Armand Ruiz

Director Data Science & AI Elite

VP of Product - AI Platform @IBM

As the Director of AI Engineering at IBM, I lead the watsonx team, a global group of over 1,000 AI Engineers who work on cutting-edge Generative AI solutions for various industries. With more than 12 years of experience in AI, I have a passion for democratizing practical AI skills and empowering professionals to leverage AI to transform their business.

Previously, I managed IBM's Data Science and AI Elite, delivering complex AI implementations for hundreds of clients. 

I also led the product management of IBM Watson Studio and Watson Machine Learning, an AI platform used by hundreds of customers and SaaS apps powered by AI. I hold a Master of Science in Telecommunications from Universitat Politècnica de Catalunya and speak three languages: Spanish, Catalan, French, and English.

Connect with me!

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📧 Email: armand@nocode.ai
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"It is kind of fun to do the impossible"
All opinions are my own and don't represent my employer.

Director watsonx team - Client Engineering

I am building the world's largest team of 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 by

- Recruiting hundreds of new engineers around the globe
- Reskilling our existing talent
- Offering them top-quality training in new-generation AI

Responsible for Pre-Sales Generative AI Pilots with the new IBM watsonx.ai Platform.

I’ve been practically living in Claude Code lately. If you build with AI daily, you know that your orchestration workflow is just as important as the model itself.

Recently, Boris Cherny (the creator of Claude Code at Anthropic) shared the internal best practices and workflows his team actually uses on a daily basis. 

Someone brilliantly distilled those threads into a structured CLAUDE.md file that you can drop straight into the root of any project.

It essentially acts as a system prompt that turns Claude into a much more autonomous, rigorous engineering partner.

Here is what the file enforces:
1/ Workflow Orchestration: Mandates a "Plan Node Default" for any task over 3 steps and uses subagents liberally to keep the main context window clean.

2/ The Self-Improvement Loop: This is the real magic. After ANY correction, it updates a tasks/lessons.md file. You are building a compounding system where the mistake rate drops over time because it actively learns from your feedback.

3/ Verification Before Done: It cannot mark a task complete without proving it works (diffing behavior, running tests, checking logs). "Would a staff engineer approve this?"

4/ Autonomous Bug Fixing: Zero hand-holding. You point it at failing CI tests or error logs, and it goes to work without requiring constant context switching from you.

5/ Strict Task Management: Forces a "Plan First" approach written to a todo.md with checkable items before implementation even starts.

It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of its changes.

If you are spending hours a day in the terminal with AI, setting up a strong .md instruction file like this will save you a massive amount of time and frustration.

It takes time and effort to set this up. If you do, you are ahead of everyone else.

If your employer isn’t providing unlimited Claude Code + secured MCPs to connect your enterprise repos, email, work-chat, meeting AI transcripts, calendar, and docs... they are likely moving at legacy speed. 

Without that context and tool access, you’re stuck in a cycle of manual copy-pasting and reinventing the wheel while the rest of the market moves in parallel workflows.

My advice? Don’t wait for the corporate roadmap to catch up. If you aren't getting these tools at work, force yourself to learn these agentic workflows on the side. 

When companies move this slowly, they eventually run into trouble and you don't want to find yourself at a disadvantage in today's market because your professional environment kept you in the past.

Stay curious and keep your skills ahead of your company's tech stack.

Everyone's talking about which LLM is best for agents. But after reading Lance Martin's deep dive into Manus's architecture, I'm convinced the real differentiator isn't the model. It's the context engineering around it.

The problem: A typical Manus task makes ~50 tool calls. Without careful management, results pile up in the context window and the LLM gets dumber the longer it runs. 

Anthropic calls it "attention budget depletion." 

Chroma calls it "context rot."

Manus solves this with three strategies:
𝟏. 𝐑𝐞𝐝𝐮𝐜𝐞: Older tool results get compacted to file path references. When that's not enough, the full trajectory gets summarized via structured schema.

𝟐. 𝐎𝐟𝐟𝐥𝐨𝐚𝐝: Fewer than 20 atomic tools bound to the LLM. Everything else runs in a sandbox via bash. The model stays lean.

