Category: AI

The Hidden Trap Destroying IoT Platforms: 3 Silent Mistakes Founders Don’t See Until It’s Too Late

Many IoT platforms began their journey with strong foundations. They had capable engineering teams, promising technology, and even early customer traction. In the early stages, everything appeared to be moving in the right direction.

Yet over time, many of these platforms quietly stalled. Some remained small niche products. Others slowly faded from the market.

The collapse rarely happened suddenly. It emerged gradually, almost invisibly.

From observing the evolution of many IoT platforms over the years, three recurring patterns often appear. These are what I refer to as the three silent killers of IoT platforms.

1. The “Nice Platform” Problem

The first and most common challenge is what I call the “Nice Platform” problem.

Technically, everything works as expected. Sensors transmit data. Dashboards display attractive charts. Connectivity is stable. Demonstrations during presentations look impressive.

Customers often respond with comments like, “This is very interesting.”

But the real question is much deeper than whether the technology works.

Is the platform essential to the customer’s operations?

Many IoT platforms unintentionally position themselves as helpful tools rather than critical systems. They focus heavily on features such as:

• dashboards
• device connectivity
• data visualisation

These capabilities are useful. They demonstrate the power of connected systems.

But organisations rarely allocate long-term budgets for visualisation tools alone.

Businesses invest in solutions that directly influence outcomes. What they are truly paying for are measurable results such as:

• reducing equipment downtime
• preventing operational accidents
• lowering energy consumption
• avoiding regulatory penalties
• increasing workforce productivity

When a platform is tightly connected to these outcomes, it becomes embedded in the customer’s daily operations. It becomes part of their operational backbone.

But when the platform only provides visibility without directly influencing decisions or actions, it remains optional.

And optional systems are the first to disappear when budgets tighten.

This explains why the most successful IoT deployments focus on mission-critical problems. Examples include:

• predictive maintenance in industrial environments
• fleet safety monitoring for logistics operations
• cold chain compliance for pharmaceutical distribution
• energy optimisation for large buildings

These systems cannot simply be turned off without significant operational consequences.

That is the difference between interesting technology and essential infrastructure.

2. The Customisation Trap

The second silent killer appears much later in the journey, often after the platform begins acquiring its first paying customers.

Early adopters frequently request modifications. They ask for specific dashboards, specialised workflows, or integrations with legacy enterprise systems.

At the beginning, these requests appear reasonable.

A startup needs revenue. The team wants to satisfy its customers. Agreeing to customise the platform seems like a practical decision.

However, a hidden risk gradually emerges.

Over time, the platform begins to fragment.

Instead of maintaining a single scalable product, the engineering team finds itself supporting multiple customer-specific versions:

• one version tailored for customer A
• another variation for customer B
• a different configuration for customer C

The product gradually shifts from a platform to a collection of bespoke solutions.

Engineering resources originally intended to improve the core platform are redirected to meet project-specific requirements.

At this stage, the business begins to resemble a consulting company rather than a product company.

The consequences are predictable:

• development cycles slow down
• engineering teams become stretched
• product direction becomes unclear
• operating margins shrink

Scaling becomes increasingly difficult because each new customer introduces new complexity.

Many IoT startups unintentionally move into this trap. They begin with a platform vision but gradually become project delivery organisations.

The strongest platform companies remain disciplined about this boundary.

They continuously ask a simple but critical question:

Is this a reusable product feature or a one-off project request?

If the capability cannot benefit many customers across different industries, it may not belong in the core platform.

Maintaining this discipline is difficult in the early stages when revenue pressure is high. Yet it is often the difference between building a scalable platform and building a services business.

3. The Ecosystem Illusion

The third silent killer relates to ecosystem development.

Many platform founders assume that once the platform is launched, developers and partners will naturally begin building solutions on top of it.

The belief is simple: build the platform first, and the ecosystem will follow.

In practice, ecosystems rarely grow automatically.

Developers and partners choose platforms based on several practical considerations:

• the size and activity of the ecosystem
• the availability of development tools and documentation
• the potential economic opportunity

The economic factor is frequently underestimated.

Developers invest their time where they can build sustainable businesses. If there is no clear revenue path, most will quickly move to other platforms.

This is one of the key reasons large ecosystems expanded rapidly. Platforms such as:

Amazon Web Services
Shopify
Salesforce
Apple

created strong developer communities by building clear economic incentives.

Developers could launch products, attract customers, and generate revenue through these platforms.

In many IoT platforms, the ecosystem layer is incomplete. APIs and SDKs are available, but the economic model is unclear.

For an ecosystem to grow meaningfully, partners must clearly understand:

• how they can generate revenue
• how easy it is to build solutions on the platform
• how large the addressable market is

Without these signals, the ecosystem remains limited.

Developers may experiment with the platform, but long-term commitment rarely materialises.

Why These Killers Are Difficult to Detect

One of the most dangerous aspects of these challenges is their subtle nature.

None of them produces immediate crises.

The company may still:

• secure new pilot projects
• receive industry recognition
• release new product features
• attract positive feedback from users

From the outside, everything appears healthy.

But internally, warning signs slowly emerge. Growth begins to plateau. Profit margins tighten. The product roadmap becomes fragmented.

Eventually, the platform struggles to reach the scale necessary to compete globally.

This pattern explains why many IoT platforms remain respectable but small companies rather than evolving into global infrastructure providers.

The difference between the two often lies not in technological capability but in strategic discipline.

For IoT platforms to achieve long term impact, they must move beyond attractive dashboards and connectivity features. They must anchor themselves in mission-critical outcomes, protect the integrity of their core product, and build ecosystems where partners can thrive economically.

Only then can a platform move from being an interesting technology to becoming part of the digital infrastructure that organisations truly depend on.

These lessons continue to shape how many leaders in connected systems approach platform strategy today, especially as IoT, AI, and edge computing converge to redefine how digital infrastructure is built and secured.

