Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agent vs Agentic AI

Most people use the terms AI Agent and Agentic AI like they mean the same thing.

They don’t.

The difference isn’t just semantic.
It’s architectural.

Here’s how the tech stack evolves from AI Agent → Agentic AI 👇

1. Intelligence models
- AI Agent typically relies on a single LLM with prompt → response workflows.
- Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems.

2. Architecture & frameworks
- AI Agent often follows a single-agent, linear execution flow.
- Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen.

3. Memory systems
- AI Agent works with session memory, short-term embeddings, and basic caches.
- Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases.

4. Tool usage & actions
- AI Agent uses predefined tools and function calling triggered by users.
- Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries.

5. Knowledge & retrieval
- AI Agent typically uses basic RAG pipelines with static retrieval.
- Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs.

6. Orchestration & workflows
- AI Agent runs sequential flows and simple backend automation.
- Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles.

7. Decision making
- AI Agent is reactive and prompt-driven.
- Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops.

8. Deployment
- AI Agent is often deployed as chatbots, copilots, or API-based assistants.
- Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems.

9. Monitoring & observability
- Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops.

10. Learning & improvement
- AI Agent improves through prompt iteration and occasional fine-tuning.
- Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks.

AI Agent = intelligent responder.
Agentic AI = autonomous system with goals, memory, tools, and orchestration.

One answers questions.
The other executes objectives.

Are you building smarter responses or autonomous systems?

Anthropic made Learning AI easier.

They’ve released a complete set of FREE courses designed to help you understand Claude, AI agents, APIs, and the Model Context Protocol (MCP) - the foundation behind modern AI applications and agent systems.

This learning path covers everything from beginner concepts to advanced agent development workflows.

Here’s what you can learn:

• Claude 101 - Understand core features and everyday AI workflows
• Claude Code in Action - Use Claude to accelerate development tasks
• AI Fluency: Framework & Foundations - Learn how to work effectively with AI
• Building with the Claude API - Develop real AI-powered applications
• Introduction to Model Context Protocol (MCP) - Connect AI with tools and data
• MCP Advanced Topics - Deep dive into advanced implementations
• Claude with Amazon Bedrock - Deploy Claude in AWS environments
• Claude with Google Cloud Vertex AI - Production AI workflows on GCP
• Introduction to Agent Skills - Build reusable AI agent capabilities

These courses are especially useful if you want to learn:
AI Agents • MCP • LLM Applications • AI Automation • Developer Workflows

No fluff.
No paywall.
Just practical AI education directly from the company building Claude.

Get all the courses here:
https://lnkd.in/gAmrrsBK

If you’re serious about understanding how modern AI systems actually work, this is one of the best free starting points available right now.

Save this for later - you’ll want to come back to it.

Claude vs ChatGPT ?

It’s not a rivalry, It's a toolkit decision.
Both are powerful. Both have blind spots.

The smartest operators aren't loyal to one - they know exactly when to use which.

Here's the breakdown : 

Long Documents & Research
Claude → Analyzing contracts, reviewing large codebases, multi-document reasoning, structured research synthesis.

ChatGPT → Summarizing quickly, extracting highlights, generating reports, visualizing insights.
Need depth? Claude. Need speed? ChatGPT.

Coding & Development
Claude → Architecture reviews, refactoring complex logic, explaining systems deeply, debugging reasoning-heavy code.

ChatGPT → Rapid prototyping, boilerplate generation, built-in Python and browser tools, building agents with APIs.
Need to understand the code? Claude. Need to ship fast? ChatGPT.

AI Agents & Automation
Claude → Safety-critical tasks, high-stakes reasoning, enterprise deployments, controlled tool execution.

ChatGPT → Production agents, function calling, quick API integrations, leveraging ecosystem tools.
Need control and safety? Claude. Need speed and ecosystem? ChatGPT.

Business & Strategy
Claude → Policy documents, governance frameworks, risk analysis, strategic breakdowns.

