🦾Jepson Taylor

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 ;)

CEO/founder 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.

Jepson Taylor

We canceled our team’s Cursor account today. Strange considering I was so smitten by it. 🌹🥀Like a high school fling, onto the next babe. Oh, and I was top 3% globally as a user in 2025 at 22B tokens of code. Should have been on their customer advisory board maybe. AMA

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

Some of the smartest people I've met in my life are... incredibly stupid. They can't read the room, they don't know how to talk to executives, they lack emotional intelligence and self-awareness. I used to always think intelligence was one-dimensional, I really cared about where I thought I was on this scale, now I see it as being ∞-dimensions. Sure, there are some standard tests we can use on humans to determine their ability to problem solve or do various coding challenges, but those tests are narrow. 

I like to say everyone is a genius, when it comes to their life experience. If you're patient enough to listen every human can teach you a lesson. I also muse with the possibility of the village idiot becoming the new savant thanks to AI. Ultimately, where we are headed the future 'genius' is unknown, and humans are not equipped to access them. Longer term, AI will be the only judge of what actually matters when it comes to human super-competency. I like this, because it tears up the standard resume. It means that someone with a PhD in neuroscience might actually be less competitive than the ambitious Uber driver in Dallas from Uganda who just needs to be enabled with a new 🥕 .

Dear r/LinkedLunatics,
I love that I live rent free in the minds of so many members of your community. I love knowing none of you will ever be able to run as far as I do in the mountains, invent at the pace that I'm capable of, or live life as a ridiculous outlier as I do. Every time I show up in your community, I take joy knowing that you are festering with my existence. Looking forward to many more posts where you feel compelled to engage and what I say/do bounces around in your simple keyboard troll minds. 

For those that don't know this community, they became famous when one of their members was caught commenting on the appearance of women on this platform. Degrading them, sexualizing them, only to be caught by accidentally sharing a screenshot that gave away their true identity. So when I think of this community, I think of a group of wanna-be ass grabbers, who have nothing better to do than tear down those that do.

Spam trick, add an Emoji to your name on LinkedIn, then make an email filter to never see your emoji in an email.

graphical user interface, text, application

I'm at the SLC airport, working, getting ready to head to Dubai. 
My 17th trip in 4 years.  If any of you are in the region I'll be speaking at:
http://aifest.ae/

For AI/business leaders, schedule permitting (filling up my calendar now), happy to come and speak to your engineering teams about what I've learn over the last year consuming ~25B tokens for coding agents. The pitfalls, the miracles, best practices, ideas gone wrong, ideas gone right... really right, all of it. And.. happy to show you examples of code that your humans will never be capable of writing, and explain why. #superhumancode

AMA

People who do high volume coding with AI often complain about 'fallbacks', where agents will catch exceptions and quickly patch with a moronic fallback. This can be back up libraries, dummy models, and even synthetic data... beyond stupid. You might get mad at AI, but... 

It makes you wonder? Was it trained with some of the worst human Q/A engineers in the world? My test isn't passing... there we go. This isn't installing... fixed. I can't find this dataset... this will do. Imagining the Q/A engineer that would behave this way makes you wonder, do they exist? If they do, I bet it’s the same people who leave dog poop in plastic bags on walking trails. 

Or is AI just making up some of the most terrible fallback short cuts to satisfy bugs? I've seen this bad behavior in all LLM providers.

Words can't describe how much I love the work I do. I can't type fast enough, I can't ask fast enough. I often will have 4-5 parallel threads of work that I'm managing, if only my brain could handle more. In the future as agents level up, it will. Working in the field I work in, with the technology vertical we are chasing is like waking up for Christmas daily. To review algorithms, and insights that I would never be able to produce on my own regardless the time, pressure, or budgets, is inspiring. 

Once when I was giving a talk to college students (I like giving talks to college kids, taught in a box, ready to break out, brains are like playdough). I think it was Berkley or Harvard, can't remember. I told them.

"When you do a startup, I hope you do, you need to find an idea that is so big you're willing to fail"

"What do I mean by fail? You need to be willing to foreclose on your home, have your cars reposed, max out every card you can get access to. Worry about your banking card clearing when you're buying gas and food. Where you need to awkwardly beg your friends and family for favors and emergency loans."