The Day I Realised I Was Becoming a Human FAQ

There was a time when I actually felt proud every time someone asked me about Favoriot.

Each question felt like a small victory.

It meant people were noticing.
It meant the story was spreading.
It meant our work was reaching somewhere beyond our small team.

Someone would message me.

“What exactly is Favoriot?”
“Is it just a dashboard?”
“Can it connect to this device?”
“How is it different from AWS IoT or ThingsBoard?”
“Do you support AI?”
“Can students use it?”
“Is it for smart cities, or factories, or farms?”

And every time, I replied.

Patiently.

Sometimes through WhatsApp.
Sometimes through LinkedIn messages.
Sometimes through emails that arrived late at night.

Okay, Mazlan… this is good, I told myself.
It means people are interested.

So I kept answering.

Again.

And again.

And again.

Until one day, after probably the hundredth explanation, I suddenly paused.

I stared at my laptop.

Then I asked myself a question that hit me harder than I expected.

Wait… am I building a technology platform… or am I becoming a human FAQ?

That was the moment something clicked.

A small but powerful realisation.

This was not really about repeating answers.

It was about something deeper.

Energy.

Focus.

And scale.

Because if the story of Favoriot only lives inside my head, then every explanation will depend on me personally.

And that is not scalable.

That night, I realised something important.

When people keep asking the same question, it is not a problem.

It is a signal.

A signal that your story is not documented clearly enough.

A signal that your knowledge is trapped inside conversations.

A signal that your platform needs a voice that can speak even when you are asleep.

That was my first aha moment.

The Emotional Side of Repeating Yourself

Let me be honest.

There were moments when I felt tired.

Not angry.

Not irritated.

Just mentally drained.

Imagine explaining the same thing dozens of times.

Sometimes, even after giving a talk or presentation.

For example, after speaking at events like The Star Cybersecurity Summit, where I was invited to share thoughts about IoT systems, AI, and the future of connected technologies, people would still approach me afterwards and ask the exact same question.

“So… what exactly does Favoriot do?”

Part of me almost laughed.

Did I not just explain that on stage for half an hour?

Then another voice in my head replied.

Relax Mazlan. Every audience is new.

Every listener hears things differently.

Every person arrives with a different level of understanding.

Some are engineers.

Some are students.

Some are policymakers.

Some are just curious.

And none of them is wrong for asking.

That was my second realisation.

Repetition is not the enemy.

Confusion is.

If people keep asking the same question, it simply means the explanation has not reached them in a form they can digest.

And that responsibility sits on my shoulders.

The Turning Point

One evening, while replying to yet another email asking the familiar question, I suddenly stopped typing.

I leaned back in my chair.

Why am I answering this privately again?

Then another thought appeared.

Why not answer it publicly once… and let it help hundreds of people instead of one?

That thought changed everything.

Instead of seeing repeated questions as interruptions, I began seeing them as content ideas.

Every repeated question was actually a signal about what people wanted to understand.

If five people ask the same thing, it deserves an article.

If ten people misunderstand a feature, it deserves a tutorial.

If customers keep comparing Favoriot with other platforms, it deserves a structured explanation.

That was the moment I started writing more seriously on IoT World.

Not random thoughts.

Not marketing slogans.

But clear explanations.

What exactly is the Favoriot Insight Framework?

How Favoriot moves from raw data to meaningful decisions.

Why IoT is not just about dashboards.

How universities can build AIoT labs.

Why local councils struggle with smart city projects.

How system integrators can deploy IoT faster.

Each question became an article.

Each doubt became a story.

Each confusion became clarity.

And slowly, something magical happened.

Instead of repeating myself endlessly, I started sending links.

“You might want to read this article.”
“This explains the architecture clearly.”
“This post shows the use case.”

The conversation immediately became deeper.

People no longer start from zero.

They started with understanding.

Building Something Bigger Than Myself

But something else happened, too.

After publishing several articles, people began asking another question.

“Is there one place where we can read everything about Favoriot?”

I smiled when I heard that.

Alright, Mazlan… now the next step is obvious.

That was when the idea of a Favoriot Resources Page was born.

Not a marketing page.

Not a product brochure.

But a knowledge hub.

A place where people can explore the ecosystem properly.

A place where they can learn at their own pace.

On that page, anyone can now explore:

What Favoriot really is
Tutorials and technical guides
Real IoT project challenges
Case studies and architecture explanations
The Favoriot Insight Framework
AI and IoT integration concepts
Videos and learning materials

I wanted it to feel like a digital campus.

Because Favoriot is not just software.

It is an ecosystem.

And ecosystems require structure.

They require stories.

They require documentation.

Without those elements, people only see fragments.

With them, people see the full picture.

The Hidden Lesson for Founders

Many startups face this same challenge.

We assume people understand our product.

We assume our website is clear.

We assume our explanation is good enough.

Most of the time, it is not.

People are busy.

They skim.

They scan.

They make assumptions.

And sometimes those assumptions are completely wrong.

So when people keep asking the same question, the worst reaction is frustration.

The better reaction is curiosity.

Ask yourself:

Why is this still confusing?

Which part of my explanation is missing?

How can I make this easier to understand?

Repeated questions are feedback.

Free feedback.

Valuable feedback.

And if you listen carefully, they tell you exactly what your audience needs.

When the Story Finally Clicked

After writing consistently and building the Resource page, I noticed something interesting.

My explanations became sharper.

Writing forces you to think clearly.

When you write publicly, your ideas become structured.

And suddenly the narrative becomes easier to communicate.

People begin to see the bigger picture.

They understand that Favoriot is not just a tool.

It is a framework.

It is an ecosystem.

It is a learning platform.

It is an AIoT foundation.

Without structure, that sounds confusing.

With structure, it becomes powerful.

The Resource page helped me connect the dots.

From devices to cloud ingestion.

From data streams to analytics.

From rule engines to AI insights.

From dashboards to decision intelligence.

