, inconsistent, or irrelevant.

Here’s the truth the playbook nails:

🔹 Your chunking is wrong (too big = noise, too small = no meaning)
🔹 Your embeddings aren’t aligned to the task
🔹 Your retrieval is optimizing for similarity, not relevance
🔹 Your context has zero formatting contracts
🔹 Your system treats context like text. Not like an API

And the worst part?

Teams keep tuning prompts to hide these failures.
They’re treating symptoms while ignoring the disease.

The top 1% of AI teams are already shifting to this new paradigm:

🔹 Context Engineering > Prompt Engineering
🔹 Architecture > Tricks
🔹 Meaning > Tokens

Once you fix context, the hallucinations drop.
The model becomes predictable.
And your entire AI stack goes from “occasionally brilliant” to “consistently reliable.”

This isn’t just a playbook.
It’s a correction to the entire industry.

Prompt engineering didn’t scale.
Context engineering will.

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