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.

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

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.

David Regalado

Hi there!

Thank you for visiting my profile. I am passionate about data & AI. Throughout my 17 years of professional experience I have seen projects of various types, sizes and industries. 

Summary

In my country, I've worked in the biggest companies like:

- Financial Services: Scotiabank, Interbank
- Telecommunications: Telefónica del Perú, Claro
- Consulting: Teradata Perú, Indra, Encora
- Consumer Credit Reporting Agencies: Equifax
- Ad-Tech: Manya.pe

In those companies, I did the whole data lifecycle: data extraction, ingestion, data manipulation, data modeling (normalization, denormalization, etc), data analysis, data quality assessments. In short, the ETL part (data pipelines) + data analysis. 

Achievements: The Data Warehouses/Lakes and dashboards were delivered to my clients, so they could start making data-driven decisions.

These recent years I've been working for U.S. clients as a contractor. Specifically:

- Academics: University of Chicago
- Healthcare: Amplifon
- Cyber Security: Anomali
- Ad-Tech: Clevertech
- Startup: Nebula.io

Achievements: Data pipelines delivered and students gratuated from the University program.

Highlight

- Founder at Data Engineering Latam, the largest, fastest-growing data community in Latin America: https://www.linkedin.com/company/data-engineering-latam
- I taught Python for Data Science in the University of Chicago.
- Recognized as Champion Innovator by Google
- I led teams of 20 people in 3 different occasions.
- Recurrent speaker and jury in events related to data & AI.

Studies

- Systems Engineer from the Technological University of Peru (UTP), tenth superior.
- With a specialization in Data Science from Johns Hopkins University.
- With a specialization in Data Warehousing for Business Intelligence from Colorado University.
- With a specialization in Data Science and Big Data Analytics from MIT.
- With 4 certifications, 15 specializations and 67 Google Cloud Platform digital badges.
- With studies in Artificial Intelligence at UBA-IALAB

Tech stack

Cloud Computing: Google Cloud (GCP), AWS, Azure
Data Engineering: Apache Beam, Airflow, Spark, Databricks, Snowflake, dbt, Hadoop, Hive, Luigi
Databases: BigQuery, Teradata, MongoDB, Elasticsearch, Oracle, SQL Server, PostgreSQL, Mysql
Data Visualization: Power BI, Tableau, Looker, Data Studio
Programming languages: Python, Scala, R
DevOps & CI/CD: Terraform, Docker, Kubernetes, Git, GitHub

Roles 

CTO, CDO, CDAO, Data Science Manager, Data Engineering Manager, Head of Data, Tech Leader, Data Architect, Data Engineer, Data Scientist, BI Consultant.

Ambassador at AI Time Journal

AI Time Journal is a publishing platform that strives to aware users of and around the world of Artificial Intelligence, growing technology trends, and other industry news. Its final aim is to contribute to steering the development of AI for the benefit of humanity.

𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱

Most beginners think Linear Regression is just a formula.

It's not. It's your first real predictive system.

𝗪𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗲𝘀

Linear Regression learns a straight-line relationship between input and output.

Example:

House size → Price
Study hours → Marks
Experience → Salary

You give historical data.

It learns a rule.

Then it predicts future values.

𝗧𝗵𝗲 𝗰𝗼𝗿𝗲 𝗶𝗱𝗲𝗮

𝒚 = 𝒎𝒙 + 𝒃

Where:

y ⤷ prediction
x ⤷ input feature
m ⤷ slope (how strongly x affects y)
b ⤷ intercept (base value)

In ML terms:

Prediction = (weight × input) + bias

That's it.

Everything else is optimization.

𝗛𝗼𝘄 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗹𝗲𝗮𝗿𝗻𝘀

It starts with random weights.

Then repeats this loop:

Predict → Measure error → Adjust weights → Repeat

The error is calculated using Mean Squared Error.

Weights are updated using Gradient Descent.

In simple words:

It keeps nudging the line until predictions fit data.

𝗙𝗹𝗼𝘄

Data
↳ Model guesses
↳ Error calculated
↳ Weights updated
↳ Better guesses
↳ Repeat

Eventually:

Best-fit line achieved.

𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗶𝘀 𝘂𝘀𝗲𝗱 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗹𝗶𝗳𝗲

Salary prediction
Sales forecasting
Demand estimation
Risk scoring
Baseline ML models

Almost every ML pipeline starts here.

Even deep learning engineers use Linear Regression as a sanity check.