That clarity changed everything.

The Unexpected Reward

Today, people still ask questions.

Of course they do.

And I welcome them.

But the feeling is different now.

Instead of feeling drained, I feel grateful.

Because each question tells me that someone is curious.

Someone is exploring.

Someone wants to understand.

And now I have something meaningful to share.

Not just an answer.

A pathway.

When someone tells me,

“I read your Resource page, and now I understand what Favoriot is.”

That feels incredibly satisfying.

More satisfying than closing a sale.

Because understanding builds trust.

Trust builds relationships.

And relationships build ecosystems.

The Aha Moment

Looking back, I now see that the repeated questions were never the problem.

They were actually guiding me.

They were telling me exactly what needed to be documented.

Exactly what was needed was clarity.

Exactly what was needed was storytelling.

And once I finally organised all that knowledge into structured content, something powerful happened.

The pressure disappeared.

The message became scalable.

And the story of Favoriot could travel further than my own voice.

That was my real aha moment.

When people stop depending on your explanation and start learning from your ideas, you know something meaningful has been built.

Favoriot is not just about connecting devices.

It is about connecting understanding.

And that journey started with a simple realisation.

Sometimes, the most annoying repeated questions are actually the best teachers.

Now I am curious.

Have you ever experienced the same situation in your own journey?

People asking the same question again and again?

What did you do about it?

Did it frustrate you, or did it push you to build something better?

FAVORIOT Resources

I Started a Startup at 56. This Is What the Journey Really Taught Me.

Techtamu Talk | 17 January 2026

On 17 January 2026, at around 10 in the morning, I stood before a room full of students, founders, and curious minds.

Before I spoke, I paused for a second.

“How do I explain a journey that never followed a straight line?”

Entrepreneurship, at least in my life, was never a planned destination. It was a series of connected experiences that only made sense much later.

That lecture was not about IoT.
It was not about startups.
It was about life, timing, courage, and knowing when to let go.

You Only Understand the Journey When You Look Back

I opened the session with a quote from Steve Jobs that has stayed with me for years:

You can’t connect the dots looking forward. You can only connect them looking backward.

That sentence explains my life better than any resume ever could.

When you are young, you worry too much about choosing the “right” path. The right course. The right job. The right company.

What nobody tells you is this.
Every experience counts, even the ones that feel like detours.

You just won’t see it yet.

From a Curious Child to a Technology Lifelong Learner

My interest in technology did not start in a lab or a classroom.

It started at home.

My late father was a clerk. But in the evenings, he repaired televisions and radios. I would sit beside him, watching circuits come back to life.

“So this is how things work.”

Then came science fiction.

Cartoons like The Jetsons showed a future that felt impossible at the time. Video calls. Smart watches. Flying machines.

Today, many of those ideas sit quietly in our pockets.

That early exposure planted a question in my mind that never left me.

“What if we could actually build these things?”

Living in Four Different Worlds

I consider myself fortunate. Few people get to experience all four.

Academia.
Corporate.
Government.
Startup.

I began as a lecturer at Universiti Teknologi Malaysia, immersed in theory and research. Later, I joined the corporate world at Celcom, where reality hits hard and fast. Customers matter. Deadlines matter. Revenue matters.

At MIMOS, I worked on national-scale research, including wireless sensor networks, long before the term IoT became popular.

Then came REDtone, where I helped build IoT initiatives inside a corporate structure.

Each world taught me something different.

But they also gave me baggage.

Experience gives confidence.
It also gives fear.

Young founders often believe everything is possible.
Older founders carry doubt.

“What if this fails?”
“What if I lose my savings?”

That voice gets louder with age.

Silicon Valley Changed Everything

At 56, I joined an immersion trip to Silicon Valley.

That trip changed my identity.

I walked into Plug and Play Accelerator and saw cubicles, whiteboards, and founders who looked just like us. That was where companies like Dropbox began.

I remember thinking:

“If this guy can do it, why can’t we?”

That was the moment I stopped seeing myself as a CEO-in-waiting.

I started seeing myself as an entrepreneur.

Not someday.
Not after retirement.
Now.

Starting Late Comes With a Price

I started my startup using personal savings. No incubator. No startup playbook. No fancy terms like ‘MVP’ or ‘pitching decks’.

Just belief and experience.

Our first idea was a smartwatch for the elderly with fall detection and emergency alerts. It looked noble. It sounded meaningful.

It failed.

The market was too small.
Children did not want to pay.
The device did not suit care homes.

That was my first real startup lesson.

Good intentions do not build businesses.
Paying customers do.

Learning the Art of the Pivot

In the startup world, pivoting is survival.

We repurposed the watch for Hajj and Umrah pilgrims. New market. Same core idea.

New problems appeared.

Unrealistic pricing expectations.
Battery life demands that defy physics.
Hardware sourcing from China.
Network roaming issues.
Travel agencies are unwilling to add cost.

Then came COVID-19. We proposed quarantine monitoring. It went nowhere.

Eventually, I made one of the hardest decisions of my life.

Ending a product.

I shared this honestly during the lecture.

Ending a product feels like ending a child you raised with love.
But holding on too long can kill the company.

A CEO must choose growth over attachment.

When More Products Mean Less Identity

We built other solutions too.

A civic complaint app sounded promising. Until each client wanted heavy customization and complaint volumes exploded beyond what they could manage.

A consumer tracking app failed because people care deeply about privacy and free alternatives already exist.

At some point, I realized something painful.

When you build too many products, people no longer know who you are.

Neither do you.

The Shift That Saved the Company

That realization led to our biggest change.

We stopped building products for users.

We started building a platform for builders.

That platform became Favoriot.

An IoT platform that lets others connect devices, visualize data, and deploy solutions quickly. Over time, intelligence was added so data could speak, not just sit on dashboards.

This shift reduced risk.

Instead of betting on one product, we enabled hundreds of use cases.

