🦾Jepson Taylor

VEOX

VEOX Inc is revolutionizing the AI industry by creating an AI-driven algorithm factory, compressing decades of progress into moments. Our technology automates AI science research, continuously improving algorithm efficiency and outperforming human-made algorithms. VEOX is not just building another AI company; we're catalyzing an explosion of technological advancements that will reshape the entire tech landscape. Join us in accelerating intelligence beyond human limits and leading the next wave of AI breakthroughs.

CEO/founder VEOX

Artificial Intelligence Masterclass, NYU - Intellibus

FYI, I’ll be co-leading an AI Master Class for about 20 senior leaders at NYU’s School of Professional Studies in September and October!  We have a fantastic team of expert AI panelists too, and I’d love to have you join the program. If you’re keen then drop me a note and I’ll make sure a spot is saved for you.  Also, feel free to send this link onto others who will benefit from joining. This course will be the #1 AI course in the US for senior leaders and will change the way you think about AI and how we tackle its opportunities and threats. A big part of this course is the close collaboration between industry and academia. More info here: https://www.aimasterclass.com/

Contributing Faculty

AtomicSoul.ai

I love being the host for the Atomic Soul Podcast where I explore the very things driving humanity forward. This isn’t your average “AI is taking over our civilization” show - it’s something much more rare that’s sure to make you reevaluate your perspective on controversial topics. Each week we’ll hear from the world’s most interesting people, like corporate giant execs and people bending the mold. It’s at this very intersection of tech and the brain where we expand our consciousness.

Podcast Host

Dataiku

I will provide straightforward industry perspectives and insights, thought leadership on the future of AI, and share the stories and best practices of Dataiku’s growing enterprise customers. I will help accelerate the company’s strong recent momentum, including: 
Over 500 customers with 150+ on the Forbes Global 2000 list
95% customer retention rate with Forbes Global 2000 customers 
Industry recognition includes being named to the Forbes Cloud 100 and Forbes AI 50 lists, top 10 in the Data50, and Snowflake’s ML/AI partner of the year.  
Certification as a Great Place to Work, with 94% of Dataikers saying the company is a great place to work (compared to 57% of employees in a typical U.S.-based company)

You can’t have fun if the fundamentals aren’t working and I was impressed with Dataiku’s growth, enterprise customer count, customer satisfaction, and one collaborative platform. 

I want to work for a market leader and believe that Dataiku is indeed the leader that industry analysts and 500+ customers say they are and I want to invest my life force into this company. 

I am very excited about the next year, and my excitement has only continued to increase since joining. This company has the soul I was seeking.

Read more about my WHY here https://blog.dataiku.com/ben-taylor

Chief AI Strategist

DataRobot

As Chief AI Strategist at DataRobot, I work closely with our innovation team, marketing, sales, and product to ensure innovation is aligned with our product roadmap/strategy. I also help nurture strategic partnerships for the innovation team, can we combine like-minded visionaries to accelerate the future? Can we inspire the next generation by combining world-class innovation with brilliant storytelling? I love working with brilliant technologists and amplifying them with genius creatives. Magic happens between these two types of experts. Some of that work was showcased with our 10,000 cast piece which ended up being nominated for a Webby award. Very excited about what we have in the pipeline this year.

Artificial intelligence is already changing our world for the better. Unfortunately, too many homegrown projects are failing giving the rest of the industry a bad rap. Too many executives are naive to the opportunity. The gap of "do-ability" isn't being crossed in the mind of the business executive. AI should inspire, it should transform, everyone should wake up in the morning excited about what AI could do to automate their business and lift our society to new innovative heights.

Chief AI Evangelist

Zeff

co-founder, Chief AI Officer

Working crazy hours to deliver the world's best machine and deep learning to product companies with the least amount of effort. Our goal was to be easiest to use, but surprisingly we are winning on scalability, accuracy, and implementation time. If you need any machine learning in 2017/2018 reach out to see if you are a fit for our current offering. Great press to come.

co-founder & Chief Data Officer

HireVue

Building out HireVue's data science team with the talent needed to execute on our technical data roadmap. We currently have one of the best deep learning teams in the state and by next year it could be one of the top 10 deep learning teams in the country. I love the work we are doing, the opportunity we have, and the success we are seeing. Working with brilliant people is critical to job satisfaction and we have that at HireVue.