ChatGPT → Market research synthesis, presentation drafts, data analysis with tools, growth strategy ideation.
Need structured thinking? Claude. Need fast outputs? ChatGPT.

Creativity & Content
Claude → Deep essays, thought leadership writing, analytical storytelling, long-form structured content.

ChatGPT → Brainstorming, social media content, image generation, multimodal creative tasks.
Need depth and voice? Claude. Need volume and variety? ChatGPT.

The Quick Decision Rule:
Choose Claude when:
- Depth beats speed.
- Structure beats creativity.
- Safety beats flexibility.
- Context window matters.

Choose ChatGPT when:
- Tools matter.
- Multimodal work is required.
- Fast iteration is the priority.
- Broad ecosystem integration is needed.

The final summary nobody argues with:
Claude = Deep thinking. Structured reasoning. Enterprise safety.
ChatGPT = Versatile. Tool-rich. Multimodal productivity engine.

Stop asking which AI is better.
Start asking which AI is better for this specific task.
That shift alone will change how much you get done.

Google just quietly released an AI tool for every layer of how humans create things.

Research. Design. Code. Video. Agents. Models.

All of it. At once.
Connected. Intentional. Ecosystem-scale. 

The tools nobody is talking about enough:

Jules - an asynchronous coding assistant that works across entire repositories, not just individual files. It understands the broader structure of a project and helps manage complex codebases.

Google A2A - a multi-agent communication protocol designed to let AI agents collaborate across different frameworks. This is infrastructure for the next generation of agent systems.

Stitch - converts prompts directly into complex UI layouts, turning ideas into interface designs within minutes. A major shift for how design workflows could work.

FileSearch API - a configurable RAG pipeline accessible through a single API call. What previously required weeks or months of engineering can now be integrated almost instantly.

Flow - a cinematic storytelling platform that lets creators generate scenes, narratives, and video content with AI-driven workflows.

Gemini 3 Pro - Google’s most advanced reasoning model so far, designed for deeper analysis, complex tasks, and next-generation AI systems.

This isn't a product launch.

It’s a full-stack AI ecosystem.

From foundational models → to research tools → to design → to development → to autonomous agents → to video creation.

Everything sits on top of the same AI foundation.

That means ideas can move from research → design → code → content faster than ever before.

The real strategy here isn’t individual tools.
It’s ownership of the entire creation pipeline.

And when one company controls every layer of that pipeline, the innovation speed compounds.

The AI race isn’t just about better models anymore.

It’s about who builds the ecosystem developers, creators, and companies build on.

People still use AI like a chatbot.
The real power starts when it becomes your execution layer.

Claude Code isn’t just another AI tool.
It acts like an operating system for building, testing, and shipping real work.

Instead of writing prompts and copying outputs, you can actually run workflows, analyze codebases, connect tools, and automate tasks from one place.

That’s the shift most teams haven’t fully understood yet.

Traditional AI tools help you think faster.
Claude Code helps you execute faster.

It moves through a structured loop:
analyze → plan → build → automate → scale.

You can connect repositories, files, databases, and internal tools.
Run commands.
Generate code.
Debug systems.
Automate repetitive work.
And build reusable skills that keep improving over time.

It also works across environments, which means you’re not jumping between ten different tools to get one workflow done.

The biggest difference?
It behaves less like a chatbot and more like a technical teammate that can actually take action.

When used properly, it becomes a working layer on top of your stack — not just a place to ask questions.

Most people experiment with AI.
Few build repeatable systems with it.

That’s where the real leverage is.

AI becomes valuable when it moves from answering questions to executing workflows.

Start treating it like an operating system for work, not just a prompt tool.

The agent gave a brilliant answer.
Then someone asked, “Where did that data come from?”
Silence. That’s when you realize intelligence needs infrastructure.

Agentic AI isn’t just reasoning.
It’s retrieval, validation, lineage, governance, and monitoring - all working quietly in the background.

Here’s what that foundation looks like:

- Data Ingestion
Pulls in structured and unstructured sources, standardizes formats, and attaches context before anything reaches an agent.