Why One Revenue Stream Is Never Enough

Another hard truth I shared with the audience.

Pure SaaS subscriptions rarely pay the bills in emerging markets.

We survived by building multiple streams.

Enterprise licensing.
Project-based solutions.
Training and certification with universities.

The platform stayed at the core. Everything else wrapped around it.

That balance kept the company alive.

Partners Build What You Cannot

No startup wins alone.

We built a partner ecosystem covering hardware, software, AI, and system integration. Today, that network spans multiple countries.

Each partner brings strength we do not have.

That is how scale really happens.

Marketing Without Big Budgets

We never had large marketing budgets.

So we wrote.
We shared.
We taught.

Blogs.
Social media.
Free e-books.

Inbound marketing works when your story is honest and your knowledge is real.

People do not buy immediately.
But they remember.

The Lesson I Hope You Carry Forward

I ended the lecture with a simple reminder.

Whatever path you take, it is building something inside you. Even when it feels random.

Do not fall in love with your product. Fall in love with solving problems.
Do not trust praise until someone pays.
Do not depend on one revenue stream.
Do not fear pivoting. Fear standing still.

And most of all, do not believe it is too late.

I started my startup at 56.

If I could begin then, what is stopping you now?

I would love to hear your thoughts.
What dots in your life are starting to connect? Share them in the comments.

When Writing Free eBooks Still Feels Like Shouting Into the Void

I did not expect this feeling to arrive so quietly.

No dramatic moment.
No emotional breakdown.
Just a soft question that kept returning while I stared at my screen.

Should I stop writing eBooks about IoT, startups, and entrepreneurship?

I have written several eBooks over the years. Some came from years of experience building platforms. Some from scars earned while running a startup. Some from observing founders struggle with the same blind spots again and again.

I made them free.
No paywall.
No upsell tricks.
Just knowledge, stories, and lessons shared openly.

Yet after my last three books (Hello IoT, The Favoriot Way: A Life Built on Curiosity and Courage, Favoriot : The Journey of an IoT Startup), something felt off.

Downloads slowed.
Shares dropped.
The quiet became louder.

At first, I blamed myself.

Maybe the topics are stale.
Maybe I am repeating myself.
Maybe people are tired of hearing from me.

Then another thought crept in.

Or maybe the world has changed.

The Moment I Could No Longer Ignore

I noticed something about my own habits before blaming anyone else.

I no longer Google as much.
I open ChatGPT.
I type a question.
I get an answer.

Direct.
Fast.
Clean.

And here is the uncomfortable truth.

I am guilty too.

I ask AI to summarise books.
I ask for key takeaways.
I skim instead of sitting with pages.

Who am I to complain when I do the same thing?

That realisation stung.

Because I used to love reading slowly. Highlighting sentences. Rereading paragraphs. Letting ideas sit for days.

Now, time feels compressed. Attention feels borrowed. Everything competes for mental space.

The Silent Shift No One Talks About

This is not about AI replacing writers.

It is about AI changing readers.

People no longer want to search.
They want answers.

They no longer want ten blog posts.
They want one response.

They no longer want to explore.
They want to arrive.

Why buy a book when a prompt gives you a clean summary?

Why spend hours reading when minutes feel enough?

That question hurts writers, but it is not wrong.

Books were once a journey.
Now they are treated like databases.

Tell me what matters. Skip the rest.

Short Attention Is Not a Moral Failure

I hear people complain about attention spans all the time.

But I do not think it is laziness.
I think it is survival.

We are flooded with inputs. Messages. Alerts. Updates. Noise.

Reading a 150-page eBook feels heavy when your mind is already full.

The new generation did not lose patience.
They adapted to overload.

They want clarity, not volume.
Direction, not depth.

At least not by default.

When Free Still Feels Expensive

Making my eBooks free was supposed to remove friction.

Yet free does not mean easy.

Reading still costs time.
Thinking still costs energy.

AI removed that cost.

One prompt feels cheaper than one chapter.

So why am I surprised?

The Hard Question I Keep Avoiding

I keep asking myself something uncomfortable.

Am I writing for impact, or am I writing out of habit?

In the past, writing eBooks felt like leaving a trail behind. Something lasting. Something searchable. Something meaningful.

Now it feels like throwing paper planes into a sky full of drones.

They fly faster.
They reach further.
They respond instantly.

Paper planes still matter.
But fewer people look up.

Books Versus Conversations

AI feels like a conversation.

Books feel like a lecture.

That difference matters.

People want interaction. They want follow-up questions. They want context tailored to their situation.

A book cannot ask back.

AI can.

And that changes expectations.

What Writing Used to Give Me

I did not write eBooks just for readers.

I wrote to think.

Writing forced clarity.
It slowed my thoughts.
It made experiences visible.

If I stop writing books, what replaces that?

Blogs?
Short posts?
Conversations?
Voice notes?

I do not know yet.

That uncertainty is unsettling.

Maybe Books Are No Longer the First Door

Here is a thought I am still wrestling with.

Books may no longer be entry points.
They may become reference points.

Not where people start, but where they return when they want depth.

AI gives direction.
Books give texture.

AI answers questions.
Books explain why the questions matter.

But fewer people reach that stage.

The Ego Check I Needed

Another truth I had to face.

I assumed free meant valuable.
I assumed experience meant relevance.

Neither guarantees attention.

The world does not owe writers readers.

Attention is earned every day.

Even by those who have written before.

Am I Really Stopping?

When I say I feel like stopping, I am not quitting writing.

I am questioning the format.

Maybe eBooks are not where my thoughts want to live anymore.

Maybe ideas want to breathe in smaller spaces.
Or in stories.
Or in conversations.

Or maybe fewer books, written slower, with deeper intent.

I am not sure yet.

What I Do Know

AI has changed how we read.
AI has changed why we read.
AI has changed when we read.

That shift is real. It is not a phase.

Fighting it feels pointless.

Understanding it feels necessary.