Chief Data Scientist

Is it possible to take something as unstructured and complicated as a digital interview and predict the quality of the candidate response? The answer is yes! Imagine what you can do with 100,000x more data than a resume. Leading HireVue's data science team to success on algorithm development for the HireVue insights IRIS product, a new data frontier for sure.

Principal Data Scientist

Metaheuristic Solutions LLC

Metaheuristic Solutions is a optimization consulting company with the world's leading metaheuristic optimization algorithm, the x algorithm. The new algorithm was developed by Ben Taylor and was made available in 2011.

Founder/CTO

IM Flash Technologies

Using Matlab, R, and Julia to develop multivariate and single variate models and analysis (MVA/UVA) for tool fault detection. These models are then used to shut down tools used in the semiconductor manufacturing process, notify stake holders, and mark up lots for real-time detection analysis (RDA). Projects also include building complex yield models from tool data and automating alarm rule setup as well as fab wide tool fingerprinting to identify rogue behavior even if alarms are not present.

I am also very busy on the web app side building PHP/Mysql wrappers to give engineers access to big data solutions. Hoping to switch soon to python/django/postgres. Also using custom built SQL queuing system that I designed on the backend to manage user loading for job requests.

Senior Data Scientist, Fault Detection

launchup.org

observer

I build stuff that doesn’t exist, an AI explorer. 
Unfortunately I have too many connections so please follow me instead. 

人工智能 (Chinese)
人工知能 (Japanese)
कृत्रिम बुद्धिमत्ता (hindi)
الذكاء الاصطناعي (Standard Arabic)
কৃত্রিম বুদ্ধিমত্তা (Bengali)
искусственный интеллект (Russian)

I watch the watchers, I direct the learners. 

I am passionate about machine learning. My life's mission is to further the boundaries of what is possible with data science. I spend as much time as I can working with deep learning, NLP, and all forms of data science. I currently have a passion project I am managing where I am combining the power of genetic programming with deep learning to automate network design. I think there is a trend happening in data science where human expertise and creativity are being replaced by powerful new methods (i.e. deep learning, big data). 

I am most well known for my machine learning work with the data science community and with HireVue. With the community, I enjoy posting opinions on jobs and tutorials on ML methods. I also enjoy speaking and presenting to all types of audiences. 

I've enjoyed working across engineering, product, sales, marketing, and customer success. This wider POV has helped shape the way I think about innovation, from the innovation farm to the customer's table ;)

Lip biting… let’s discuss on LinkedIn 

This is a photo of me… 24yrs ago doing an authentic lip bite hanging out with my girlfriend (now wife). 

The lip bite is interesting… it has a lot of emotion in it. There is “choas hesitancy” in it? It is awkward when you see it. I’m not settled. 

My hope is all of my kids will find someone who makes them bite their lip awkwardly. I think no expression captures love more than this... staring at a 5th class rapid and saying I'm all in on this, and diving head first. 

Will AI ever bite it’s own lip, if so… what would that mean? One of those random human posts, love that my life parter still makes me bite my lip today 24yrs later.

CEO/founder VEOX Inc | Fasted Ultra Runner | Contributing Faculty AIMasterClass NYU | Keynote Speaker | former child

No alternative text description for this image

Well this is might be a little awkward but I'll ask anyway, I like awkward. If you are a DataRobot or Dataiku customer and you are thinking about not renewing next year I would really like to understand why [confidentially]. In return I'll offer you an in-person dinner and a longer conversation about AI and strategy. I'll also share some of my 'off the menu' Jepson stories. Having longer conversations lately with various CDAOs on the future of ML platform companies and whether or not their moats are evaporating or... are they keeping pace. Please send me a PM and we can jump on a quick zoom and schedule an in-person dinner (assuming you are in the US, outside of the US will have to be aligned with other travel). 

Also qualifies: other ML platforms not mentioned H2O.ai etc...

