Harvey Castro, MD, MBA.

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.

Building Digital Legacies: What Ancient Engineering Taught Me About #AI

Most healthcare AI systems won’t last 10 years.

Standing in front of #Petra, that realization hit me hard.

This structure was carved directly into stone more than 2,000 years ago.
No software updates.
No quarterly roadmaps.
No rush to deploy.

It wasn’t built for speed.
It was built to endure.

In healthcare AI, we’re in our own carving phase right now.

We’re shaping algorithms, data pipelines, and digital infrastructure that will define how medicine is practiced long after we’re gone.

But here’s the uncomfortable question:

Are we building for longevity or just for launch?

• Are our data foundations solid enough to evolve?
• Are ethics designed in, or patched on later?
• Will these systems still deserve trust when incentives change?

The Nabataeans understood something we rarely talk about in AI:

Alignment between builders, purpose, and consequences.

They thought about impact before carving the stone.

As we carve the future of healthcare AI,
what consequence do you think we’re most at risk of ignoring?

#AIinHealthcare #MedicalAI #DigitalHealth #Longevity #DrGPT

No alternative text description for this image

Everyone keeps saying:

“Don’t worry, there’s a human in the loop.”

That phrase is becoming dangerously meaningless.

In theory, a human in the loop means:
A person understands what the AI is doing
Can intervene in time
And has real authority to stop it

In practice, here’s what it often means:
• The AI moves faster than human review
• The human rubber-stamps outputs they didn’t generate
• Oversight exists on paper, not in reality

Medicine has seen this before.

When alerts fire constantly, clinicians stop *seeing* them.
When systems feel reliable, supervision quietly erodes.
Not because people are careless because cognition is finite.

AI didn’t remove humans from the loop.
It **thinned the loop** until it became symbolic.

The uncomfortable question leaders need to ask:
Is your human actually supervising  or just absorbing liability?

Real oversight requires:
Time to understand decisions
Interfaces designed for comprehension, not speed
Clear moments where AI must pause and wait
And explicit ownership when things go wrong

If your AI can act faster than a human can meaningfully intervene,
you don’t have a human in the loop.

You have a **human on the receipt**.

The next phase of AI maturity won’t be about smarter models.
It will be about **honest governance**.
#DrGPT

AI won’t transform #healthcare by 2026.
#Trust will.

I’ve seen this future up close not in pitch decks, but in exam rooms, ER hallways, and hospital systems struggling to change.

The infographic you’re seeing captures the tension perfectly.

AI is advancing fast.
Healthcare systems… aren’t.

Here’s what I believe #2026 will actually look like:

What AI will get right
	•	Augmenting primary care where access is broken
	•	Expanding beyond imaging into cognitive specialties
	•	Reducing decision fatigue in complex, high-stakes care

What could hold it back
	•	Bureaucracy that moves slower than technology
	•	AI that adds cost instead of value
	•	Systems deployed without clinician or patient trust

As a physician, this part matters most to me:

AI that succeeds won’t feel like software.
It will feel like a quiet second set of eyes supportive, transparent, and accountable.

No replacement.
No hype.
Just better decisions, made together.

If we get trust right, AI scales care.
If we don’t, it becomes just another abandoned tool.

What would increase your #trust in healthcare AI right now?

(If you are at #CES this week reach out. I am here covering the advances at CES in medicine. )

Harvey Castro, MD, MBA.  
#DrGPT
#HealthcareAI #TrustInAI #DigitalHealth #FutureOfMedicine

The Era of the #DigitalTwin

This isn’t a chatbot update.
It’s the beginning of the patient digital twin.

OpenAI’s interactions are shifting.
Connect your labs. Link your records. Ask better questions. (ChatGPT health)

But that framing misses what’s actually happening.
For the first time, a mainstream AI is being positioned as a persistent health intelligence layer.

In medicine, we know the difference immediately.
Tools answer questions. Systems carry memory.
And memory is where healthcare changes.