𝟑. 𝐈𝐬𝐨𝐥𝐚𝐭𝐞: Sub-agents exist to give each task a clean context window, not to mimic human roles. No context bleed.

On top of that, Manus doesn't commit to one model. It routes tasks; Claude for code, Gemini for multimodal, OpenAI for math. 

Model-agnostic by design.

The takeaway: Manus has been refactored 5 times since launch. If a stronger model doesn't improve your agent, your engineering is the bottleneck.

Stop obsessing over which model to pick. Start obsessing over the implementation around it.

Full article: https://lnkd.in/gaY3rAV5

Prediction: Token budgets will soon surpass human salaries.

We are entering an era where the cost of compute for reasoning is becoming a primary line item, and the numbers are scaling fast.

I am already seeing developers consuming $100k+ a week in tokens.

It sounds astronomical, but when you look at the output, the ROI is there. They aren't just running a single chat thread; they are spinning up massive parallel processes. Swarms of agents coding, debugging, testing, and architecting simultaneously. The velocity is incredible.

But at that scale, cloud inference costs become the bottleneck.

The big unlock? Local Compute.

Putting a Mac Mini (or equivalent high-performance local hardware) on every developer's desk to run local models is the inevitable next step. We need to move the heavy lifting to the edge to make this sustainable. 

Invest $10,000 on a local computer and you have unlimited AI.

This creates a massive new need for the Enterprise: Fleet Management for Models.

Just as we manage laptops with MDM, companies will need a way to manage these distributed AI systems; securely pushing the latest Open Source models to local machines, updating weights, and orchestrating the workflow between local "thinking" and cloud "verification."

The future is hybrid, and the infrastructure to support it is being built right now.

I’m building my Personal OS to automate my mental overhead. For now, it’s simple, but the potential is already showing. I’ve moved away from "checking notifications" to a Pulse-based system.

How it works: 
1️⃣ Orchestration: Claude.md acts as the central hub, invoked by a launchd scheduler. 
2️⃣ Sequential Logic: Every "Pulse" (Morning/Midday/EOD) triggers a sequence of my agents: Scout → Horizon → Focus. 
3️⃣ Multi-Channel Delivery: I get a unified briefing via macOS notifications, an HTML digest, and my terminal.

The Agent Roster:
- Scout (/scout): Google Chat & Email monitor. It knows my VIP list and drafts replies before I even see the message. If something is important it will notify me, and even give me a call.
- Horizon (/horizon): The calendar expert. No more walking into meetings cold.
- Focus (/focus): The filter. It turns the noise into a top actionable list.

I feel spend less time "managing" my day and more time executing it. 

I also turn all the context for the day ahead into a fun podcast, and that gets me ready for the day and fully prepared.

I've been using computers in a completely different way lately. No much clicking. No browsing through tabs. No filling out forms. I describe what I want done, and agents go do it.

Many are still building software for humans. Replicating existing apps with AI. 

But that's not where this is going. The moment I stopped thinking of myself as a "user" and started thinking of myself as an orchestrator, everything changed. I set goals. I define constraints. I verify outcomes. That's it. The entire middle — the actual work — I don't touch it anymore.

And now I can't unsee it: most software we're building today is already obsolete. It's designed for humans to operate. But humans aren't going to be the operators anymore.

The new generation of software won't have dashboards. It won't have onboarding flows. It'll have APIs, state machines, and feedback loops built for AI agents, not for people.

The real question isn't "how do we develop this product/feature for humans using AI?" --> It's "if no human ever had to touch this, what would we build from scratch?"

if you are in a leadership position, you are punishing your team by keeping them busy.

If your team is too busy doing their "normal jobs" to spend hours a week experimenting with AI, you are failing them.

You are setting them up to be replaced by people who have the time to learn.

They will get disrupted, either by another department within the company or by a competitor, not because of AI, but because of your lack of urgency and understanding of the current landscape.

You have to give people the time, because integrating AI into daily workflows isn't something that happens overnight.

You have to actively remove work from their plates so they can learn the new way to work.

Because we are now all beginners again.

If you don't give them the time, they will never learn it, and your company will suffer a slow, painful decline into inefficiency.