(network: likes/comments/trolling on this post appreciated for reach)

I already wasn't a big fan of Forester Public Relations, but when they were paid to defend nonfounding executives selling $32M of stock... 

"Executive stock sales ($32M) don't necessarily indicate problems." -Forester

How can you trust them on anything else they say after that? 

“Whoever is careless with the truth in small matters cannot be trusted with important matters.”
— Albert Einstein

WTF… this parking lot should be full? Too cold?

So in the future... some of you may choose to have a neural link implanted in your mind, a second observer. It will consume your life experience and give you real-time advice. What to say, what to do, reminders, knowledge retrieval. Feels like science fiction but in... 20-30 years I would say of course this will be available. A secret super power. 

Now for the fun part, if it is always right, if the times you don't follow it you make mistakes (less money, hurt relationships, etc..) then... would you consider allowing it to take over and override your internal decisions. Cruise control through life to maximize the outcomes you want. Have the hard conversations, do the hard workouts, make the best decisions for future you. 

The concepts shines a lot of what it means to be human... to remove all mistakes, to reach perfection... removes you. Would you cruise control your life if you knew it would say all the right things and do all the right things for you? Sometimes I feel like my daily optimal decisions are <20% (like working out, etc..)

Staying ahead in AI is tough, nearly impossible for most executives. We're launching our 5th cohort this fall at NYU November 20-22. A few spots remain for leaders wanting to level up.

Join me and Aditya (Ed) Watal for 2.5 intensive days unpacking everything we've implemented with our teams. This is the only executive AI course I know that dives into these exact topics at this level of depth. 

Group discounts are also available, bring your leadership teams (message for details around group discount rates.) 

Find out more here: https://lnkd.in/gcZhh_P7
Or reach out to me directly with course questions.

AIMasterClassNYU

So this is how the dyson sphere gets started... building data centers in space. Unlimited power + incredible cooling. Awesome to see this starting with Starcloud, very curious how compact they can go on the compute/storage... is natural convection enough or do they need active cooling in space?

Greg Coquillo

Amazon Web Services (AWS)

As a member of the Data Center Systems & Automation (DCSA) Critical Operations Leadership team, I lead a cross-functional groups comprised of 100+ Network Engineers, Software Engineers, Technical Infrastructure Program Managers, and Infrastructure Delivery to build robust distributed systems (using Native AWS services) that accelerate network scaling capacity for AWS customers to support global AI/ML workloads.

Product Management Technical - Data Center Automation

Amazon

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.

While at Amazon Retail, I led cross-functional teams of Product, Engineering Program, and Risk to innovate on behalf of customers, while ensuring product compliance and safety. The collective effort reduced time-to-market by xx% and operational costs by $xxM annually.

Deliverables include: 
-A Transformer-based document orchestrator and processor handling xxxM data points per month.

- An automated purchase order gate that increased Gross Merchandise Sales by $xxxM.

 -A successful migration of xx petabytes of data to a new, secure, and private data lake with Redshift-enabled architecture to ensure our business complies with global regulations.

Product Management, Gates & Media Automation

CharlieDAO

CharlieDAO is a collective of analysts, product folks, crypto natives, and web developers making Web 3.0 tools and systems. Areas of Focus: NFT Analytics & DeFi primitives. 

We're growing on Discord, please DM if you would like to learn more and possibly join! All we expect is a willingness to learn & participate 2-5 hours / month. Sharing articles, meeting others, and supporting projects as you see fit.

Advisor - Product Management & Strategy

Lonza

Harvesting the power of information through global data analysis, professional networking, cross-team collaborations and strategic business decisions to maximize profits for the $1.2B Microbial Control Solutions Portfolio.

