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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"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)

Senior Program Manager | Change Manager | AI GTM

Senior Program Manager in Operations and Strategy with 15+ years turning messy, high-stakes problems into structured, scalable systems. I’ve helped drive more than $100M in revenue growth, built platforms used across continents, and aligned teams from APAC to EMEA who normally wouldn’t be caught in the same meeting room.

I specialise in the intersection of operations, data, and behaviour; where real transformation actually happens.

What Drives Me

P&L ownership and finding hidden margin others miss
Turning legacy operations into modern digital ecosystems
Go-to-Market strategy that accelerates revenue, not reporting cycles
Leading cross-functional teams through change they actually adopt
Using AI and data to remove friction and speed up decisions
Building growth loops that compound, not campaigns that fade

Let's Connect!

Houman Asefi

For 200 years, economic power followed intelligence.

The smartest people built the most valuable organizations.

Consultants sold knowledge.

Engineers built systems.

Analysts interpreted data.

AGI breaks this historical pattern.

Because when intelligence becomes abundant, it stops being the primary source of power.

Power doesn’t disappear. It relocates.

It moves to what surrounds intelligence:

infrastructure, distribution, coordination, and legitimacy.

This shift will define which companies dominate, which institutions remain stable, and how economic value concentrates in the decades ahead.

I wrote a deep dive on this transformation:

 👉 https://lnkd.in/gP9m4gcG

Senior Program Manager | Data, AI & Transformation | Change | Enterprise Analytics & Delivery Specialist

Unpopular opinion:
 
Models will become commodities faster than anyone expects.

Open-weights are improving.
 
Training costs are dropping.
 
Talent is diffusing globally.

The AGI power stack looks like this:

• At the bottom → Compute & infrastructure
 Energy, chips, data centers. This is where hard power lives.

• Above that → Optimization & training systems
 The invisible engineering layer that determines efficiency and cost 
dominance.

• Then → General intelligence architectures
 Yes, this matters — but it won’t stay scarce.

• Then → Alignment & control
 Who defines the goals of AGI will shape economies.

• At the top → Applications & distribution
 The layer that captures the majority of economic value.

AI is all about who controls the stack from electrons to distribution.

This is why the winners in the AGI era won’t just be AI labs.

They’ll be:

Energy providers
Chip manufacturers
Cloud platforms
Distribution monopolies

AGI is not a software story.

It’s an industrial power story.

And people are still arguing about prompts.

In the next 60 seconds I’m going to show you the Math AI landscape.

Math superintelligence is the decision-making infrastructure of future economies.

NOT a tool for mathematicians.

Here's the power map; and why less than 10 actors globally control this race:

The 3 camps building it:

↳ Formal Proof AGI (Axiom, Google DeepMind); not doing math. Building flawless reasoning engines for markets, logistics, and strategy


↳ Quantitative Optimization AGI (quant funds, Palantir Technologies, NVIDIA); the most secretive. Where trillions actually sit


↳ Engineering-Reality AGI (Tesla, NVIDIA Omniverse, defense AI); math AGI as civilization-level infrastructure


Where the money lands:

Finance → the Bloomberg Terminal for reasoning

Mining & Energy → extraction and grid optimization at scale

Government → the sleeper market nobody is pitching yet



Renaissance Technologies already proved this model works.

They didn't build a hedge fund.

They built a math AGI 30 years early

and extracted billions from markets that didn't understand what hit them.

The next version won't just trade markets.

It will simulate economies, model climate risk, and run national resource allocation.

Governments that don't own this layer will rent it from companies that do.

Math AGI is not tech.

It's sovereignty.

The race has fewer than 10 serious actors.

The window to engage is still open.

Not for long.

AGI could create the biggest wealth inequality event in history.

Here's why:

For 200 years, you could climb the ladder through intelligence.

Study hard → Get into a good school → Work your way up → Build wealth.

If you were smart and worked hard, you could join the elite.

Now intelligence is becoming free.

Everyone has access to genius-level thinking. 

AGI is the great equalizer, right?

WRONG.

You can't hard-work your way into owning a data pipeline. 

You can't study your way into controlling compute infrastructure.

The old path was: effort → intelligence → power

The new path is: infrastructure → power (intelligence is free)

So what happens?

The people who already own the rails:

compute
data
distribution

they win everything.

Everyone else gets the same commodity: infinite intelligence with nowhere to deploy it.

We're about to find out if AGI democratizes wealth...

Or concentrates it like never before.

One thing I learned working on analytics and AI-driven operational programs at Cisco:

AI transformation doesn’t fail because the models don’t work.

It fails because the organisation isn’t ready for how AI changes visibility and decision power.

In one initiative, we built a solution that could surface operational risks almost in real time.

Technically, it was a success.

But adoption stalled at first; not due to data or tooling, but because the insights suddenly made performance gaps transparent across teams.

Once leadership aligned ownership, clarified governance, and embedded the outputs into workflows, adoption accelerated quickly.

That experience taught me something I still see today:

AI doesn’t just automate processes.

 It reshapes how decisions are made; and that’s where the real transformation challenge lies.

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