The college kids faces... eyes wide... 

Then I went back to the idea. If you aren't willing to endure that, then the idea wasn't big enough. I'm not talking about big enough for your professor! Your friends! Your parents! For YOU! Too often people want validation on their ideas, the best founders I know needed none. They were determined to chase the heroes journey despite head winds from their tribe. Parents and friends telling them they were stupid, and should get a real job. Find that idea big enough for you, the idea burning a hole in your head. The idea that is beyond passion, beyond obsession, entering the realm of purpose. 

For me, I have that idea, and couldn't be happier.

For all my 'fast typers' out there in Antigravity, Cursor, Claude, Chrome, etc... who are on Macs. I'm sure you have accidentally in your typing storm pressed Command+Q and closed your application. Very upsetting, well... you can change that close command on all your applications to Command + Option + Q (or any custom command). Now? Type faster!

Flights, time to think when the internet sucks. Qatar Airways was a dream my last trip with Starlink 100-150mbs down the entire time with great latency. I felt like I was (nearly) home in my office. Emirates.. gave the 2014 feeling with Onair 10-12mbs down experience with constant outages. I'm surprised more airlines don't get with the times, especially with those of us that work 24/7. In the future I'm going to call ahead to check if the plane I'm committed to has Starlink or not. 

During my thinking time, I was thinking more about the vast majority of the world is not ready for AI and will never be. You have toe dippers, and you have sprinters. The sprinters are leveling up every day, moving past 10x engineers and into territories that feel mythical. All of the 'vibe coding' nightmares are getting fixed, the wack-a-mole game is being won. Every problem found has a solution. Also, new technologies keep showing up that provide huge strides forward. 

I think a year from now, the toe-dipper engineers will be cave men compare to aliens. Are you a cave man? Or are you becoming an alien? What is your favorite tool/trick that makes you feel like you have more control over the chaos?

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 Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. 

They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. 

These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. 

As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision.

The below chart shows just how broad their impact is:

1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions
2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching
3.🔹 Education : Flashcards, concept explainers, AI tutors
4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach
5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates
6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring
7.🔹 Research : Academic writing, data cleaning, topic expansion
8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries
9.🔹 Smart Environments : Digital twins, voice commands, access control
10.🔹Ops Automation : Shift scheduling, system alerts, order tracking

What used require significant manual effort, now takes a few smart agents.

I believe it’s a great time to start exploring and experimenting in this space…

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed.

People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently.
Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills.

This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms.

Here’s the full breakdown, day by day:

Day 1 - AI Automation Fundamentals
Learn what automation really means, how it differs from AI and agents, and see real examples.

Day 2 - Automation Thinking
Break work into steps, triggers, and outcomes - the mindset behind every good automation.

Day 3 - APIs & Webhooks Basics
Understand how apps communicate and how events trigger workflows.

Day 4 - No-Code Automation Platforms
Explore Zapier, Make, n8n - and how no-code tools actually run workflows.

Day 5 - Build Your First Automation
Create a simple trigger-action workflow and connect two apps.

Day 6 - Data Handling
Pass data between steps, map fields, and work with text, numbers, and dates.

Day 7 - Logic & Error Handling
Add filters, conditional logic, retries, and fallbacks to keep automations reliable.

Day 8 - AI Model Basics
Learn prompts vs system instructions, tokens, limits, and LLM behavior.

Day 9 - Using AI Inside Automations
Insert AI steps into workflows and parse structured AI outputs.

Day 10 - Prompt Design for Automation
Write consistent prompts and reduce hallucinations with JSON outputs.

Day 11 - Text-Based Task Automation
Automate email replies, summaries, CRM updates, and document tasks.

Day 12 - Knowledge Automation (RAG Basics)
Connect AI to internal documents and fetch accurate answers from real data.

Day 13 - AI Agents Basics
Understand agent planning, tools, and identify use cases for agents.

Day 14 - Business Use Case Automation
Automate lead qualification, ticket routing, and internal processes.

Day 15 - Sales & Marketing Automation
Personalize outreach, repurpose content, and automate follow-ups.