Lead Product Manager, Global Pricing Optimization

Maximize Profit (EBITDA) for a global portfolio of products achieving $350 Million in annual sales.
•	Improve MRP system via SAP, BW and IBP for global sites’ Raw Materials and Finished Goods to enable automated replenishment, production planning and service delivery. 
•	Implement and lead Value Chain and S&OP meetings globally and set KPIs for all sites.  
•	Manage product’s life cycles through proper segmentation and value category.
•	Analyze value chain and submit Capex for Key Projects to increase market share and improve operational efficiency
•	Post-Merger integration and portfolio optimization between Lonza and Diacon.  Reduce portfolio by 25% by eliminating custom blends and re-branding under Lonza umbrella. 
•	Partner with Sourcing, Supply Chain, Operation, Marketing, Sales and IT globally to bring value to the organization by organic market share gain and operational excellence.

Lead Product Manager, Global Value Chain

Avery Dennison

Lead a team of 60 employees and drove profitability for an $80 Million business.
- P&L Management and Owner of key KPI's including Safety, Quality, Productivity, Cost and Delivery for the Finishing Department.
-Lead deployment of continuous improvement activities centered on data-driven root cause analysis, Corrective Actions and Counter Measures. 
-Managed the department's R&O (Risks and Opportunities), while aligning with Divisional S&OP and reducing Labor Hour per BSI by 30 % in 6 months.

Value Stream Manager

Supporting key plant metrics to contribute to the organization’s OGSM:
			Safety - Zero Recordable (Zero is Achievable Program)
			Quality - CPMO (Complaints per Million Opportunities) target of 1.37
			Delivery - Stock Flex target 97.3%, Custom Coat target 97%
			Productivity - Coater UEE (Uptime and Equipment Effectiveness 70%)
			Losses (% of TVP) - Target of 2.22%
•	Manage the Safety Participation Program to achieve 100% completion rate
•	Support the plant’s ISO 9001 standard through the management of the Quality Inspection System (Technical Analysis, Certificate of Analysis, Process Control Standards, etc…)
•	Manage the Corrective and Prevention Action Program for customer satisfaction
•	Establish rapport with vendors and partner with them during Continuous Improvement projects
•	Support customers claims with short response time while providing root cause analysis and corrective action
•	File Vendor Claims exceeding $400,000 per year to reduce Losses
•	Lead the Coater Wrinkle Project to reduce wrinkles claims by 25%
•	Lead the Finishing Machine Cleaning Standard Project to reduce contamination claim by 50%
•	Perform data analysis of adhesive coat weight profiles using an ROQ Roll Hardness Tester to predict  die change (Total Preventive Maintenance) and avoid High/Low Coat Weight claims

Quality and Safety Process Engineer

Global Operations Leadership Development Associate
Worked with plant leaders and cross-functional team members of Finishing, Coating, Shipping and Supply Chain to achieve a stock delivery flexibility of 97.6% and a custom coat shipped flexibility of 98% while leading a team of 28 employees in Finishing and driving operational excellence through safety, quality, delivery and cost. 
•	Zero safety injuries in 2014 through daily improvements and communications using our “Zero is Achievable” approach. 
•	Reduced transactional quality complaints by 30% with the B-Shift Finishing team.
•	Increased productivity by 15% through SMED and flexible staffing.
•	Increased training completion rates and overall skills of B-shift Finishing (Operators, Assistants).
•	Consolidated small rolls and reduced by 25% in the facility and increased machine up time by 10%

Finishing Manager

Global Operations Leadership Development Associate
Collaborated with plant leaders including the materials manager, the master production scheduler and coating manager to improve and maintain a service level of 99.3%, a custom coat shipped flexibility of more than 90%, and a stock delivered flexibility of 94%, while saving at least $400,000 in operational costs across LPM plants and promoting safety through the “Zero is Achievable” campaign.

Supply Chain Planner

Publix Super Markets

Teamed up with manufacturing leaders like plant managers, production and quality control managers, plant engineers and production line associates to execute continuous improvement projects that included waste reduction, quality assurance, safety training, transfer of knowledge and leadership development, expecting to save upwards of $3,000,000 for 2 Bakeries, 2 Fresh Foods, 1 Deli plants by the 4th quarter of 2011.