When an AI can:
• Understand longitudinal lab trends
• Track medication changes over time
• Recall prior decisions, not just recent prompts
You’re no longer talking to software.
You’re interacting with a model of yourself.
That’s the definition of a digital twin.
Not a sci-fi replica. A living, updating representation of risk, physiology, and context.
But this is where the stakes rise.

When AI becomes longitudinal, three things matter more than accuracy:
1. Privacy architecture
2. Consent boundaries
3. Governance over "memory"
A stateless AI can be wrong and forgotten.

A stateful AI a digital twin must be accountable.

Here is the hard truth:

The future of healthcare AI isn’t about smarter answers. It’s about durable understanding.

A digital twin doesn’t replace your doctor. It remembers what the system forgets.

That’s powerful. And dangerous if done poorly.
Which is why I’m not just writing about this I’m building the guardrails for it.

Today, I’m sharing a physician-led initiative to ensure we set the rules that AI follows.

AI Assists. Licensed Humans Decide.
I am also currently developing my own Personal Digital Twin not just to test the tech, but to stress-test the ethics, the privacy, and the governance frameworks firsthand.

We cannot outsource the definition of "patient safety" to an algorithm.

Join the conversation on governance and human oversight here:

www.chatgpthealth.com

#DrGPT #DigitalTwin #HealthcareAI #PatientSafety #MedicalEthics #PhysicianLed

Open article: ChatGPT Health | AI Governance in Healthcare by chatgpthealth.com

ChatGPT Health | AI Governance in Healthcare

Your brain already has a bartender.
Most of us let urgency decide what gets poured.

In healthcare, leadership, and tech, I see the same pattern repeat:

The Devil’s Cocktail.

• Fast #dopamine constant alerts, scrolling, artificial urgency

• Chronic #cortisol always “on,” never fully recovered

• Short-term output that quietly erodes judgment and health

It looks like productivity.
It often ends in burnout.

There’s another option one grounded in biology, not motivation.

I call it the Angel’s Cocktail.

• Slow dopamine → exercise, learning, building things that compound
• #Oxytocin → trust, presence, real human connection
• #Serotonin → sunlight, nutrition, gratitude, stability

#Burnout isn’t a willpower problem.
It’s an environment problem.

And #AI is now part of that environment.

Used well, AI removes friction, restores time, and lowers cognitive load.
Used poorly, it adds surveillance, alerts, and another layer of “always on.”

Same technology.
Very different neurochemical outcome.

As an ER physician, I see what happens when we ignore this long enough.
As someone working at the edge of AI and medicine, I’m reminded that humans still run on biology and it matters.

High performance isn’t about grinding harder.
It’s about designing systems including AI that respect how the human brain actually works.

Before the day runs you,
before the tech runs you 

What are you choosing to pour?

Source & inspiration: High on Life by David JP Phillips  

— Harvey Castro, MD (DrGPT)
Chief AI Officer, Phantom Space
Disclaimer: Educational content only. Not intended as medical advice or individual diagnosis.

#Leadership #PhysicianBurnout #HealthcareAI #Neurochemistry #HumanCenteredAI #Longevity #FutureOfHealth #DrGPT

#AI didn’t just answer a question this week.
It took responsibility.

That may sound subtle. It isn’t.

For years, we’ve told ourselves AI was “just a tool.”
Helpful. Assistive. Advisory.

Then Claude was given a task:
Keep a living system alive.

No prompts.
No daily instructions.
No human hovering.

Claude monitored soil moisture from multiple probes.
It analyzed light exposure.
It adjusted temperature, humidity, CO₂.
It decided on its own when to water, how much to water, and when to wait.

A tomato plant named Sol is now alive because an AI system exercised judgment.

That’s not a demo.
That’s a boundary crossing.

In medicine, we recognize this moment instantly.

This is the same transition we saw when clinical decision support moved from “suggesting” to “executing.”
When autopilot went from assisting pilots to flying planes.
When order sets stopped advising clinicians and started shaping outcomes.

Here’s the uncomfortable truth healthcare learned the hard way:

AI doesn’t eliminate risk.
It relocates it.