No matter how big you are.

you need to clear your calendar and invest in AI implementation for your own productivity asap

The hardest part isn't the technology. It's making the time to actually sit down and try it.

Block 2 hours a day. Thank me later

Parallelism is the biggest unlock in AI. The real shift isn't speed; it's concurrency.

 I can now run 10 "hard things" simultaneously while I'm in meetings.

My workflow has flipped:
- Old way: Block time to do the work. Maybe use AI to do it faster.
- New way: Block time to review the results and give more context knowledge to the AIs.

We are moving from being "doers" to orchestrators. Your output is no longer limited by your hours, but by the number of parallel streams you can manage.

I get 100s of emails and chats daily. The only way to manage it is to stop everything, dive in, and manually filter the signal from the noise. It's inefficient and a total focus-killer.

So i built Mark, my Communications AI Agent. Its job is simple:
1/ Monitor Everything: It watches all my inboxes and chats 24/7.
2/ Surface What Matters: It identifies critical messages and ignores the rest.
3/ Notify Intelligently: I get a clean Terminal and Desktop notification with a summary file. No more context switching.

But that's just the start. The real magic is next → I'm connecting Mark to a shared knowledge base of all my documents, past conversations, and daily meeting notes.

Soon, it won't just notify me. It will draft context-aware replies for almost everything.

That's my way to solve this. I'm curious...have you developed something similar?

I moved from the EU to the US more than 10 years ago, and while I miss many things about Europe, I have to agree with the man of the hour 🦄 Peter Steinberger : the US is an incredible place for builders right now. 

There's an undeniable energy and enthusiasm here that's hard to find elsewhere. 

It feels like a special time to be building here.

Manus just shared why most people are still stuck in the "chat" era: the real value has shifted from conversational bots to custom superagents.

Chatbots answer questions; superagents execute workflows. 

But they aren't magic, you need to understand the underlying mechanics and put in the time to customize them for your specific needs. If you treat them like a standard search bar, you’re leaving 90% of the value on the table.

Here is how to actually get them to work for you:
1/ Move from Prompting to Engineering: Stop asking "Can you do X?" and start defining the architecture of the task.

2/ Invest in the Setup: Spend the time upfront to define personas, data sources, and specific tool-use constraints. The more "narrow" the agent's focus, the higher the quality.

3/ Iterative Refinement: A superagent is never "done." You have to monitor the execution and tune the instructions based on real-world failures.

Swipe through for the tips that actually matter for high-stakes execution.

Manus Intro

What’s the point of buying software when you can just create it on the fly?

It might take our generation a minute to adapt, but the next generation is growing up expecting to build exactly what they need. For them, the only thing that matters is the infrastructure to power it.

I’ve been building non-stop lately. I’ve never felt this empowered in my career. I can literally turn my ideas into reality in a matter of hours.

yesterday at the gym I heard someone pitching their new AI tool that helps you “do more, faster.” i feel like doing less and slower. am i the only one?

ok, so you moved from a chatbot to an AI agent. and you think you are ahead already.

but if you are still the one triggering all the actions...

if the agent isn't working autonomously for you all day long...

and if you don't have 10 of them running in parallel...

you are nowhere close to calling yourself AI-native. 

i recommend you spend the weekend learning. this is a good place to start: https://lnkd.in/eW4Sam_6

Every PM should understand evals. Not "be an expert." Not "build the eval framework yourself." Just understand them.

Here's why.

A/B testing changed product management forever. Before it, PMs guessed. After it, they measured. Evals are doing the same thing for AI products and systems but most PMs have heard the word but they don't know what they're talking about. 

An eval is how you answer one simple question: "Is my AI actually good?"

Not vibes. Not "it seems better." Not "the demo looked great." Actual, measurable, repeatable quality assessment.

Here's what you should tell tell your product leads:

1. Start with a golden set: a curated dataset where you know the right answers. This becomes your source of truth.

2. Define your graders: what does "good" look like? Accuracy? Tone? Factual correctness? Be specific.

3. Run your model against the golden set and score it. That's your baseline.

4. Every time you change something - new prompt, new model, new data — run evals again. Did it get better or worse?

5. Automate this. The speed of your eval loop determines how fast your AI improves.

That's it. That's the core.