Industrial Process Engineer

AI Product @Amazon | Startup Investor | 2X Linkedin Top Voice - 2021 Tech & Innovation, 2020 Data Science & AI | Quantum Computing & Web 3.0 | I use technology to scale and improve compliance processes | Content Creator

Book a session with at Topmate: https://topmate.io/greg_coquillo

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

🗺️ Here’s a quick mindmap to learn Agentic AI

Agentic AI is so far the biggest shift from traditional, static AI models to autonomous, goal-driven systems capable of reasoning, planning, and acting on their own. 

Instead of just generating text or images, these agents can now make decisions, interact with tools, and even collaborate with other agents. And they do so all while learning continuously.

If you’ve ever wondered how ChatGPT plugins, AI copilots, or workflow automation tools “think,” that’s Agentic AI in action. Learning it means understanding how to design agents that plan, execute, and optimize tasks dynamically, bringing human-like adaptability into automation.

1. Agentic AI Fundamentals
Start with the basics, which is “what Agentic AI is”, how it differs from traditional AI, and the types of agents (reactive, proactive, goal-driven). Learn how agents communicate with humans or other agents to complete complex workflows.

2. Querying and Interaction
Master prompt engineering for agents, context management, and natural language interfaces. Learn how agents handle multi-turn conversations and execute actions using APIs or databases.

3. Core Components & Architectures
Understand the building blocks of Agentic AI which include frameworks (LangChain, AutoGen, CrewAI), reasoning methods like Chain-of-Thought and Tree-of-Thought, and memory systems that retain and use information efficiently.

4. Optimization and Scaling
Explore how agents coordinate in multi-agent systems (agent swarms), decompose complex tasks, and optimize workflows through feedback loops and continuous learning.

5. Tools and Ecosystem
Get hands-on with popular frameworks like LangChain, AutoGen, Haystack, and CrewAI, and connect them to LLMs such as GPT-4 or Gemini. Learn how to use APIs, plugins, and orchestration tools like Zapier or Make for real-world deployment.

6. Security, Safety, and Reliability
Building responsibly involves implementing role-based permissions, policy enforcement, human-in-the-loop supervision, and strategies for error recovery and reliability in agent systems.

7. Use Cases and Applications
Apply what you’ve learned to real-world solutions: customer support agents, workflow automation assistants, data analysis copilots, or team collaboration bots.

🔹Here’s a tip for you: Start small by building a single-task agent, then expand into multi-agent collaboration. Continuous iterations over time will improve your agentic system building skills.

#agenticsystems

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

Check out this 8-Layer Architecture for LLM Systems

Large Language Models (LLMs) are more than just massive neural networks, they’re complex multi-layered systems built for performance, reliability, and scalability.

Each layer plays a unique role; from managing raw data and embeddings to deployment and safety. Together, they form the backbone of how modern AI operates in real-world environments.

1. Infrastructure Layer
The foundation of LLMs, handling compute power, networking, and storage across CPUs, GPUs, or TPUs.

2. Data Processing Layer
Focuses on data ingestion, cleaning, tokenization, and sampling, which turns raw data into training-ready datasets.

3. Embedding & Representation Layer
Transforms words into numerical embeddings for semantic understanding using techniques like positional encoding and PCA.

4. Model Architecture Layer
Defines the core neural network structure which includes attention heads, normalization, and architecture design for token prediction.

5. Training & Optimization Layer
Handles pretraining, fine-tuning, and distributed optimization for model performance and scalability across datasets.

6. Alignment & Safety Layer
Ensures models align with human values and ethics through reinforcement learning, feedback loops, and safety policies.

7. Evaluation & Serving Layer
Manages testing, inference, and model evaluation pipelines, ensuring reliability and real-world performance consistency.

8. Deployment & Integration Layer
Covers API deployment, SDKs, monitoring, and analytics, bringing the model into production environments.

To summarize, each layer in the LLM architecture contributes to a balanced system that enables real-world integration. However, this doesn’t come without challenges.


#LLM

If you look closely at this stack across providers, you’ll notice that AI is just part of the puzzle. I’m not exaggerating when I say, when launching production-grade systems, 80% of the AI challenges continue to be engineering challenges.
 