- ETL / ELT Pipelines
Transforms, validates, and loads data into usable systems so downstream workflows aren’t built on chaos.

- Data Versioning
Tracks changes, creates reproducible snapshots, and enables rollback when something breaks or drifts.

- Vector Pipelines
Chunks content, generates embeddings, and indexes it for semantic retrieval in RAG and agent workflows.

- Metadata Management
Registers datasets, records ownership, and enables search and observability across the ecosystem.

- Data Governance
Enforces policies, access controls, and compliance rules to prevent misuse and ensure accountability.

- Data Warehouse / Lakehouse
Stores refined datasets that power analytics, feature engineering, and intelligent querying.

- Streaming Data
Processes real-time events so agents can react, trigger actions, and operate dynamically.

- Data Lineage
Maps where data originated, how it transformed, and who depends on it.

- Data Quality & Reliability
Applies validation checks, detects anomalies, and keeps pipelines healthy under pressure.

At the center is the agent itself -
retrieving context, reasoning over data, planning actions, learning from feedback.

But without the layers beneath it,
even the smartest answer becomes a liability.

Intelligence is impressive.
Traceable, governed intelligence is enterprise-ready.

If someone asks where your agent’s answer came from, can you show them?

If you learned AI even three years ago, you’re already studying history.

The conversation has moved far beyond models and prompts. What we’re witnessing now isn’t just incremental improvement, it’s a fundamental shift in how intelligence is built, deployed, and experienced inside real systems.

AI didn’t suddenly appear with ChatGPT.
It evolved through decades of breakthroughs, each solving the limitations of the era before it - gradually transforming software from rule execution into adaptive intelligence.

Understanding that evolution is what reveals where AI is actually heading next.

From Rules → Learning
AI moved from hard-coded logic toward systems capable of learning patterns directly from data instead of predefined instructions.

From Learning → Representation
Deep learning and transformers introduced contextual understanding, enabling language comprehension, perception, and multimodal intelligence at scale.

From Models → Platforms
Foundation models and Retrieval-Augmented Generation turned AI into reusable platforms connected to dynamic, real-world knowledge sources.

From Assistants → Agents
AI progressed from answering questions to planning actions, reasoning through problems, and executing complex multi-step workflows autonomously.

Where AI Is Heading (2026+)
Intelligence is moving toward edge computing, embodied AI, persistent memory, and systems embedded seamlessly into everyday environments.

Every generation of AI didn’t replace the previous one, it stacked on top of it.

Many organizations struggle today because they attempt to adopt the newest layer without understanding the foundations underneath. Real advantage comes from recognizing how these layers connect:

Rules → Learning → Reasoning → Agents → Autonomous Systems

We’re not reaching the end of AI evolution.

We’re entering the phase where intelligence stops being a feature and becomes a system layer woven into everything we build.

#AI

Anthropic’s latest report highlights a shift many enterprises still underestimate:

AI agents have moved from experimentation into core operations and adoption is happening faster and wider than expected.

The report tracks deployments across 15+ business domains. What stands out isn’t just scale, it’s how deeply agents are embedded into real workflows across the enterprise.

This isn’t just a tech trend. It’s an operational shift.
The companies pulling ahead aren’t spending more - they’re deploying smarter, where agents remove friction and accelerate outcomes.

Here’s where enterprises are actually putting agents to work:

• Software Engineering - Writing, reviewing, testing, and debugging code while accelerating delivery cycles.
• Back-Office Automation - Eliminating manual workflows like data entry, processing, and internal operations.
• Marketing & Copywriting - Scaling content creation, personalization, and campaign execution end-to-end.
• Sales & CRM - Automating prospecting, follow-ups, and CRM workflows so teams focus on closing.
• Finance & Accounting - Reconciliation, anomaly detection, reporting, and audit-ready operations.
• Data Analysis & BI - Turning raw data into insights, dashboards, and narratives instantly.
• Customer Service - Resolving tickets autonomously while escalating only complex edge cases.
• Academic Research - Synthesizing literature, accelerating reviews, and mapping knowledge faster.
• Cybersecurity - Monitoring threats continuously and triggering real-time automated responses.
• Document & Presentation Creation - Structuring reports, slides, and outputs directly from inputs.
• Education & Tutoring - Personalized learning, real-time assistance, and adaptive assessments.
• Medicine & Healthcare - Clinical documentation, workflows, and patient communication support.
• Legal - Contract review, due diligence acceleration, and compliance workflows at scale.
• E-commerce Operations - Managing listings, pricing, inventory signals, and customer support.
• Gaming & Interactive Media - Driving adaptive narratives and real-time interactive experiences.
• Travel & Logistics - Route optimization, bookings automation, and supply chain coordination.

The pattern is clear:

Agents aren’t a single-use tool - they’re becoming infrastructure across every function and industry.

The question isn’t whether your business will use agents. It’s whether you’ll move fast enough to stay competitive.

Which domain are you building in?

If you think an AI agent is just an LLM connected to tools, you’re only seeing the surface.

What actually powers a production-grade AI agent is a layered intelligence architecture - where memory, reasoning, governance, and infrastructure work together to transform responses into reliable autonomous behavior.

Modern AI systems are no longer single models.
They are orchestrated systems designed to think, plan, adapt, and operate safely at scale.

This visual breaks down the internal anatomy of a real-world AI agent across three evolving layers:

From Execution → Coordination
The Basic Layer handles core operations — processing inputs, orchestrating prompts, invoking models, executing tools, managing errors, and delivering structured responses.

From Coordination → Reasoning
The Intermediate Layer introduces structured intelligence. Agents decompose tasks, plan execution strategies, validate outputs, manage context, monitor costs, and maintain observability for reliable performance.

From Reasoning → Autonomy
The Advanced Layer enables true production intelligence through long-term memory, multi-agent collaboration, governance guardrails, adaptive learning, self-reflection, and scalable deployment infrastructure.

Each layer doesn’t replace the previous one — it builds on top of it.

Organizations struggle with AI adoption not because models are weak, but because architecture is incomplete. Real AI capability emerges only when these layers operate as a connected system:

Execution → Reasoning → Governance → Autonomy

We’re moving toward a future where AI isn’t an application feature.

It becomes an operational layer embedded into every digital system we build.

Gemini just launched Embedding 2 !!!

And this quietly changes how we build AI systems.

This isn’t just another model update.
It’s Google’s first natively multimodal embedding model - meaning text, images, video, audio, and documents are all mapped into a single shared understanding space.

And that unlocks a completely different level of intelligence.

🔹 What makes Gemini Embedding 2 different?
Instead of handling data separately, it understands relationships across modalities.
You can now input text + image + audio in a single request and the model captures the full context.

This means:
• Search using an image → retrieve relevant videos
• Query with text → find exact moments inside audio
• Combine inputs → get deeper semantic meaning

🔹 Key capabilities you should know
• Text: Up to 8192 tokens context
• Images: Up to 6 images per request
• Video: Up to 120 seconds supported
• Audio: Direct embedding (no transcription needed)
• Documents: PDFs up to 6 pages

Plus, it supports 100+ languages, making it globally usable out of the box.

🔹 Why this matters for AI builders
This model simplifies what used to be complex pipelines:

Before:
Text embeddings + image models + speech-to-text + vector search stitching

Now:
One model → one embedding space → unified retrieval

That’s a massive reduction in system complexity.

🔹 Where this will be used (immediately)
• RAG systems with multimodal context
• Semantic search across media libraries
• Recommendation systems using mixed data
• Content clustering and classification
• Enterprise knowledge retrieval (docs + media together)

🔹 Performance leap
Early benchmarks show strong improvements across text, image, and video tasks.
Real-world use cases are already hitting 85%+ recall in text-to-video retrieval.

That’s not incremental, that’s a shift in capability.