The Choice In Front of Me

I can keep writing eBooks and accept fewer readers.

I can stop writing books and find new ways to share ideas.

Or I can redefine what a book means in a world that no longer reads the same way.

Right now, I am sitting with the discomfort.

No dramatic announcement.
No final decision.

Just honesty.

A Quiet Ending With an Open Question

I still believe ideas matter.
I still believe stories shape thinking.
I still believe writing is worth doing.

But I no longer believe format guarantees relevance.

Maybe the real question is not whether I stop writing eBooks.

Maybe it is whether I am brave enough to write differently.

If you are a writer, a reader, or someone who quietly stopped reading books, I would love to hear your thoughts.

Have you felt this shift too?

Favoriot: AI Agents Not Needed Now

Do Favoriot need to develop an AI Agent feature?

Short answer? No, Favoriot does not need full AI Agent automation right now.

And yes, what you have today is more than enough for the market you are serving.

Let me explain this the way I usually reason with myself.

I asked myself this quietly

“Do customers really want systems that act on their own…

or do they want systems they can trust?”

When I sit with city operators, facility managers, engineers, or even researchers, one thing keeps coming up.

They are not asking for autonomy.

They are asking for clarity.

They want fewer surprises.

They want earlier signals.

They want confidence before taking action.

That matters.

What Favoriot already does well

Right now, Favoriot Intelligence does something very important and very rare.

It learns patterns from real operational data

It surfaces what looks unusual

It feeds those insights into a Rule Engine

And then… it stops

That stopping point is not a weakness.

It is a design choice.

The system says,

“Here is what changed.

Here is why it matters.

You decide what to do next.”

That is precisely where trust is built.

Rule Engine + ML is not a compromise

Some people frame this as:

“Rule Engine now, AI Agents later.”

I don’t see it that way.

I see it as:

ML decides what deserves attention

Rules decide what action is allowed

This separation is powerful.

Why?

Because rules are:

  • Auditable
  • Explainable
  • Governable
  • Aligned with SOPs and regulations

And ML is:

  • Adaptive
  • Pattern-driven
  • Good at spotting drift and anomalies

Together, they form a human-in-the-loop intelligence system, not a black box.

That is exactly what enterprises and public sector teams are comfortable with today.

Do customers actually want AI Agents?

Here’s the uncomfortable truth.

Most organisations say they want AI to “automate everything”.

But when you ask one more question…

“Are you okay if the system shuts down equipment on its own?”

“Are you okay if it triggers evacuation automatically?”

“Are you okay if it changes operating parameters without approval?”

The room goes quiet.

What they really want is:

  • Earlier warnings
  • Better recommendations
  • Fewer false alarms
  • Less manual rule tuning

Favoriot Intelligence already delivers that.

Where AI Agents actually make sense later

I’m not against AI Agents. Not at all.

But their place is conditional, not universal.

AI Agents make sense when:

  • Policies are mature
  • Actions are reversible
  • Risk is low
  • Trust has been earned over time

For example:

  • Automated report generation
  • Recommendation ranking
  • Suggesting rule adjustments
  • Proposing actions for approval

Notice the word: suggesting, not executing.

That is a natural evolution path.

Not a starting point.

Strategically, Favoriot is in the right place.

By keeping:

  • ML for learning and insight
  • Rules for control and action

Favoriot positions itself as:

  • Reliable
  • Safe
  • Deployable today
  • Acceptable to conservative sectors

Smart cities.

Utilities.

Campuses.

Critical infrastructure.

These sectors do not reward “full autonomy” first.

They reward predictability and confidence.

My honest conclusion

If I had to answer this as simply as possible:

Favoriot does not need AI Agents to be valuable.

Favoriot Intelligence with ML-driven rules is already the right solution for today.

AI Agents can come later, carefully, selectively, and with guardrails.

Right now, Favoriot is doing something more important than automation.

It is helping people think earlier, not react later.

And that, in my book, is real intelligence.

Small Beginnings. Big Futures.

Some dreams look small at the start.
Almost invisible. Almost forgettable.
Just like seeds sitting quietly in the soil.

But silence never means failure.
It means preparation.
It means something is gathering strength out of sight.

Your dream isn’t late.
Your moment just hasn’t arrived yet.

Keep planting.
Keep watering.
Keep showing up.

When the season is right, the world will finally see what you’ve been building in the dark.

Your time will come.

Leading LLMs of August 2025: Who’s Winning the AI Race?

If AI progress felt like a sprint in 2023, by 2025, it looks more like a rocket launch. Models aren’t just improving year by year—they’re leaping ahead month by month. What we thought was “cutting edge” last quarter is already yesterday’s news.

Here’s the reality: the global LLM market is surging toward $105.5 billion in North America by 2030. That’s not a forecast—it’s a signal. AI is no longer a novelty; it’s infrastructure.

But with so many options, which models actually matter right now? Which ones are shaping the way businesses, developers, and researchers use AI today?

I’ve rounded up the 10 large language models making the most significant impact in August 2025. Each one has its own unique personality, strengths, and trade-offs.

1. OpenAI – GPT-5

ChatGPT 5 is the next step in OpenAI’s journey, moving beyond GPT-4.5’s strengths to deliver a model that feels sharper, more adaptive, and more transparent in its reasoning. Where GPT-4.5 leaned heavily on pattern recognition, ChatGPT 5 combines that fluency with stronger deliberate reasoning, giving it the ability to break down problems with more structure and clarity.

It is also built to integrate more smoothly into real workflows. From handling long-form context with greater accuracy to providing clearer explanations of its answers, ChatGPT 5 is less about simply generating text and more about acting as a reliable partner. The model handles multimodal input—text, images, audio, and video—with greater fluidity, making it useful across industries from education to enterprise automation.

Like its predecessor, ChatGPT 5 remains proprietary, available through subscriptions or enterprise licensing. But for teams that want both conversational polish and deeper reasoning ability in one package, ChatGPT 5 has quickly become the new reference point.