Day 16 - Operations Automation
Manage approvals, notifications, and repetitive operational tasks.

Day 17 - Monitoring & Optimization
Track workflow success, cut costs, and improve performance.

Day 18 - Build & Ship Your System
Design, test, document, and finalize a complete end-to-end automation.

You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution.

Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Data ➝ AI ➝ Value.
That’s still what companies think AI Looks like

But in the real world…

AI inside companies looks nothing like that.

Because value doesn’t come from “building a model”.

Value comes from building a working system around the model.

This guide explains what AI actually looks like in companies 👇

It’s a full production chain, not a single step:

1) Data
Selection, sourcing, synthesis - getting usable data is already 50% of the work.

2) Data Science
Exploration → cleaning → normalization → feature engineering → scaling
Then modeling: selection → training → evaluation → tuning

3) Value (Operationalizing)
Real value starts only after:
registration → deployment → monitoring → retraining

So ML is not “AI work”.

It’s data engineering + modeling + MLOps, all together.

What GenAI-enabled AI actually looks like

GenAI doesn’t remove complexity.

It shifts complexity from “models” to workflows + control.

The real GenAI path is:

- Understand the decision process
What decisions are being made today? Where does judgment happen?

- Workflow experimentation
Iterate with prompts, APIs, tools, retrieval, automation, coding — until it works reliably.

- Guardrails
Policies, safety filters, access control, cost limits, prompt rules, grounding.

- Monitoring
Track quality, drift, failure modes, user behavior, data leakage, hallucinations.

- Human in the loop
Because the highest-value workflows still need supervision, approvals, and feedback.

The takeaway?

AI isn’t a “feature.”

AI is an operating system change.

And companies that understand this early…

ship faster, safer, and with real ROI.

If you're building enterprise AI, stop chasing “Data → AI → Value”.

Start building: Workflows → Guardrails → Monitoring → Value.

#AI

Knowing RAG is easy. Measuring it is the real skill.

Everyone can plug in a vector DB and call it a RAG system.
But only a few can actually evaluate whether the system retrieves the right knowledge, avoids hallucinations, and answers with confidence.

That’s where retrieval metrics matter, they turn guesswork into engineering.

Here’s a simple breakdown of the 20 metrics that actually show whether your RAG system is good… or just lucky.

1. Precision@K - Checks how many retrieved chunks are truly relevant.
2. Recall@K - Measures whether all important chunks were found.
3. Hit Rate - Confirms if at least one correct chunk appears.
4. MRR - Rewards results where the correct chunk ranks high.
5. NDCG@K - Evaluates ranking quality, not just correctness.
6. Top-1 Accuracy - Tests whether the first chunk is the best one.
7. Query Coverage - Checks if retrieval answers the entire question.
8. Evidence Sufficiency - Asks: “Is this enough to answer fully?”
9. Context Relevance Score - Measures alignment between query meaning and chunk content.
10. Chunk Redundancy Rate - Tracks repeated or duplicate chunks.
11. Chunk Diversity Score - Shows whether results come from varied, reliable sources.
12. Source Coverage - Checks if retrieval pulls from the right documents.
13. Citation Accuracy - Confirms the final answer cites real, correct evidence.
14. Groundedness Score - Tests how strongly the answer stays rooted in retrieved context.
15. Faithfulness - Ensures the model doesn’t distort the evidence.
16. Hallucination Rate - Measures unsupported claims the model invents.
17. Retrieval Latency - How fast chunks reach the model.
18. Token Efficiency - How much useful context fits into the token limit.
19. Freshness - Checks if the retrieved info is up-to-date.
20. Retrieval Failure Rate - Tracks how often retrieval returns irrelevant or misleading chunks.

Anyone can build RAG.
Only skilled teams can measure retrieval, and that’s what decides whether your system scales or breaks.

Which metric do you think is the most underrated right now?

Single agents are powerful.
But multi-agent systems?
That’s where AI starts behaving like a real team.

As AI moves from “answering questions” to doing actual work, the real unlock isn’t a bigger model, it’s how multiple agents collaborate.

Different patterns fit different types of tasks, and choosing the right one can be the difference between a smart system and a chaotic one.