Selecting which model to work with isn’t even close to being the whole story. To successfully deploy and scale intelligent systems, one needs to understand how to make tradeoffs while evaluating hundreds of services offered by cloud providers like AWS, Google Cloud, and Microsoft Azure 

Each cloud has its edge; AWS leads in scalability, Google in data innovation, and Microsoft in enterprise integration. Let’s see how they compare across every key layer of the stack :

1.🔸Security & Governance
- AWS ensures secure access and monitoring with IAM and GuardDuty.
- Google focuses on unified security through Command Center and KMS.
- Microsoft leads enterprise defense with Azure Defender and Sentinel.

2.🔸Integration & Automation
- AWS automates workflows with Step Functions and Glue.
- Google connects systems using Dataflow and Workflows.
- Microsoft streamlines operations through Logic Apps and Data Factory.

3.🔸Compute & Infrastructure
- AWS delivers scalable compute with EC2, Lambda, and Inferentia chips.
- Google uses TPUs and GKE for AI scalability.
- Microsoft powers hybrid workloads with Azure VMs and Functions.

4.🔸Data & Analytics
- AWS supports data analysis through Redshift and Athena.
- Google dominates big data with BigQuery and Looker.
- Microsoft combines analytics and visualization via Synapse and Power BI.

5.🔸Edge & Hybrid
- AWS offers low-latency AI with Outposts and Wavelength.
- Google secures edge processing with GDC and Confidential Computing.
- Microsoft extends cloud capabilities using Azure Arc and Stack Edge.

6.🔸Cloud AI Services
- AWS offers SageMaker, Comprehend, and Rekognition APIs.
- Google provides Vertex AI and Gemini for advanced AI solutions.
- Microsoft integrates OpenAI, Cognitive Services, and ML Studio.

7.🔸Agent & Developer Tools
- AWS includes Bedrock Agents and CodeWhisperer.
- Google enables Gemini and LangChain integrations.
- Microsoft supports Copilot Studio and Semantic Kernel.

8.🔸Prototyping & Design Tools
- AWS empowers testing with SageMaker Studio Lab.
- Google simplifies development using AI Studio and Opal.
- Microsoft focuses on no-code creation via Designer and Recognizer Studio.

9.🔸Core Models
- AWS relies on Titan and Bedrock models.
- Google leads with Gemini.
- Microsoft uses Phi, Orca, and Azure OpenAI.

Understand how to set up your architecture for scalability, performance, cost, and reliability is a huge advantage, whether via single-cloud, multi-cloud, hybrid, or on-prem. 

Curious to know how you evaluate tradeoffs from services across these providers to set up your AI systems.

Ever tried to connect the dots between AI, ML, DL, and LLMs and felt like you were swimming in a sea of acronyms?

AI Engineers who consistently build AI products have learned that understanding the full AI ecosystem is just as critical as knowing the latest model release. One cannot successfully build intelligent systems without a fundamental understanding of the layers that make them work, from data and models to infrastructure and agents.

This All in One AI Cheat Sheet (2026 Edition) is a compact technical map that shows how everything fits together across the modern AI stack.

Here is what it covers:

1️⃣AI vs ML vs DL
Learn how Artificial Intelligence, Machine Learning, and Deep Learning differ and where they converge.

2️⃣Core Concepts
Grasp the fundamentals of neural networks, supervised and unsupervised learning, and reinforcement learning.

3️⃣Model Types
Explore regression, classification, clustering, generative models, and transformers that enable context aware reasoning.

4️⃣AI Tools and Frameworks
Work with TensorFlow, PyTorch, Scikit learn, LangChain, AutoGen, and CrewAI to build and deploy intelligent systems.

5️⃣Popular Algorithms
Understand the mathematical foundations behind Linear Regression, Random Forests, SVM, and Gradient Boosting.

6️⃣AI Infrastructure
Discover how FAISS and Pinecone enable vector storage, while MLflow and Weights and Biases support experiment tracking and monitoring.

7️⃣Agentic AI
My favorite evolution in the AI landscape. These are systems that plan, reason, and act autonomously using protocols such as MCP (Model Context Protocol) and A2A (Agent to Agent Communication). This is where LLMs evolve into agents that can collaborate and learn continuously.