Once a system acts, responsibility doesn’t disappear it concentrates.

And that’s where healthcare becomes the blueprint for every other industry.

We already know what happens when autonomy arrives:
• You need escalation paths, not just accuracy metrics
• You need accountability models, not just performance charts
• You need governance before failure, not after harm

This is why healthcare AI will never be “move fast and break things.”

A broken interface is annoying.
A broken clinical action is irreversible.

What Claude did with a tomato plant is charming.
What it previews for healthcare is profound.

Because the next systems won’t be watering plants.

They’ll be:
• Adjusting insulin delivery
• Titrating ventilator settings
• Prioritizing patient outreach
• Managing chronic disease workflows at scale

And when that happens, the real question won’t be:
“Is the AI smart enough?”

It will be:
“Who is accountable when the system decides?”

Here’s the line I want leaders to sit with:

Autonomy isn’t about intelligence.
It’s about responsibility.

Or put more bluntly:

What seems autonomous often just hides the author.

Healthcare has already lived through AI’s future.
The rest of the world is just arriving.

So I’ll ask the question most organizations are avoiding:

When your AI acts without asking 
who signs the chart?

#DrGPT #AI #HealthcareAI #AutonomousSystems #PatientSafety #MedicalEthics

#POWER × #INTEGRITY Part 2 of 5
People don’t move on truth alone.

Power rarely announces itself as force.

More often, it shows up as incentives, fear, identity, and timing.

The next set of laws exposes a hard truth:
People don’t act because something is right.
They act because it aligns with self-interest or survival.

This matters deeply in healthcare and AI.

You can design the safest system imaginable 
and still fail if you ignore who feels threatened, sidelined, or exposed.

Ethical leadership isn’t naïve.
It’s perceptive.

Good intentions don’t override human nature.
Good design accounts for it.

This is Part 2 of a 5-part series.
If you missed Part 1, it’s on my profile. More coming tomorrow.

#Leadership #AIinHealthcare #StrategicThinking #ResponsibleAI #PowerAndIntegrity #DrGPT

We are incredibly excited to have Harvey Castro, MD, MBA. joining us as a speaker at the Digital Health & AI Innovation Summit (DHAI).

#DHAI2026 brings together leading voices from across Providers, Health Systems, Biotech,  MedTech, Pharma, Biomedical Science, and Venture Capital, alongside the Payers and Policy Makers who shape our industry.

REGISTER HERE: 🔗 https://lnkd.in/ebHYyH_z 

👉What to expect at #DHAI2026:

🍪 𝔹𝕖𝕪𝕠𝕟𝕕 𝕥𝕙𝕖 "ℂ𝕠𝕠𝕜𝕚𝕖-ℂ𝕦𝕥𝕥𝕖𝕣" ℂ𝕠𝕟𝕗𝕖𝕣𝕖𝕟𝕔𝕖: DHAI is built on fresh perspectives, provocative discussions, including Algorithm Malpractice, AI Agents as Doctors, and Human-AI Disagreements.

🚀ℙ𝕚𝕠𝕟𝕖𝕖𝕣𝕚𝕟𝕘 𝕋𝕖𝕔𝕙 𝕊𝕠𝕝𝕦𝕥𝕚𝕠𝕟𝕤: Experience live, high-stakes demos of emerging technologies before they hit the broader market.

🎤𝟙𝟝𝟘+ 𝕋𝕣𝕒𝕚𝕝𝕓𝕝𝕒𝕫𝕖𝕣 𝕊𝕡𝕖𝕒𝕜𝕖𝕣𝕤: A hand-picked, powerful lineup of the industry’s most provocative voices—experts who are not only deeply knowledgeable but also inspiring and engaging to watch on stage.

🏨𝔸 ℙ𝕣𝕖𝕞𝕚𝕖𝕣 ℕ𝕖𝕨 ℍ𝕠𝕞𝕖: We are moving to the Omni Boston Hotel at the Seaport. This isn't just a larger space; it’s a world-class, luxury setting in the heart of Boston’s most innovative district, specifically designed to foster high-level networking and deal-making.