𝗦𝗶𝗺𝗽𝗹𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

𝘧𝘳𝘰𝘮 𝘴𝘬𝘭𝘦𝘢𝘳𝘯 . 𝘭𝘪𝘯𝘦𝘢𝘳_𝘮𝘰𝘥𝘦𝘭 𝘪𝘮𝘱𝘰𝘳𝘵 𝘓𝘪𝘯𝘦𝘢𝘳𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯
𝘮𝘰𝘥𝘦𝘭 = 𝘓𝘪𝘯𝘦𝘢𝘳𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯()
𝘮𝘰𝘥𝘦𝘭 . 𝘧𝘪𝘵(𝘟_𝘵𝘳𝘢𝘪𝘯, 𝘺_𝘵𝘳𝘢𝘪𝘯)
𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘰𝘯 = 𝘮𝘰𝘥𝘦𝘭 . 𝘱𝘳𝘦𝘥𝘪𝘤𝘵([[5]])
𝘱𝘳𝘪𝘯𝘵(𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘰𝘯)

𝗪𝗵𝗲𝗻 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝘄𝗲𝗹𝗹

⤷ Relationship is roughly linear
⤷ Data is clean
⤷ Outliers are controlled

𝗪𝗵𝗲𝗻 𝗶𝘁 𝗯𝗿𝗲𝗮𝗸𝘀

⤷ Complex nonlinear patterns
⤷ Heavy outliers
⤷ Feature interactions

That's when trees or neural nets step in.

𝗧𝗵𝗲 𝗯𝗶𝗴 𝗹𝗲𝘀𝘀𝗼𝗻

Linear Regression teaches you:

How models learn
How loss works
How optimization behaves
How features influence predictions

If you truly understand this…

everything else in ML becomes easier.

𝗧𝗟;𝗗𝗥

Linear Regression isn't basic. It's foundational.

It shows how machines turn data into decisions.

Master this properly, and half of ML stops feeling mysterious.

--
☁️👨‍💻

I post about Data & AI. I also share thoughts and resources along the way. Follow me for more.

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💸📈Unlocking Business Potential with Data & Generative AI ╏ Startup Advisor ╏ Mentor Featured on Times Square ╏ International Speaker ╏ Google Developer Expert

I've been relentless in my criticism of that misconception. Whoever popularized it did us a lot of harm.

What can be done in a day or a week ends up taking months.

And the worst part is when we talk about data governance. Nobody wants to take responsibility for the accuracy and timeliness of the data, yet they're demanding results from IT and data teams. 😐

👉 𝑫𝒐𝒏'𝒕 𝒍𝒆𝒂𝒗𝒆 𝒚𝒐𝒖𝒓 𝒅𝒂𝒕𝒂 𝒘𝒊𝒕𝒉 𝑰𝑻.

I wrote about it in my Medium account. Check it out: https://lnkd.in/esR2aVHp

--
☁️👨‍💻

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Fractional CDO | Helping Scaling Tech founders turn data into faster decisions | Founder @ Valiotti Analytics

Data strategy is “IT’s problem.”
Which is corporate for: “Please install the growth.”

It usually starts innocently: data feels technical and IT feels technical. So we put the data team under IT and call it alignment → congratulations, you’ve just turned your future competitive advantage into a helpdesk category!!!
Now watch what happens.

The business asks, “Why are we losing customers in month three?”
Data replies, “The pipeline is stable.”

Marketing asks, “Which channel actually drives margin?”
Data replies, “We migrated to a new warehouse.”

Finance asks, “Can we trust this forecast?”
Data replies, “Access permissions are updated.”

Everyone is technically correct, aaand… completely missing the point.

When data sits inside IT, it inherits IT’s incentives: uptime, cost control, security, system health. All important. None of them answer, “Where should we place our next €5M bet?”

So the organization slowly drifts into a very modern tragedy:

Beautiful dashboards → Zero decisions.
Six definitions of “active customer” → One very awkward board meeting.
Three new tools → No one accountable for the number.

Then leadership concludes, “We need more data.”
No. You need fewer hiding spots.

A data strategy is not a collection of tools. It’s a set of hard choices.

→ Which metrics actually drive this business? 
→ Who owns them? 
→ What decision changes when they move? 
→ What are we willing to stop doing because the signal says so?

That conversation doesn’t belong in a ticketing system.
Execution is cheap now. AI will write your SQL. Vendors will promise a single source of truth by Friday. Interns can spin up a dashboard before lunch.
What’s rare is alignment: around definitions, ownership, decisions that hurt a little.

If your data team only reports into IT, you don’t have a data strategy.
You have very sophisticated plumbing. And plumbing is great, until you expect it to drive growth.

-----

👋  Hi, I’m Nick! If this felt uncomfortably accurate, you’re my kind of reader, so follow along!

The music industry is facing an invisible invasion. A recent video by Rick Beato exposes the alarming surge of AI-generated music on major streaming platforms. Take Sienna Rose, an "artist" boasting over 4 million monthly listeners on Spotify, yet, she's nothing but an algorithm, a digital phantom.

𝐖𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐚 𝐛𝐢𝐠 𝐝𝐞𝐚𝐥?