The teams that ship reliable AI products aren't the ones with the best models. They're the ones with the best evals practice.

If you're a PM working on anything AI-related and you can't answer "how do we know it's good?"... start there.

Evals are the new A/B testing. And just like A/B testing, the PMs who get it early will have a massive advantage.

Manus Agents just launched on Telegram and it changes how you interact with AI agents entirely. I've been using this all weekend non-stop.

This isn't a chatbot bolted onto a messaging app. It's the full Manus — reasoning, multi-step task execution, tool use — running inside a conversation you already have open.

Here's what makes it different:
1/ Zero setup friction. Scan a QR code. You're connected in under a minute. No API tokens. No config files. No terminal.

2/ Full task execution from chat. Research, data processing, structured reports, PDFs — initiated and delivered without ever leaving the conversation.

3/ Multimodal by default. Send a voice message, a photo, a document. The agent transcribes, interprets, and acts. No manual file handling.

4/ It remembers how you work. Set your communication style — concise, structured, conversational — and it adapts across every interaction.

5/ You pick the model. Manus 1.6 Max for deep reasoning and creative work. Manus 1.6 Lite for fast, everyday tasks. Both go end-to-end.

But here's what got my attention even more — the cofounder just shared what's coming next:

1/ Create your own specialized agents and plug them into any group chat.

2/ WhatsApp, LINE, Slack, Discord support landing soon.

3/ Native Windows and Mac apps that let Manus operate your computer — like Browser Operator, but way more powerful.

And a lot more already in the shipping pipeline. Big updates dropping over the next 30 days.

Your AI agent shouldn't live behind a login screen on a separate platform. It should be wherever you already are — one message away, ready the moment you need it.

Learn more here: https://lnkd.in/gSuAx5UX

trust your AI overlord and... just hit accept 🤣

Recently, I stumbled across a YouTube video that completely stopped me in my tracks.

An urban explorer infiltrates the abandoned IBM center on the Côte d'Azur in La Gaude in South of France. Hearing the narrator describe it as a "vestige of Silicon Valley" and seeing that massive concrete colossus sitting empty in the middle of the forest is absolutely heartbreaking 💔 

For me, that place wasn't just a building. It was pure magic. It was where my professional life truly began and where my life completely transformed.

Early in my career, I had the chance to visit this unbelievable, high-energy campus while I was interning for IBM in Belgium. I knew immediately I belonged there, and I did absolutely everything in my power to get hired and eventually, I earned my spot.

When I look at the footage of those abandoned offices and empty reception halls, I don't see decay. I see endless, vibrant memories:

- Going from a nervous intern to earning my first full-time role.
- Working late into the night and battling those early-career nerves before major presentations.
- Learning a completely new language (French!).
- The incredible lunches, coffee breaks and walks around the campus with colleagues who became amazing, lifelong friends.
- Spending hours on the campus soccer & tennis courts, both competing in tournaments and playing with colleagues. I also learned to play la pétanque!
- And most importantly, it’s where I met my incredible wife.

That campus was the launchpad that gave me the opportunity to eventually relocate to the US. 

Looking back, I can draw a direct line from walking through those doors in France to my time as VP of AI Platform at IBM, and to do now the most exciting work in my career in Silicon Valley.

The doors might be closed, but the energy of what we experienced there is still very much alive. 

I will forever be grateful for that chapter, those people, and those memories.

Link to the video: https://lnkd.in/gA-Wja2s

Open video: INFILTRATION d’IBM, le plus gros site abandonné de la Côte D’Azur mais aussi le plus Surveillé !!!  on youtu.be

INFILTRATION d’IBM, le plus gros site abandonné de la Côte D’Azur mais aussi le plus Surveillé !!! 

I thought I was a heavy AI user. Then my usage 3x'd in the last 30 days. The pace of change is accelerating and what was cutting-edge a month ago is now the baseline. The new default is managing AI agents, not just using them.

This chart below is my AI usage (tokens)

Nagesh Nama

1️⃣ OVERVIEW:

Are you seeking to lower GxP validation costs and want to use AI for the Intelligent Validation? 