2. DeepSeek – The Open-Source Challenger

China’s DeepSeek R1 took the AI world by storm with 671B parameters in a Mixture-of-Experts setup. By May 2025, their DeepSeek-V3 was leading the open-source leaderboard, proving that open models can compete head-to-head with proprietary giants.

The magic? 30 times cheaper than OpenAI’s o1 and 5 times faster. It thrives in reasoning-heavy tasks like math, coding, and scientific simulations. And with RAG integration, enterprises can plug it into sensitive datasets while maintaining control.

If you want open-source power with enterprise-level results, DeepSeek is redefining the game.

3. Qwen – Alibaba’s Efficiency Master

Alibaba’s Qwen 3 family is quietly powering industries across Asia. Their standout, QwQ-32B, rivals GPT-4o and DeepSeek in reasoning and coding while requiring far less compute.

With 32K context windows, Apache 2.0 licensing, and a parameter range from 1.8B to 72B, Qwen has become one of the most accessible and widely adopted LLM ecosystems. Already, over 90,000 businesses use it for gaming, consumer electronics, and enterprise workflows.

Qwen proves you don’t need hyperscale resources to compete at the highest level.

4. Grok – Elon Musk’s Conversational Rebel

Built by xAI and integrated into the X platform, Grok 3 feels different. It’s witty, fast, and plugged into real-time information.

With Think, Big Brain, and DeepSearch modes, it breaks down problems and pulls fresh data directly from the web and social feeds. Trained with 10x the compute of Grok 2, it’s designed for speed and trend awareness.

If your world demands live analysis, news tracking, or instant customer interaction, Grok brings something truly unique.

5. Llama – Meta’s Open-Weight Titan

Meta’s Llama 4 arrived in April with two flagship versions: Scout and Maverick. Both are natively multimodal, handling text, images, and short video, and they boast 256K token context windows.

The openness of Llama remains its secret weapon. Businesses and researchers can run it on their own terms, tune it to specific workflows, and avoid vendor lock-in.

If freedom and flexibility matter most, Llama is the open-source heavyweight to trust.

6. Claude – Anthropic’s Reflective Thinker

Anthropic’s Claude 4 Sonnet is like the careful colleague who always double-checks their work. Its extended thinking mode allows the model to pause, reflect, and refine outputs before committing.

With a 200K-token context window, it handles long documents with ease, making it a natural fit for legal analysis, compliance-heavy industries, and coding projects that need extra accuracy.

If reliability is more important than speed, Claude delivers consistency and thoughtfulness.

7. Mistral – Small but Mighty

Sometimes you don’t need a massive model—you need one that’s fast and affordable. Enter Mistral Small 3.

With 24B parameters, Apache 2.0 licensing, and speeds up to 150 tokens per second, it’s optimised for low-latency applications. The kicker? You can run it on a single GPU or even a MacBook.

For startups and lean businesses, Mistral proves that small models can pack a punch.

8. Gemini – Google’s Reasoning Powerhouse

Google’s Gemini 2.5 is pushing boundaries with a 1M-token context window. That means it can process entire books or databases in one shot.

It’s multimodal, handling text, images, and code, and comes with self-fact-checking to reduce hallucinations.

It’s proprietary, so data compliance matters, but if you want enterprise-grade multimodality and serious reasoning, Gemini is one of the most advanced options on the market.

For those preferring open weights, Google’s Gemma 3 (1B–27B) brings much of the same reasoning strength in a lighter package.

9. Command R – Cohere’s Enterprise Specialist

Cohere isn’t trying to win the hype war—it’s focused on enterprise workflows. Their Command R+ offers 128K context windows, built-in citations, multilingual coverage, and retrieval-augmented generation.

It excels at policy manuals, compliance-heavy industries, and multilingual customer service. And for companies needing control, Command A is open-sourced at 111B parameters with 256K context support.

For enterprises where accuracy and compliance come first, Cohere is a trusted partner.

10. Falcon – The Middle Eastern Power Play

From the Technology Innovation Institute (TII) in Abu Dhabi, Falcon has emerged as one of the strongest open-weight LLMs outside the US, China, or Europe.

The latest version, Falcon 2, boasts multilingual capabilities, optimised efficiency, and open-access licensing. It’s trained on a diverse dataset with an emphasis on global inclusivity, making it particularly strong in Arabic and other underrepresented languages.

What makes Falcon stand out is its mission: bringing AI sovereignty to regions that often depend on Western or Chinese tech. By providing a robust open-source model, Falcon gives governments, universities, and enterprises across the Middle East a homegrown alternative.

If AI diversity and regional sovereignty are important to you, Falcon is an LLM worth watching closely.

Closing Thoughts

Ten models. Ten different approaches to the future of AI.

  • OpenAI and Gemini lead with polished, proprietary power.
  • DeepSeek, Qwen, Llama, and Falcon prove open-source can compete and even outpace.
  • Claude and Cohere focus on reliability and compliance.
  • Mistral and Grok carve out niches in speed, agility, and personality.

The bigger question isn’t “Which is the best?” but “Which one is the best fit for you?”

AI in 2025 is not a single path—it’s a crossroads with ten directions. And whichever road you choose, the destination is changing how we work, build, and think.

Now I’d love to hear from you. Which of these ten models do you think will dominate the AI race by 2030—and why? Share your thoughts in the comments.

Why FAVORIOT Exists: The Deeper Purpose Behind Our IoT Mission

“Why do you do what you do?”

It’s a simple question — but one that hit me like a lightning bolt the first time I heard it posed by Simon Sinek in his book “Start With Why.” I thought I had the answer years ago when we founded FAVORIOT. We wanted to build an IoT platform. We wanted to be part of the Fourth Industrial Revolution. We wanted to make Malaysia proud.

But after reading Find Your Why, I realized I had only scratched the surface.