8️⃣LLM Techniques and Prompting
Master methods like Chain of Thought, ReAct, RAG, fine tuning, and tool use to enhance reasoning and adaptability.

9️⃣Evaluation Metrics
Track performance through accuracy, RMSE, BLEU, Perplexity, and autonomy efficiency to measure true model and agent impact.

🔟Real World Use Cases
See AI in action through predictive analytics, voice generation, robotics, and decision making in dynamic environments.

Would love feedback on anything to be added or removed from this list. Dont forget to share your experience with the community at large. 

#AI

Who’s leading the charge among the Top 15 AI Companies?

We’ve witnessed how AI has gradually become the backbone of value in tech. Check out how these top 15 major players structure their moats to deliver value in this space:

🔸NVIDIA ($4.33T) anchors the stack since its GPU and compute engines power nearly everything.

🔸Microsoft ($3.80T) and Apple ($3.79T) are turning AI into product abstractions at scale.

🔸Google / Alphabet ($2.98T) remains a core innovator via models, search, and foundation services.

🔸Meta ($1.86T) and Tesla ($1.46T) push AI into social systems, robotics, perception, and autonomy.

🔸Players like CoreWeave, Palantir, Adobe, and Tempus AI illustrate how infrastructure, domain specialization, and platform depth are increasingly prized.

Some Insights Worth Noting

🔹Infrastructure is shifting from commodity to strategic. The recent $14B Meta-CoreWeave deal underlines how long term compute supply contracts can alter competitive balance.  

🔹Among AI firms, multiples remain aggressive: the median revenue multiple is approx 25-30×.  

🔹Efficiency metrics matter: some “AI supernovas” deliver over $1.1M ARR per engineer, far above conventional SaaS norms.  

🔹The frontier is vertical AI: models fine-tuned for health, law, finance, biotech will win share over general models when domain constraints and compliance weigh heavily.

What does real moat look like? It’s a combination of compute + model IP + data + application + feedback loops.

Which company would you add to the list and which one would you remove? And why?

graphical user interface, application, Teams

We’re often fascinated when observing AI Agents act autonomously, being capable of planning, reasoning, and collaborating like humans. But how many of us are aware of what’s under the hood to make all of this possible?

Behind every powerful AI agent lies a multi-layered protocol stack, just like the internet has TCP/IP or cloud has architecture layers.

The below stack defines how agents think, act, and interact, enabling structured intelligence across reasoning, communication, execution, and learning.

Think of it as the “Operating System of Agentic AI”, where each layer plays a role in making decisions, retrieving knowledge, executing tasks, and adapting from feedback.

Here’s the breakdown:

🔸Foundation Layer (L1):
The core data and compute layer, powered by LLMs (GPT, Claude), embeddings, datasets, and GPUs/TPUs.

🔸Execution Layer (L2):
Runs the agent’s actions in real or simulated environments via runtimes like LangGraph and automations on Make.com or n8n.

🔸Infrastructure Layer (L3):
Ensures secure communication through context tokens, message passing, and protocols like A2A and A2E.

🔸Agent Layer (L4):
Focuses on autonomy and intelligence. Includes memory systems (LangChain Memory), tool usage (MCP, APIs), and feedback loops (RLHF).

🔸App Layer (L5–L7):
Handles interaction and coordination, which includes reasoning, planning, and knowledge access using tools like ReAct, ReWOO, and RAG.

In this complex space, building modular intelligence is a good strategy. As an example, you may not need a persistent memory for your agents at first launch, based on well thoughtout tradeoffs. Later on, you should be able to evolve your system by adding long-term memory. Just be careful about how this addition will affect user experience as well as agent performance. 


#AIAgents

taplio

The Battle

🦾Jepson Taylor

Greg Coquillo

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Performance Score🦾Jepson Taylor's LinkedIn Performance

Performance ScoreGreg Coquillo's LinkedIn Performance

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🦾Jepson Taylor

Greg Coquillo

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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);}🦾Jepson Taylor's LinkedIn Performance

Performance ScoreGreg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance Score🦾Jepson Taylor's LinkedIn Performance