Here are 8 multi-agent collaboration patterns every builder should understand:

1. Manager–Worker Pattern
One agent breaks down the task, others execute the pieces.
Great for large, structured work like research + writing + QA.

2. Specialist Agents (Role-Based Team)
Each agent has a defined role - researcher, analyzer, writer.
Best when accuracy and expertise matter.

3. Planner–Executor Pattern
A planner decides the steps; an executor runs tools and actions.
Ideal for automation tasks, workflows, and system integrations.

4. Critic / Reviewer Agent
One agent generates, another fixes mistakes.
Useful for RAG responses, code checks, compliance-sensitive tasks.

5. Debate Pattern
Agents challenge each other with alternative solutions until the best idea wins.
Good for strategy, architecture, and trade-off decisions.

6. Voting / Consensus Pattern
Multiple agents propose answers, the system picks the best one.
Great for open-ended, ambiguous, or high-risk queries.

7. Swarm / Parallel Exploration
Many agents explore multiple sources at once.
Perfect for fast research and time-sensitive retrieval.

8. Tool Router / Dispatcher Pattern
A router agent decides which tool or agent should handle the request.
Works best in enterprise setups with many databases, APIs, and systems.

AI isn’t becoming powerful because models are getting bigger.
It’s becoming powerful because agents are learning to work together.

This is what turns AI experiments into real products.

Teams get excited about the model… 
but the model is only one slice of the stack.

Real AI products work because every layer around it is engineered with intent.

This breakdown shows what a modern, production-grade AI product stack actually looks like: 

1. Application Layer
The user-facing shell - authentication, UI, APIs, monitoring, analytics. This is where reliability, speed, and trust are felt long before AI runs.

2. Agentic AI Layer
The intelligence engine - reasoning, retrieval, tool use, orchestration, and memory. This layer decides whether your system simply answers… or actually acts.

3. GenAI Ops Layer
The guardrails - tracing, evaluation, experiments, and quality checks. This is how you keep an AI system stable as it learns and evolves in production.

4. Tools & Services Layer
The essentials - model APIs, data warehouses, and external integrations. These services give your AI the inputs, structure, and capabilities it needs to function.

5. Infrastructure Layer
The backbone - compute, storage, GPUs, networking, and security. If this layer is weak, nothing above it scales safely.

AI products don’t succeed because of one great model.
They succeed because every layer, from UI to GPUs - works together as one system.

RAG is not just “upload PDFs + chat with them.”

But that’s like calling every vehicle a “car.”

Because in real AI systems, there are 3 completely different architectures hiding behind the same word:

Naive RAG vs Agentic RAG vs Agentic Memory.

And if you don’t know the difference, you’ll build the wrong system… and blame the LLM later.

Here’s what’s actually happening : 

1. Naive RAG (Basic Retrieval)
Naive RAG is the most common setup: documents get indexed once, stored in a vector database, and whenever a user asks a question, the system fetches the closest chunks and sends them to the LLM. It works well for simple document Q&A, but the accuracy completely depends on retrieval quality  - if the wrong chunk is retrieved, hallucinations become inevitable.

2. Agentic RAG (Tool-based Retrieval + Reasoning)
Agentic RAG is not a fixed pipeline - it’s a decision-making system. Instead of blindly retrieving from a single database, an agent dynamically decides which sources to search, which tools to use, whether the answer needs verification, and when to retry retrieval. This is what enables enterprise-grade search across documents, CRMs, knowledge bases, and even the web - but it also introduces more complexity and cost.

3. Agentic Memory (Long-term Learning Loop)
Agentic Memory is not about pulling knowledge from documents - it’s about building continuity. The system stores useful signals from past interactions (preferences, decisions, repeated tasks, user context) and retrieves them later to give better responses over time. This turns the assistant into something that evolves with usage but if memory isn’t governed properly, it can quickly become noisy, irrelevant, or even risky.

The easiest way to remember it:

Naive RAG = answers from stored docs
Agentic RAG = answers using tools + multiple sources
Agentic Memory = answers that improve over time

If you’re building agents in 2026…
understanding this difference is not optional, it’s architecture.

taplio

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Greg Coquillo

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taplio

The Battle

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Greg Coquillo

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