So I decided to go deeper. To strip away the features, the dashboards, the data streams — and ask myself, what is our true reason for being?

The Early Sparks: Frustration as Fuel

I spent decades in various ecosystems — from academia to government, corporates to startups. In every world, I saw the same problem repeat like a broken record: brilliant people with smart ideas were stuck because the technology was either too expensive, too complicated, or too foreign.

“Why are we importing tech for things we can build locally?”

“Why can’t our students graduate with real IoT skills, not just theories?”

“Why does every ‘Smart City’ pilot end with a press release but no long-term sustainability?”

Each “why” turned into fuel.

And that’s how FAVORIOT was born. Not from a business plan, but from frustration. From the belief that things should be simpler. That IoT shouldn’t be reserved for tech giants. That a kampung farmer, a Form 5 student, and a municipal engineer all deserve access to the same tools of transformation.

Understanding Our WHY

According to Find Your Why, every organization must uncover its purpose through reflection, story, and the impact it wants to make. It isn’t about what you do — it’s about why you do it.

And the format is simple yet powerful:

TO [your contribution] SO THAT [your impact].

So I asked myself:

  • What do we do when we’re at our best?
  • What makes us proud?
  • What kind of future do we want to build — not just for us, but for others?

Our WHY Statement

To empower people and organizations with accessible IoT technology, so that they can build smarter, connected futures on their own terms.

Let me unpack that for you.

“To Empower People and Organizations…”

We don’t just provide a dashboard.

We empower students to build their final year projects with confidence. We empower lecturers to teach IoT without needing an AWS certification. We empower entrepreneurs to launch sensor-based services. We empower city councils to detect flood risks, monitor waste bins, and receive alerts directly on Telegram — without vendor lock-ins or complex coding.

This empowerment comes in the form of:

  • A local, developer-friendly IoT platform (FAVORIOT Cloud)
  • Training and certifications via FAVORIOT Academy
  • Partnerships that build ecosystems, not just transactions

We’ve seen it firsthand — the moment someone realizes “Hey, I can build this myself” — that’s where our real work begins.

“…with Accessible IoT Technology…”

IoT is often wrapped in buzzwords: LPWAN, edge computing, mesh networks. But in truth, most users don’t need to know all that.

What they need is:

  • A clean dashboard
  • A reliable API
  • A simple setup guide
  • Local support, not just chatbot replies from time zones away

We built FAVORIOT with accessibility in mind. Not “dumbed down,” but demystified. So that even if you’re a high school student or a small-town official, you can say, “Yes, I understand this.”

We’re proudly Made in Malaysia, but we’re built for global adoption — especially in regions where digital transformation is often a PowerPoint slide, not a daily tool.

“…So That They Can Build Smarter, Connected Futures…”

This is the impact. The soul of our mission.

It’s not about selling more subscriptions or deploying more gateways. It’s about helping others take control of their own digital transformation.

A university that trains 500 certified IoT graduates per year?
That’s a smarter future.

A logistics company that reduces vehicle downtime with sensor data?
That’s a smarter future.

A kampung that uses IoT to monitor river levels and avoid flooding?
That’s not a Silicon Valley fantasy. That’s reality. And it’s happening.

Because we gave them the tools — and more importantly, the confidence — to build it on their own terms.

What Favoriot Is Not

We’re not trying to compete with AWS or Azure on scale.

We’re not just another smart city vendor with flashy mockups and no follow-through.

And we’re definitely not in it for vanity metrics.

What we are building is a platform that:

  • Trains the next generation of engineers and technologists
  • Supports local system integrators with ready-to-deploy tools
  • Strengthens national resilience by owning our tech stack
  • Connects the dots between ambition and execution

Why This Matters — Especially Now

Everyone’s talking about AI. And yes, AI is exciting.

But here’s the truth: AI needs data. And data comes from IoT.

Without sensors, there are no predictions. Without real-time input, there’s no intelligent decision-making. IoT is the nervous system — AI is the brain. You can’t build a smarter future with just one.

Yet IoT is often the unsung hero.

FAVORIOT exists to make that hero visible — to give it a platform, a purpose, and most importantly, a presence in our communities.

Closing Thoughts: Why I’m Still Here

People sometimes ask me, “After all these years, what keeps you going?”

And honestly, it’s not the tech.

It’s the message I got from a student who said, “Dr., because of the Favoriot certification, I got hired immediately after graduation.”

It’s the local council officer who said, “We prevented a flood this year — because of your alerts.”

It’s the partner in Indonesia who said, “We never thought we could build our own IoT solution — until Favoriot.”

That is our WHY.

That is why we exist.

And that is why we’ll keep building.

Your Turn

If you’re a student, a policymaker, a developer, or an entrepreneur — and you’ve ever thought “IoT is too complex” — I invite you to rethink that.

Because with the right platform, the right support, and the right purpose — you’re closer to a smarter future than you think.

And we, at Favoriot, are here to help you build it.

Let’s democratize IoT. Together.

Let’s Make IoT Great Again — The Malaysian Comeback We’ve Been Waiting For

“Malaysia’s not ready yet…”

You’ve heard that line, haven’t you?
I’ve heard it in government meetings, corporate pitches, startup huddles, even in university halls.

“Let’s wait for the right timing.”
“Let’s see if the budget gets approved.”
“Let’s hold until the talent pool matures.”

Enough waiting. Seriously.

Because if we keep hitting pause, someone else is going to press play — and leave us behind in the dust.

South Korea Didn’t Wait. China Didn’t Either.

In the 1980s, South Korea was still recovering and rebuilding.
In the 1990s, China was just finding its footing on the world stage.

They weren’t “ready” either.

But they moved.
They dared.
They started.

And now? The world watches them. Learns from them. Competes with them.

Malaysia, it’s our turn. But only if we dare to move — even if it’s messy.

Whatever Happened to IoT?

I still remember when IoT was the darling of tech conferences.

Smart cities.
Smart farming.
Smart industries.
Smart everything.