Would you like to minimize risks, shorten timelines, and enhance compliance? 

Are you considering transitioning from CSV to CSA? 

Are you aiming to establish a robust digital data integrity program? 

Are you looking to forecast maintenance needs to minimize breakdown time and enhance efficiency?

Are you looking for a temperature mapping app driven by AI/ML? 

Are you looking for Intelligent AI Agents to automate your GxP processes?

We offer Continuous Intelligent managed services to assist life science clients to migrate to an AI/ML-driven platform with built-in intelligent validation.

2️⃣ WHO IS THIS FOR? 

Our clientele includes multinational pharmaceutical corporations, agile biotech firms, and medical device companies. 

3️⃣ HOW DO WE HELP? 

We can assist you in migrating to an intelligent platform with many pre-built AI/ML apps.   We deliver all our apps with best practices and intelligent validation baked in.

We will help you reduce costs, increase workflow efficiency, comply better…..all these by a factor 10x or more. 

4️⃣ WHAT DO WE DO? 

We have pioneered a "continuous intelligence" platform that enhances compliance, mitigates risks, and streamlines your GxP processes.  

These processes span predictive analytics for operations and maintenance, temperature mapping for storage areas, real-time compliance monitoring for 21 CFR Part 11, intelligent validation for computer systems and more.  

Our managed services exceed the current expectations set by the FDA's CSA. 

Our AI/ML models are monitored for compliance by IBM’s watsonx.gov. 

5️⃣ WHAT ARE CUSTOMERS SAYING:

https://testimonial.to/xlm-continuous-validation/all 

==**==

SERVICES:

🎯Continuous Intelligent Validation (cIV)

🔭Continuous Predictive Maintenance (cPdM)

🌡️Continuous Temperature Mapping (cTM)

Chief Executive Officer

xLM helps Pharma/Biotech/Medical Device Companies incorporate AI/ML apps into their operations:
1.	We design and build AI/ML apps that are continuously validated.
2.	We offer a suite of Data Integrity IT services that can provide intelligent infrastructure for GxP.
3.	We offer cost effective validation managed services that leverages software test automation.

xLM was founded as a subsidiary of ValiMation, Inc. in 2016. We were the first to launch a "Continuous Validation" platform that supported IaaS/PaaS/SaaS while complying with 21 CFR Part 11 / Annex 11.

xLM has been audited by several of its customers and has a well established QMS.

Since 2016 we have continuously developed our platform that has helped our customers contain validation spend (over 7️⃣5️⃣%). 

We started with Continuous Validation services and now offer ContinuousDI (Data Integrity) as well as ContinuousAI managed services.  
Our managed services span the entire IT stack:
-	ContinuousAI (We leverage AI Foundational Models to design/deploy/validate AI apps)
-	ContinuousALM (Application Lifecycle Management)
-	ContinuousDM (Document Management)
-	ContinuousSM (Service Management)
-	ContinuousRM (Risk Management)
-	ContinuousITOM (IT Ops Management)
-	ContinuousMTR (Managed Threat Response)
-	ContinuousRMM (Remote Monitoring and Management)
-	ContinuousLMS (Learning Management System)
-	ContinuousMP (Mail Protection)
-	ContinuousValidation 

Why xLM❔
1.	FIRST to deliver AI/ML apps that are GxP compatible out of the box.
2.	FIRST to deliver a suite of Continuous Managed Services that comprises of the entire IT stack that is Enterprise Class, Always Validated.
3.	FIRST to deliver continuous validation as a service to help life science organizations successfully meet their Cloud App Validation challenges.

My latest article on Forbes Technology Council.

The frontier QA organization of the agentic AI era won't look like the paper-heavy, headcount-intensive departments of the past. Instead, it will function as a human-led, AI-operated ecosystem. In this new paradigm, QA leaders will no longer be managers of people alone; they'll be the "organizers" overseeing an army of specialized AI agents.​

Leading AI/ML in Life Sciences for GxP Compliance

Open article: The Frontier QA Organization: Governing The Army Of Agents In Pharma by forbes.com

The Frontier QA Organization: Governing The Army Of Agents In Pharma

The "big deal" about DeepSeek V3.2 is that it breaks the linear relationship between intelligence and cost. It achieves GPT-5 class performance while drastically reducing the computational memory required to run it, primarily through DeepSeek Sparse Attention (DSA) and Multi-Head Latent Attention (MLA).