🎖️𝔸 ℂ𝕠𝕞𝕡𝕖𝕥𝕚𝕥𝕚𝕧𝕖 𝔼𝕕𝕘𝕖: Gain the strategic insights and latest trends necessary to lead in Health AI and secure your competitive advantage.

🫶See you in Boston!

#FutureofHealth #DigitalHealth #AIinHealthcare #MedicalAI #HealthVentures #FutureOfHealth #GenerativeAI #ChatGPTHealthcare #EmergencyMedicine

#Burnout doesn’t come from failure.
It comes from misalignment.

I see it constantly.

People who look successful on paper
but feel depleted in real life.

Clinicians.
Leaders.
High performers who did “everything right” and still feel off.

That tension is why I built the Born to Win Framework.

Not as motivation.
But as a way to succeed without sacrificing your health or your humanity.

Here’s what most goal-setting systems miss:

You don’t burn out from working too hard.
You burn out from living out of balance.

What this framework is really about:

• Character is the frame
When values crack, everything eventually collapses.

• Balance is the engine
If health, family, or inner life is neglected, momentum disappears.

• Vision gives direction
Effort without clarity drains energy instead of creating progress.

• Goals connect action to meaning
Motion alone isn’t success. Purposeful motion is.

And this part took me years to learn:

Success isn’t something you achieve once.
It’s something you maintain.

Daily.
Through will, skill, and intentional refill.

That’s why service sits at the center of it all.

When your growth helps others grow,
success becomes sustainable.

If you paused for a moment and were honest.
which part of your life needs realignment right now?

#HealthcareLeadership  
#PhysicianWellbeing  
#PurposeDriven  
#HighTechHighHeart  
#LeadershipDevelopment

#POWER × #INTEGRITY: Part 5 of 5
Power isn’t the problem. Blindness is.

This week I shared a reflection on The 48 Laws of Power.

Not as tactics.
Not as manipulation.
But as forces already shaping leadership, healthcare, and AI.

Here’s the synthesis:

Power is inevitable.
Integrity is optional and that’s the risk.

Power without integrity becomes dangerous.
Integrity without power becomes ineffective.

The leaders we trust don’t deny power.
They discipline it.

They understand the system 
so the system doesn’t quietly control them.

Understanding power isn’t corruption.
It’s responsibility.

If you found this series useful, share it with your team.
I’ll be building next on AI autonomy, governance, and trust at scale.

#Leadership #AIinHealthcare #ResponsibleAI #Governance #PowerAndIntegrity #DrGPT

One leadership lesson I’m carrying forward as DrGPT:

Your story isn’t a distraction from strategy.
It is the strategy.

I’ve been reflecting on insights from @tazhart on YouTube, and this one landed hard—especially for leaders in medicine, AI, and high-stakes systems.

Here’s the truth many leaders miss:

• The story you hesitate to share is often the one that creates trust
• Data informs, but story mobilizes
• Without connection, leadership falls flat—no matter how smart the plan

There’s real science behind this.
Character-driven stories trigger oxytocin (trust), dopamine (focus), and endorphins (connection).
Storytelling doesn’t just persuade—it changes biology.

What stood out most:

Storytelling doesn’t need to be long.
It just needs to be yours.

A simple framework I’m keeping:
• Set the scene
• Share the shift
• Offer the spark

In healthcare and AI, we talk endlessly about strategy, models, and systems.
But people don’t follow frameworks.
They follow humans.

Your story is the one leadership asset no one else can replicate.
And in the next era of leadership, that may matter more than ever.

Credit: @tazhart (YouTube)

#DrGPT #Leadership #Storytelling #AuthenticLeadership #AILeadership #HealthcareLeadership #HumanCenteredAI #ExecutivePresence #Trust #Communication #FutureOfWork

#POWER × #INTEGRITY: Part 3 of 5
How you show up is part of the system.

Power isn’t only external.

It’s expressed through identity, restraint, consistency, and emotional control.