IoT was the buzzword. The future.

But slowly, it faded. AI came in with a bang — and now even school kids are doing AI projects. Meanwhile, IoT became the forgotten tech. The backup dancer.

But guess what? IoT never went away. It just stopped trending.

And that’s not fair — because IoT is the foundation.
No IoT, no data.
No data, no AI.
No AI, no “smart” anything.

We’ve been cheering for AI, but forgot where AI gets its brain food — real-world data from IoT devices.

So let’s bring IoT back to the main stage.

Waiting for a Masterplan? Here’s the Truth.

Malaysia loves blueprints. Loves roadmaps. Loves waiting for official green lights.

But progress rarely comes from the top. It starts in the cracks.
In university labs.
In garage workshops.
In kopitiam brainstorms.
In “I-don’t-know-coding-but-I’ll-try” kinda attitude.

You don’t need to be a coding wizard.
You don’t need RM100,000.
You just need the guts to start.

Platforms like FAVORIOT make it ridiculously easy to test, build, and learn. Plug and play. Create a dashboard. Get alerts. It’s not rocket science anymore.

And you don’t need permission to innovate.

Here’s My Challenge to You

I’m not asking you to build Malaysia’s next unicorn startup tomorrow.

I’m asking you to:

  • Build a small IoT project with your kids.
  • Monitor your home’s electricity using sensors.
  • Start a DIY smart farm with friends.
  • Teach students how to send data to the cloud.
  • Connect a temperature sensor to a dashboard just because you can.

Each small project creates momentum.
Each momentum builds confidence.
Each confidence turns into a movement.

Imagine hundreds — no, thousands — of these projects happening across Malaysia. That’s not hype. That’s ecosystem-building.

Start Small. Start Messy. But Please—Start Now.

Let’s stop worrying if it’ll fail. Let’s stop doubting ourselves.

Failure is part of the story.

Every successful nation, every great tech innovation — it all started with people trying, failing, adjusting, and trying again.

If we want Malaysia to lead in IoT, we need to stop talking and start doing.

Because:

  • The technology is already here.
  • The talent is growing.
  • The platforms are local and ready.
  • The excuses are tired.

The Revival Starts Here — and With Us

I’m writing this not just as someone in the IoT industry, but as a Malaysian who’s tired of hearing “We’re not ready.”

What if we stopped asking for permission?
What if we trusted ourselves to build something great from the ground up?
What if our “small” becomes the next big thing in Southeast Asia?

This isn’t a government-only mission. This isn’t a corporate-only opportunity.

This is everyone’s movement.

If we wait for perfect conditions, we’ll never move.

So let’s stop waiting. Let’s start building.

Malaysia, This Is Your IoT Moment

It’s not about who’s ahead now. It’s about who dares to start — and keeps going.

We’ve got what it takes.

Let’s build the sensors.
Let’s write the code.
Let’s run the dashboards.
Let’s fix the bugs.
Let’s train the students.
Let’s test the ideas.
Let’s MAKE MISTAKES.

And let’s make IoT great again — in our own Malaysian way.

Not by following others, but by leading with bold, messy action.

Are you in?

The Book That Finally Told the Truth About IoT

For years, I’ve watched the Internet of Things evolve—promises, pilots, platforms, and… silence.

We were told IoT would change everything. And yet, here we are. Smart cities still look like science projects. Predictive maintenance rarely gets past a demo. Even in conferences, IoT is the quiet cousin, while AI receives the spotlight.

So when I read “IoT: The Hype No One Knows About” by Afzal Mangal, I didn’t just read it—I felt it.

This wasn’t another technical deep dive. It wasn’t a glossy case study collection either. It was something rare: an honest book written by someone who’s actually done the work, faced the resistance, and survived the grind.

It’s Not the Technology That’s Broken

The core message? IoT works. That’s not the problem. The issue is that no one knows or sees it, and often, no one asks for it.

IoT doesn’t fail in the lab. It fails in the boardroom. It fails when:

  • Decision-makers don’t know what problem IoT solves
  • Internal champions give up after the pilot
  • Sales teams can’t explain it without five slides and a PhD

This hit hard. I’ve seen excellent IoT projects—solid tech, measurable impact—die quietly because there was no momentum to take them further.

Meanwhile, AI Took the Stage

Mangal makes a bold (and fair) comparison: AI and IoT were both hyped. But only one became mainstream.

Why?

Because AI built a tribe, it became aspirational. It had influencers, evangelists, podcasts, and memes. It was everywhere. IoT, on the other hand, stayed niche. It stayed quiet. It stayed technical.

It didn’t show off its wins. It didn’t shout. And that’s where we lost the game.

The Book Offers Solutions, Not Just Complaints

The best part isn’t just the diagnosis. It’s the prescription.

Mangal outlines 70 actions—from marketing to product strategy—that are refreshingly doable. No jargon. Just real advice:

  • Put “IoT” in your product name
  • Sell small and scale later
  • Educate the market like a campaign
  • Speak in stories, not specs
  • Make IoT visible in daily life

It sounds simple, but when did we last do any of that?

My Honest Take

This book isn’t for those looking for another buzzword to pitch. It’s for those tired of being invisible. It speaks to the founders, engineers, salespeople, and educators who want IoT to finally get the recognition it deserves, technically and publicly.

It made me reflect deeply on how I present, pitch, and teach IoT.

We can keep building great tech. But until we start creating awareness, IoT will remain a background actor in a play it should lead.

My Final Thoughts

Afzal Mangal didn’t just write a book—he wrote a mirror. If you’ve ever been frustrated with the slow progress of IoT adoption, this book gives you clarity—and a plan.

I highly recommend it to anyone in the IoT space, especially:

  • Startup founders
  • Product managers
  • Policy-makers
  • Tech educators
  • Marketers are trying to position IoT solutions

The industry doesn’t need more hype. It needs truth, clarity, and action.

This book delivers all three.