DeepSeek V3.2 is a major architectural evolution released in late 2025, effectively merging the efficiency of the DeepSeek-V3 base model with the reas

Open article: #031:  DeepSeek 3.2: Why we admire this model? by Nagesh Nama • 7 min read

#031:  DeepSeek 3.2: Why we admire this model?

Forbes Technology Council Thank you for amplifying this critical discussion. This article perfectly captures how the paradigm shift in QA isn't just about automation—it's about fundamentally rethinking how we ensure patient safety and regulatory compliance in the AI era.

The 'organizer-worker' protocol and the concept of human-led, AI-operated ecosystems really resonates. Especially in regulated environments like pharma, the move from 'doer' to 'verifier' represents a more strategic role for QA leaders, freeing them for high-value System thinking.

What I appreciate most is the emphasis on 'compliance by design' and the Annex 22 mandate—this isn't just innovation theater; it's grounded in the real regulatory requirements that govern our industry. The AsyncThink workflow comparison makes the benefits of this frontier model tangible and actionable.
https://lnkd.in/efYaRU5M

We've entered the era of the "frontier firm"—an organization where artificial intelligence (AI) isn't just a tool but the operating system.  https://hubs.ly/Q042nJ350 

Written by Nagesh Nama of xLM - Continuous Intelligence

Open article: The Frontier QA Organization: Governing The Army Of Agents In Pharma by hubs.ly

Given my work in GxP compliance and pharmaceutical manufacturing, EBMs could be profoundly relevant. The core value proposition — provably correct behavior rather than statistically likely behavior — maps directly to validation and regulatory requirements. When Logical Intelligence talks about sectors where "certification, liability, and auditability are prerequisites for deployment," that sounds a lot like the life sciences industry we serve.

1. The Basics: What Are Energy-Based Models? Think of EBMs through a physics analogy you'll find intuitive. In nature, physical systems naturally seek

Open article: #032: Energy Based Models: Why Life Sciences should be interested? by Nagesh Nama • 7 min read

#032: Energy Based Models: Why Life Sciences should be interested?

Bob Buhlmann - Thank you for your strategic, pragmatic and lively conversation last week.  https://lnkd.in/eQ6sxsnS

Open article: #094: GxP in the Age of AI: From AstraZeneca's Head of Digital Strategy (LinkedIn Live event) by xLM - Continuous Intelligence • 4 min read

#094: GxP in the Age of AI: From AstraZeneca's Head of Digital Strategy (LinkedIn Live event)

The headline is not harmonization.  The headline is this:

Risk management now drives inspections. Audit documentation is no longer shielded. AI lifecycle governance is under regulatory scrutiny.  https://lnkd.in/eS-bNe8Z

Open article: #097: FDA’s QMSR Is Here: What It Means for Med Device by xLM - Continuous Intelligence • 5 min read

#097: FDA’s QMSR Is Here: What It Means for Med Device

In practical terms for pharma and life sciences: a neuro-symbolic world model could simulate a manufacturing process deviation, predict downstream quality impact, and simultaneously verify whether the predicted state violates any 21 CFR Part 11, Annex 11, or USP <1058> rule — producing an audit-ready trace. A 2024 study showed neuro-symbolic systems outperformed traditional AI by over 42% in clinical trial patient cohort identification precisely because they combined LLM language understanding with domain-specific rule systems.