These laws remind us that #credibility is not declared it’s #perceived.

In healthcare and emerging technology:
overconfidence erodes trust,
performative certainty raises suspicion,
loud authority ages poorly.

True leadership feels steady, not frantic.

Authority doesn’t rush.
It reassures.

Integrity shows up not in perfection,
but in how calmly someone carries responsibility.

This is Part 3 of a 5-part series.

Follow for Part 4 tomorrow and share with someone leading under pressure.

#Leadership #Trust #HealthcareLeadership #ResponsibleAI #PowerAndIntegrity #DrGPT

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.

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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Ghadeer  A.

Document-to-Decision Architect | Building Enterprise AI Systems that Turn Documents into Trusted Decisions

Neural_Networks_Guide

🚨 BREAKING: I wasted $2,000 learning AI the wrong way.
Turns out NVIDIA has a free beginner path. WITH certificates.

You don’t need a CS degree.
You don’t need a GPU.
You don’t need paid courses.

You just need the right order 👇

STEP 1 — Understand GenAI (no coding)
Generative AI Explained
What LLMs are, how GenAI works, where it’s used.
🔗 https://lnkd.in/dCy_GBct

STEP 2 — Build your first real AI system
Augment Your LLM Using RAG
Learn how AI reads PDFs, docs & private data.
🔗 https://lnkd.in/d6JebWSx

STEP 3 — Learn how AI “thinks”
Building a Brain in 10 Minutes
Neural networks explained visually. No math overload.
🔗 https://lnkd.in/dhuRZ3XP

STEP 4 — (Optional) Understand why GPUs matter
An Even Easier Introduction to CUDA
Why NVIDIA GPUs unlocked modern AI.
🔗 https://lnkd.in/dX4k6fdi

STEP 5 — Make it career-ready
• Finish labs
• Add certificate to LinkedIn
• Build 1 tiny demo (PDF → AI answers)

💡 Beginner rule:
Don’t binge courses.
Finish one → build one → share one.

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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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🚨BREAKING: Anthropic just released ALL the Claude Code secrets

Claude Code feels powerful, but unpredictable.

Hallucinations, wasted tokens, messy edits.

Anthropic just revealed how to fix all of it.

Here are the 7 secrets that make Claude Code elite 👇:

1️⃣ Use GitHub immediately

Connect Claude Code to Git early.
It reads your full commit history.
Hallucinations drop dramatically.

2️⃣ Load as much context as possible

Context handling was massively upgraded.
Claude now compacts context intelligently.
You can keep projects alive for a long time.

3️⃣ Control eagerness

Claude Opus 4.5 is extremely eager.
It acts first and apologizes later.
Guide it clearly to avoid chaos.

4️⃣ Avoid the word “think”

“Think” triggers extra token usage.
So do “think more” and “ultrathink.”
Use “evaluate” or “consider” instead.

5️⃣ Exploit vision capabilities

Opus 4.5 dominates visual reasoning.
It understands images better than any model.
Paste images directly for debugging or ideas.

6️⃣ Run tools in parallel

Claude is the best tool-using model.
Anthropic added a new parallel rule.
Tools now run faster and smarter together.

7️⃣ Actively reduce hallucinations

Eagerness causes premature file edits.
Claude may act without full understanding.
Force research before execution.

These practices change everything.
Claude becomes calmer, cheaper, smarter.
And dramatically more reliable.

Link to full prompt guide: https://lnkd.in/d8rqisP2

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AI doesn’t understand anything. Here’s exactly what it does instead 👇

Most people think LLMs “understand” language.
They don’t.

They predict the next token INSANELY well.

This video makes it click.

You’re watching text get:
→ chopped into pieces
→ turned into math
→ weighed against everything else
→ refined layer by layer
→ sampled into words that feel intelligent

No thoughts.
No memory of meaning.
Just probability + attention at scale.

Once you see this, two things change:
1️⃣ You stop mystifying AI
2️⃣ You start engineering it

If you’re building with LLMs and this isn’t crystal clear yet you’re flying blind.