👉 Deception at Scale: Streaming giants like Spotify are actively promoting these AI fabrications without disclosure. This is a deliberate act of deceiving listeners who believe they're engaging with human creativity. What else are we consuming without knowing its true origin? 🍝💩🧑‍🦯

👉 Theft of the Human Spirit: As Beato powerfully states, this AI music is forged from "fragments stolen and recombined" from the very work of real artists. This is an alarming form of digital appropriation, exploiting human artistry without credit, compensation, or consent. 💰💰

👉 Erosion of Reality: The rise of AI artists with inflated, unverified metrics—millions of listeners with virtually no real-world engagement—is corrupting the very foundations of how artistic success is measured. This creates a deceptive landscape where genuine human connection is overshadowed by algorithmic manipulation, leaving us to wonder: what is truly real in the music we hear? 🎧🎶

This situation sparks critical questions about the future of art and authenticity.

What are your thoughts? Should we demand full transparency from streaming platforms about the origin of the music we consume? Or does it even matter, as long as the song sounds "good"? Share your perspective in the comments below!

PS: Rick Beato is a popular American YouTuber, multi-instrumentalist, music producer, and educator known for his in-depth music theory analyses, "Everything Music" series, and interviews with legendary musicians. With over 5 million subscribers, he analyzes, critiques, and celebrates music across genres, often focusing on production, songwriting, and technical aspects. 

--
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Google just dropped a FREE Gemini Prompting Guide

Learn how to prompt effectively, no experience needed.

Free to download, no sign-up required.

Includes expert tips, real-world examples, and best practices.

--
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David Regalado - Google Prompting Guide 101.pdf

🎉 Starting today: Gemini 3.1 Pro is rolling out!!!

The upgraded core intelligence is rolling out across consumer and developer products. You can access 3.1 Pro through the Gemini API, Vertex AI, the Gemini app, and NotebookLM.

𝐁𝐮𝐥𝐥𝐞𝐭 𝐩𝐨𝐢𝐧𝐭𝐬 𝐟𝐨𝐫 𝐩𝐞𝐨𝐩𝐥𝐞 𝐢𝐧 𝐚 𝐡𝐮𝐫𝐫𝐲

✅ Gemini 3.1 Pro: A smarter model for your most complex tasks introduces Google's upgraded AI model.
✅ Gemini 3.1 Pro is rolling out to developers, enterprises, and consumers via various platforms.
✅ This new model shows improved reasoning, scoring significantly higher on complex problem-solving benchmarks.
✅ It's designed for tasks needing advanced reasoning, like synthesizing data or explaining complex topics.
✅ Google is releasing 3.1 Pro in preview to validate updates and advance agentic workflows further.

More details in the link in the comments.

--
☁️👨‍💻

I post about Data & AI. I also share thoughts and resources along the way. Follow me for more.

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Google just released 9 FREE AI courses with certificates + Bonus!
 No paywalls. No fluff. Actually useful.

If you want to understand AI fundamentals and how to apply them in real work, this is a solid place to start.

Here’s the full list:
1️⃣ Introduction to Cloud Computing
 Learn cloud basics and why they matter for AI infrastructure.
 🔗 https://lnkd.in/dszqj_qn

✅Bonus: I am delivering a virtual keynote about Gen AI in 2026. It directly shapes how products are built, teams operate, content is created, and decisions are made. https://bit.ly/465OZFd


2️⃣ Prompt Engineering in Vertex AI
 Learn how to write better prompts and get more out of Gemini.
 🔗 https://lnkd.in/d9wC4ebU

3️⃣ Google AI Essentials (Coursera)
 Use AI for productivity, brainstorming, and writing.
 🔗 https://lnkd.in/dEwRtF8D

4️⃣ What Is Generative AI?
 Beginner-friendly, no coding required.
 🔗 https://lnkd.in/daSzYHbB

5️⃣ Large Language Models Explained
 Understand how models like ChatGPT actually work.
 🔗 https://lnkd.in/dPSfAkjp

6️⃣ AI Principles in Google Cloud
 How Google applies responsible AI in real products.
 🔗 https://lnkd.in/dkpqVKjz

7️⃣ Programming Basics
 Foundational coding and logical thinking.
 🔗 https://lnkd.in/dAeumffE

8️⃣ Cloud Computing Foundations
 Cloud, big data, and machine learning — together.
 🔗 https://lnkd.in/dncT-CjT

9️⃣ Responsible AI Practices
 Build ethical, safe, and inclusive AI systems.
 🔗 https://lnkd.in/dZ8XzpKR

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The Battle

Greg Coquillo

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taplio

The Battle

Greg Coquillo

David Regalado

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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);}Greg Coquillo's LinkedIn Performance

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Performance ScoreGreg Coquillo's LinkedIn Performance