First, a Quick Disambiguation The term "Web World Model" actually spans two overlapping ideas you need to separate before going deeper: World Models b

Open article: #033: Web World Models: A Business-Focused 101 by Nagesh Nama • 6 min read

#033: Web World Models: A Business-Focused 101

The recent release of Google's agentic AI documentation marks a milestone in how the industry views autonomous systems. The document presents agents not merely as prompt-driven bots but as complete software systems capable of reasoning, acting, orchestrating tools, and operating within an agentic loop of “Think → Act → Observe” 

https://lnkd.in/eznjPzTr

Open article: #096: ContinuousOS in Sync with Google's Agentic AI Vision by xLM - Continuous Intelligence • 7 min read

#096: ContinuousOS in Sync with Google's Agentic AI Vision

The next competitive advantage in pharma will not be automation alone. It will be governed autonomy.  Firms adopting the Frontier QA model early will define the next decade of compliant digital transformation.  https://lnkd.in/eDqTuzHH

Open article: #095: The Frontier QA Organization: Governing a Swarm of Agents in GxP Pharma by xLM - Continuous Intelligence • 6 min read

#095: The Frontier QA Organization: Governing a Swarm of Agents in GxP Pharma

Tough Son or Stinson — A Tough Choice!

Saturday was a beautiful morning in the San Francisco Bay. Crisp skies, brisk wind, no humidity, and a shining sun ☀️ — a perfect day to be outside!
My younger son and I decided we had to soak it all up. So we headed to Stinson Beach, a few miles northwest of the Golden Gate Bridge. The 90-minute drive, with its winding roads and stunning vistas, was worth every minute. This was my first visit to Stinson Beach.

When we arrived, the sight of the beach nestled at the bottom of a steep cliff was breathtaking. I was torn — meander along the beach or take on the challenge of hiking up the mountain?

For my son (tough as a rock — mountain biker, fitness maniac, 22 years young), it was a no-brainer: trek miles up into the mountains. For me, my heart was pointing at the beach while my head said, be tough. A real tough choice!

I listened to my head and started the hike. Every step, every vista, every breath was fascinating. The views were postcard-perfect. I was literally floating on cloud nine.

Reflecting on it, there's a real leadership takeaway here. As I shape xLM in the fast-moving Agentic AI race, this moment reminded me: every great leader must be comfortable collaborating across generations. The younger generation brings one kind of brush. The more experienced generation brings another. The best paintings use both.

My son gave me a brush I'll remember forever — especially with a team whose average age is in the twenties. 🙏

View company: xLM - Continuous Intelligence

Discover how a new generation of U.S.-based API manufacturing is being built from the ground up.
​
Join xLM - Continuous Intelligence and John Raech, Co-Founder and President of Pharma Resource Group, as they unpack PRG’s state-of-the-art continuous flow and digital-first API facility and its path to the FDA’s Emerging Technology Program.
​
We’ll dive into real-world applications of advanced analytics, AI/ML-driven process control, and real-time quality intelligence—and what they mean for tech transfer, batch release, and regulatory confidence.
​
If you’re driving innovation in manufacturing, quality, digital transformation, or regulatory strategy, you won’t want to miss this conversation on the future of pharma production.

PRG's Road to FDA's Emerging Technology Program (ETP)

Artificial Intelligence is transforming pharmaceutical R&D from preclinical modeling to pharmacovigilance analytics. In regulated environments, innovation must not compromise compliance, data integrity, or patient safety.

Join us for an exclusive LinkedIn Live conversation with Larry Puderbach, Associate Vice-President in Research Quality at Merck & Co., exploring the realities of implementing AI within internal R&D ecosystems.

AI in Pharma R&D: An AI Conversation with Larry Puderbach, AVP, Merck 

Outstanding initiative, Kashyap Joshi. Tech Fortnight 2026 exemplifies the kind of systematic, cross-functional approach that transforms engineering culture from the inside out.

The breadth of topics—from SOC 2 & ISO 27001 implementation to Databricks foundations, security architecture, and LLMs for development—demonstrates a mature understanding that AI-first engineering requires deep integration across quality, security, and architecture layers. This isn't just training; it's organizational DNA building.

https://lnkd.in/eY7whvbm

Program Manager @ xLM | Transforming Life Sciences with AI & ML | Pioneer in GxP Continuous Validation |

🚀 xLM Tech Fortnight 2026
Engineering · Quality · Research · Innovation

At xLM - Continuous Intelligence, technology excellence is a discipline, not a milestone.

We recently concluded our first xLM Tech Fortnight 2026, a company-wide initiative aligning teams on designing, building, testing, securing, and scaling intelligent systems.

This annual initiative reflects our CEO Nagesh Nama's vision to build research-driven, AI-powered, quality-first engineering at scale.