🔹 Technical edge (important)
Gemini Embedding 2 uses Matryoshka Representation Learning (MRL)
→ You can scale embedding dimensions (3072 → 1536 → 768)
→ Balance between performance and storage cost

This is huge for production systems.

🔹 The bigger picture
We’re moving from:
Single-modality AI → Multimodal intelligence layers

From:
“Search data” → “Understand data across formats”

Embedding models are no longer just backend tools -
they’re becoming the foundation layer of AI systems.

If you’re building anything in:
AI Agents, RAG pipelines, enterprise search, or automation systems

This is something you should be paying attention to.

Because the next generation of AI won’t just read text - it will understand everything.

81k | Director, Data & Analytics/ AI @ Gartner

Summary

Making state-of-the-art Artificial Intelligence concepts accessible and profitable to professionals from any background for over 35 years!

Highlights
-  Veteran innovator with 3 decades of experience leading state-of-the-art AI and Analytics ventures
-  98% of students surveyed would recommend me to peers
-  Generative AI/ Agentic AI expert-  ChatGPT, Claude, Copilot, Perplexity, Lovable, n8n, more
-  Experienced AI instructor for Cisco, LinkedIn Learning, University of Pennsylvania, & Simplilearn
-  Senior AI Curriculum Advisor for Graduate School of Education at Ivy League university UPenn
-  Winner, International Society of Data Scientists Outstanding Voice Award
-  Social media leader with over 107k LinkedIn followers


Selected Clients
 
-  3M
-  Audi
-  Biogen 
-  Booz Allen Hamilton
-  Bristol-Myers Squibb
-  Cisco
-  Citicorp
-  Franklin Templeton
-  Gartner/ Evanta
-  Harvard Innovation Labs’ Experfy
-  IBM
-  International Olympic Committee
-  Los Angeles Times
-  Mercedes
-  Morgan Stanley
-  Royal Bank of Canada
-  St. Jude’s Children’s Hospital
-  Wells Fargo
-  US Homeland Security
-  US Veterans Administration 
-  Yale University

Partial Skills Inventory
 
-  AIOps
-  Accountable AI/ Ethics
-  Anomaly Detection
-  Anthropic/ Claude
-  Artificial Intelligence
-  AWS
-  Brokerage Firms
-  Client Management
-  Consulting
-  Compelling Presentations
-  Compliance Regs and Frameworks
-  Curriculum Development
-  Dashboards
-  Data Storytelling
-  Data Visualization
-  Education
-  Explainable AI
-  Feature Engineering
-  Financial Services
-  Fraud Detection
-  Gap Analysis
-  Generative AI
-  GitHub Models
-  Google Cloud
-  Google Gemini
-  Insurance
-  KPI's and Metrics
-  Lovable AI
-  Machine Learning
-  Microsoft Azure
-  MLOps
-  Model Ensembles
-  Nano Banana
-  OpenAI/ ChatGPT
-  Orange Data Mining
-  Perplexity
-  Postgres SQL
-  Professional Development
-  Research
-  Retail
-  Retention and Attrition
-  SPSS
-  SQL Server
-  Underwriting

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗛𝗶𝗿𝗶𝗻𝗴- 𝗝𝘂𝘀𝘁 𝗪𝗵𝗮𝘁 𝗔𝗥𝗘 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿?

The Terrible Facts:

- Artificial Intelligence needs a lot more experts.
- Tons of qualified professionals aren't getting hired.
- The expectations of hiring managers are just out of control.

If you're a hiring manager, please STOP looking for an entire department in just one employee.  It simply doesn't work that way.  You need a team.

Personally, I find the job search to be truly dreadful. It is exhausting, soul-crushing, depressing, dispiriting- actually, I will run out time before I run of negative emotions on this topic!  

But I hold out hope that one day everyone else will figure this out!

Please follow me for more analysis! 

- T. Scott Clendaniel

🥳 || #AI Training for 3.1 Million Worldwide || 111K Followers || Follow me for genuine ROI from AI!!