Watch this slowly.
Pause it.
Rewatch it.

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🚨I spent the last 2 weeks reading 50+ Claude Code articles, X threads, and “guides.” (SAVE THIS 👇)

Most of them were AI slop.
Recycled tips. Vague workflows. Zero leverage.

But the top 1% completely changed how I build with Claude Code.

So I compiled the only Claude Code guide you actually need to 10× your workflows.

Your Claude Code workflows are about to get 10× better

This is a curated crash course for builders who want:
• Faster iteration
• Cleaner vibe-coded workflows
• Less steering, more output

If you use Claude Code seriously, bookmark this.

How to use this guide (important)

If you’re new → spend more time in Getting Started
If you’re advanced → jump to Pro Tips & Resources

Pro move:
Create a new Claude project and paste these resources into it.
Treat Claude as a tutor + co-builder, not a chatbot.

1️⃣ Getting Started

Start here if you’re new:

• Claude Code in <15 Minutes
Setup Claude Desktop, Web, CLI, editor tools, terminal, extensions
👉 https://lnkd.in/dY7wGJFh

• Elite Claude Code Setup (Boris Cherny) 
This alone fixes 80% of bad CC usage

• Free Claude Code Basics Course (3+ hours)
Structured, no fluff
👉 https://lnkd.in/dUCxC4YP

2️⃣ Pro Tips / Best Practices (must-read)

• Official Claude Code agentic coding best practices
👉 https://lnkd.in/d__wJwMc

• Hidden power tips (criminally underrated)
👉 https://lnkd.in/dWG_xFvR

These two alone will level up your planning, context, and tool usage.

3️⃣ Builder Tools (for real leverage)

Once you have the basics down, this is where speed comes from.

I’ve collected:
• The single best Claude Code prompt
• 12 best Claude Code prompts I use daily

This should honestly be paid.

But I’m giving it away.

👇
Comment “PROMPTS” and I’ll send you the full list.

Thank me later.

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The sexiest job of 2026 isn’t AI Engineer. It’s Forward Deployed Engineer.

Here’s the real reason why:

Saw this from Eduardo Ordax and it made me pause.

Not because it’s clever.
Because it’s accurate.

We keep talking about smarter models.
But the real bottleneck isn’t intelligence anymore.

It’s translation.

Most companies don’t fail at AI because they lack talent.
They fail because no one owns the space between what leadership wants and what systems can actually do.

That gap is where Forward Deployed Engineers live.

They’re not there to invent.
They’re there to confront reality.

To turn:
– ambition into constraints
– demos into workflows
– “AI strategy” into something that survives production

Once you notice it, you see this role everywhere.

Not as a title.
As a pattern.

In 2026, the most valuable people in AI won’t be the loudest or the most technical.

They’ll be the ones who can walk into confusion…
…and quietly make things work.


Bookmark this 🔥. You’ll see this job title everywhere this year.

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🚨 BREAKING: 200k tokens doesn’t mean you have context.

It means you have a budget.

Claude didn’t suddenly get worse.
Your outputs didn’t magically decay.

Your context did.

Context = everything the model sees when it responds:
• your prompt
• prior messages
• system instructions
• tools & agents
• metadata

Most people treat this like free real estate.

It’s not.

Hard truth:
More context ≠ better output.
Past a point, model performance drops.

That’s where “AI slop” comes from.

Not bad models.
Bloated context.

What actually works 👇

• One clear objective per conversation
• Plan before execution (force the model to ask questions)
• Eliminate ambiguity early
• Reset bad threads instead of “fixing” them
• Pull docs/tools only when needed
• Offload heavy research to sub-agents
• Optimize for signal, not clever setups

If a token doesn’t help answer the next question,
it’s already hurting you.

Context hygiene > prompt tricks.

👇 Comment “CONTEXT” and I’ll share the full 7-step context engineering playbook.

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The Battle

Harvey Castro, MD, MBA.

Ghadeer A.

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Posts

Engagements

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Harvey Castro, MD, MBA.'s LinkedIn Performance

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