Over two intensive weeks, we covered:
🔹 Engineering vision and product strategy
🔹 SOC 2 & ISO 27001 practical implementation
🔹 Architecture, APIs & integration standards
🔹 Databricks & Spark foundations
🔹 Design patterns & engineering rules
🔹 QA lifecycle (STLC) & unit testing best practices
🔹 LLMs for development
🔹 Salesbot architecture, security & testing
🔹 Git security & penetration testing
🔹 LinkedIn marketing best practices

This initiative integrated DEV, QA, platform, security, and business teams, reinforcing that quality, security, and architectural discipline are shared responsibilities.

Special thanks to our CEO Nagesh Nama for the vision behind xLM Tech Fortnight 2026.

We appreciate our COO Geetesh G. Talajia for organizing and driving this initiative to successful execution.

Your leadership shapes xLM into a research-led, AI-first engineering organization.

This is just the beginning. Tech Fortnight will become an annual platform to strengthen our engineering culture and execution standards.

#xLM #EngineeringExcellence #QualityFirst #AIEngineering #SoftwareArchitecture #ContinuousIntelligence

🚀 xLM Tech Fortnight 2026

Our CEO Nagesh Nama was featured on Microsoft Reactor's AI Agents vlog. 

*About the AI Agents Vlog* 
Everyone’s talking about AI Agents—but how are today’s founders actually building and leveraging them to shape what’s next?

This casual, insightful vlog series dives into the minds of visionary founders who are not just riding the AI wave—they’re steering it. Whether you're a developer, designer, strategist, or simply AI-curious, each episode unpacks real-world use cases, emerging trends, and bold ideas in the AI Agent space.

This series features founders from across the globe who are part of the Microsoft for Startups Founders Hub Program, where they’re building and scaling AI Agent innovations. 

https://lnkd.in/exEpPJ8a

Open video: AI Agents Vlog - Nagesh Nama - xLM Continuous Intelligence on youtu.be

AI Agents Vlog - Nagesh Nama - xLM Continuous Intelligence

The interpretability story is especially critical for regulated industries. The CRV Science analysis notes that BDH's thermodynamic framework could eventually enable mathematically provable safety bounds — a "Probably Approximately Correct" guarantee that the AI won't make a reasoning error beyond a defined probability. That kind of auditable AI is the prerequisite for FDA-grade autonomous decision-making.

BDH (Baby Dragon Hatchling) is a post-Transformer AI architecture built by the Parisian-Polish startup Pathway, designed to do something today's AI ca

Open article: #034: BDH — The Dragon Hatchling: A New Kind of AI Brain by Nagesh Nama • 6 min read

#034: BDH — The Dragon Hatchling: A New Kind of AI Brain

The frontier of pharma compliance isn’t a single AI model or a clever chatbot. It’s a new operating system for validation—built on information that is unified, governed, and ready for intelligent execution. The companies that build this foundation now will define how the industry operates in the next decade. Those that wait will be playing catch-up with their competitors and their regulators.
https://lnkd.in/e-8rRAf2

Pharma talks a lot about AI, but very few teams are actually ready to trust AI with validation and compliance work yet—and the gap has nothing to do with models or chatbots. In his latest Fast Company article, our CEO Nagesh Nama explains why the real unlock for AI-powered pharma compliance is a unified, governed content foundation that connects all the scattered systems and documents teams rely on today.

He breaks down how a “central nervous system” for compliance data can transform validation from manual document hunting into consistent, inspection-ready decision workflows—setting the stage for AI agents that are not just fast, but fully defensible in front of regulators.

If you’re leading digital, quality, or IT in life sciences and wondering where to start with AI, this perspective is a must-read.

https://lnkd.in/eW-aCQsK

Nagesh Nama is a member of the Fast Company Executive Board.

Open article: The real first step to AI-powered pharma compliance by fastcompany.com

The real first step to AI-powered pharma compliance

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The Battle

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The Battle

Armand Ruiz

Nagesh Nama

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Posts

Engagements

Likes

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);}Armand Ruiz's LinkedIn Performance

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Performance ScoreArmand Ruiz's LinkedIn Performance