🔬 #AI for the Environment

The Ocean Cleanup provides remarkable technology in the form of the Interceptor 006, which I discovered courtesy of Pascal BORNET!

▶▶ Interceptor 006, the new Interceptor Barricade, shows early promise following deployment in Rio Las Vacas, Guatemala.

▶▶ Estimated 856,973 kg of trash removed and prevented from polluting the Caribbean Sea during installation and first trials; still awaiting anticipated ‘trash tsunami’ conditions to fully validate solution.

▶▶ Strong initial performance provides hope of ending annual trash floods into the Gulf of Honduras each year.

▶▶ Contributes to restoration and improvement of environment throughout Guatemala and to The Ocean Cleanup’s global mission of ridding the oceans of plastic.

Enjoy,

T. Scott Clendaniel

🥳 || #AI Training for 3.1 Million Worldwide || 109K Followers || Follow me for genuine ROI from Artificial Intelligence!

🔬 #Learning #AI:  𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲!

PLEASE 𝗦𝗔𝗩𝗘 𝗮𝗻𝗱 𝗦𝗛𝗔𝗥𝗘 this so your team can take advantage of it!

Claude Code has absolutely EXPLODED and taken the lead by a country mile.

So please don't let poor project structure defeat you!  Fortunately, we have the amazing Brij kishore Pandey, the king of project structures, to show a better approach.


🧭

🥳 || #AI Training for 3.1 Million Worldwide || 110K Followers || Follow me for genuine ROI from Artificial Intelligence and LinkedIn!

#AI #ArtificialIntelligece: Claude Code Project Structure!

Sharing some PURE #Wisdom!  🥳






(Go follow Carmen Morin- she is constantly sharing gems 💎 like these!)

#1 LinkedIn Education Creator 🇨🇦 | Performance-Based Learning Strategist & Keynote Speaker | Concert Pianist Turned 7-Figure Education Founder

It's not about the money you offer. 

It's about the environment you create.

Most "retention strategies" completely miss the point.

Leaders obsess over compensation packages.

While their people dread Monday mornings, toxic meetings, and feeling invisible. 

A raise loses its shine in weeks.

But culture impacts you from the moment you wake up.

❌ Toxic cultures drain talent regardless of salary because they:

↳ Create stress that money can't compensate for 
↳ Limit growth potential and career advancement 
↳ Foster competition over collaboration 
↳ Ignore individual strengths and development needs 
↳ Prioritize output over human well-being

✅ Healthy cultures outperform pay raises because they:

↳ Build psychological safety where people can be authentic 
↳ Foster continuous learning and growth opportunities 
↳ Create meaningful connections between team members 
↳ Recognize contributions beyond just compensation 
↳ Align individual purpose with organizational mission 
↳ Support work-life integration, not just balance 
↳ Encourage experimentation without fear of failure

You can't buy your way out of toxic culture. 

You have to build your way out.

The best talent chooses environments, 
not just paychecks.

♻ Repost to champion culture-first leadership. 
🔔 Follow Carmen Morin for more on human-centered systems.

🔬 #AI: The Problem With All Those Plans to Replace Senior Executives

𝗡𝗲𝘄 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝘃𝘀. 𝗮 𝗠𝗮𝘀𝘁𝗲𝗿 by Ravi Samrat Mishra 

So you have AI.  You're going to wipe out BOTH expensive existing talent AND new hires. You think you can just bring in agents to replace all that experience.

You might want to think again.  

Check out the video.  The younger employee is doing a heck of a lot of work.  In fact, he creates the crack that the "master" later exploits.

AI didn't swing a single hammer.  

YOU NEED to be onboarding the next generation!


𝗧𝗼𝗽 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗡𝗲𝘄 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀
-------------------------------------------

𝗦𝘁𝗮𝗿𝘁 𝗲𝗮𝗿𝗹𝘆: Begin training new hires on their first day so they are eager to learn and don't get lost to another organization. 
 
𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗹𝗮𝗻: A plan with clear goals helps employees understand expectations and gives managers a consistent way to train new hires. 
 
𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Make training materials easily accessible and prepare onboarding resources. 
 
𝗨𝘀𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆: Create courses, test learning, and track progress using technology. 
 
𝗔𝘀𝘀𝗶𝗴𝗻 𝗮 𝗺𝗲𝗻𝘁𝗼𝗿 𝗼𝗿 𝗯𝘂𝗱𝗱𝘆: A mentor can help new employees feel valued and learn from someone already in the organization. 
 
𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗿𝗲𝗴𝘂𝗹𝗮𝗿 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Feedback helps employees develop their skills and recognize their potential. 
 
𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘃𝗮𝗹𝘂𝗲𝘀: Ensure new hires understand the company's vision, goals, and values. 
 
𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘀𝗵𝗮𝗱𝗼𝘄𝗶𝗻𝗴: Allow new employees to shadow more experienced employees. 
 
𝗨𝘀𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Onboarding automation can streamline and modernize the onboarding process. 

Credit: https://bit.ly/3O3cWn6

- T. Scott Clendaniel, AI Educator

LinkedIn Top Voice | Personal Branding Strategist & Executive Mentor | 81M+ Views | Helping Leaders & Founders Build Authority that Converts.

Hardest Part of Parenting?

Raising a human is the hardest work we will ever do. It is much more than just keeping them safe or making sure they have what they need. Our children are like little mirrors that reflect every single thing we do back at us. They do not just listen to our words, but they watch how we move through the world, even when we think they are not looking.

They see how we treat a stranger on the street and how we react when we are tired or frustrated. Our children mimic our morals and our behaviours every single moment of the day. They are learning from us even when we are not trying to teach them anything at all. It is a heavy weight to realise we are their primary map for life.

Every choice we make today becomes the voice inside their heads tomorrow. It makes me stop and look at my own actions with a lot of honesty. Am I being the kind of person I want them to grow up to be? We are not just raising kids, but we are shaping the future of how people treat one another.

How would you like them to be when they grow up?

#Parenting #Character #Legacy #Mindfulness #Inspiration

Children & Teen Personal Development Coach | I help students build self-belief/confidence through my Lead With Love program, empowering parents & teachers with leadership & emotional intelligence, grounded in Taekwondo.

View Ligia Chacón Hernández’s profile

Don’t Wait Too Long to Love ❤️

A little girl ran through the animal shelter, excited to find a dog.🐶

Kennel after kennel… barking, jumping, tails wagging.

Until she stopped.

In the corner sat an old brown dog with gray around his eyes. Quiet. Still.

His tag read: Milo — 10 years old.🐶

While the others barked for attention, Milo simply walked over and rested his head against the bars when she approached.

“I want him,” she said.

Her parents gently explained that Milo was very sick.
He might not have much time left.

She didn’t understand time.

She only understood that he had been waiting.

They promised to think about it and come back.

But the next morning, when they called the shelter…

Milo had passed away during the night.

The volunteer said he was found lying by the kennel door.

As if he was still waiting.

Sometimes the ones who wait the longest
are the ones we overlook the fastest.

Adopt the older dog.
Choose the one no one else sees.

Because love doesn’t need years.

Sometimes it just needs a chance. ❤️


Follow #Motivation #leadership #India #leadershiplove  #creativity #WhatInspiresme #education

#Advertisting #GenAI #Marketing 🎉👏🎉
⬇️⬇️⬇️  Most Stunning 3D Billboards Around the World!  ⬇️⬇️⬇️



𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀:

 ▶▶ Audi
 ▶▶ BMW
 ▶▶ Coca-Cola
 ▶▶ Google 
 ▶▶ Louis Vuitton 
 ▶▶ Mercedes
 ▶▶ Netflix
 ▶▶ Nike
 ▶▶ NINTENDO WORLD™
 ▶▶ Spider-Man
 ▶▶ Super Mario Bros 
 ▶▶ VERSACE
 ▶▶ WhatsAPP


T. Scott Clendaniel

🔬#Learning #AI: Ten Commandments of AI Agents

How I wish I thought of these myself!  But I'm happy to share them.


The Commandments

➡️  Don't use agents just to "show off"
➡️  Agents need 2 characteristics: small, specialized, and decoupled
➡️  Enforce structured output
➡️  Explain the "why"
➡️  Orchestration is more important than autonomy 

➡️  Prompt Engineering Beats Fine-Tuning 
➡️  Tool 🔧 descriptions are VITAL
➡️  Cache like you mean it
➡️  Leverage shared artifacts
➡️  Log everything (seriously!)


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-T. Scott Clendaniel

𝟏𝟎 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐄𝐯𝐞𝐫𝐲 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐅𝐨𝐥𝐥𝐨𝐰 𝐖𝐡𝐞𝐧 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
Many teams rush into using AI agents without a clear strategy, often ending up with bloated, unreliable systems. Building powerful, production-grade AI agents requires discipline, structure, and thoughtful design choices. Here are 10 essential principles that separate hobby projects from scalable solutions:

𝟏. 𝐃𝐨𝐧’𝐭 𝐔𝐬𝐞 𝐀𝐠𝐞𝐧𝐭𝐬 𝐉𝐮𝐬𝐭 𝐭𝐨 𝐒𝐚𝐲 𝐘𝐨𝐮 𝐃𝐢𝐝
Use agents only where they truly add value. If a simple tool integration works, don’t overcomplicate it with orchestration.

𝟐. 𝐒𝐦𝐚𝐥𝐥, 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝, 𝐚𝐧𝐝 𝐃𝐞𝐜𝐨𝐮𝐩𝐥𝐞𝐝
Break big workflows into smaller specialized agents: Support, Manager, Verifier. Each should have a clear role.

𝟑. 𝐄𝐧𝐟𝐨𝐫𝐜𝐞 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐎𝐮𝐭𝐩𝐮𝐭
Always define output formats. This reduces ambiguity, improves reliability, and makes downstream processing easier.

𝟒. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐖𝐡𝐲, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐖𝐡𝐚𝐭
Agents should understand context. When they know the why, they make better decisions.

𝟓. 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫 𝐁𝐥𝐢𝐧𝐝 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲
Orchestrate multiple steps intelligently instead of relying on a single agent to do everything. This boosts flexibility and reduces failure.

𝟔. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 > 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠
Maximize what you can do with retrieval, RAG, prompt patterns, and few-shot techniques before reaching for expensive fine-tuning.

𝟕. 𝐃𝐨𝐮𝐛𝐥𝐞 𝐃𝐨𝐰𝐧 𝐨𝐧 𝐓𝐨𝐨𝐥 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬
Agents need crystal clear instructions on how and when to use tools, including what to avoid and constraints to follow.

𝟖. 𝐂𝐚𝐜𝐡𝐞 𝐋𝐢𝐤𝐞 𝐘𝐨𝐮 𝐌𝐞𝐚𝐧 𝐈𝐭
Avoid unnecessary calls. Cache results and orchestrate smartly to reduce costs and latency.

𝟗. 𝐔𝐬𝐞 𝐒𝐡𝐚𝐫𝐞𝐝 𝐀𝐫𝐭𝐢𝐟𝐚𝐜𝐭𝐬
Use shared document IDs or structured outputs to avoid data duplication and improve collaboration.

𝟏𝟎. 𝐋𝐨𝐠 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐒𝐞𝐫𝐢𝐨𝐮𝐬𝐥𝐲
Good logging makes debugging, optimization, and scaling infinitely easier. If it’s not logged, it did not happen.

𝐓𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚 𝐛𝐚𝐬𝐢𝐜 𝐚𝐠𝐞𝐧𝐭 𝐚𝐧𝐝 𝐚 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞𝐬𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐜𝐡𝐨𝐢𝐜𝐞𝐬. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐟𝐨𝐥𝐥𝐨𝐰 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐛𝐮𝐢𝐥𝐝𝐬?

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