Tag: IoT

Why Universities Need a Real IoT Lab, Not Just Another Embedded Lab

I still remember a meeting with a group of lecturers, where I asked a simple question.

“Why do we really need an IoT Lab in universities?”

I paused for a moment.
Because deep down, I already knew the answer.

Most universities already have something they proudly call an IoT lab. Rows of ESP32 boards. Arduino kits. LEDs are blinking happily. LCD screens displaying temperature values. Students smile because something lights up.

And yet, something feels incomplete.

This is not IoT. This is just the beginning.

This blog is not meant to criticise universities. I have spent years inside them. I was once a lecturer designing syllabuses, labs, and assessments. This comes from care. From concern. From watching students graduate with confidence in embedded programming but struggle the moment systems become real, connected, and dependent upon.

This reflection is based on a recent lecture I delivered on the need to establish a proper IoT Lab in universities, one that reflects how systems are actually built, deployed, and trusted today.

Embedded Systems Taught Us How to Build Devices

Let me be very clear.

Embedded systems are important. They are foundational.

Students need to learn how to program microcontrollers. They need to understand sensors, actuators, interrupts, memory, and power consumption. All of that matters.

An embedded system is usually a standalone device. It senses something. It controls something. It logs data into local storage.

There is nothing wrong with that.

In fact, embedded systems are still used today in places with no connectivity. Remote areas. Harsh environments. Offline conditions.

But here is the problem.

Once the data is captured, someone has to physically go to the site, connect a laptop, download the data, return with it, and process it manually.

I have seen this happen too many times.

Two technicians. One vehicle. Hours of work. Just to retrieve data that could have been transmitted automatically.

I always ask myself… why are we still doing this in 2026?

Connectivity Changes Everything

The moment a device is connected to the internet, everything changes.

Data no longer waits for humans to come and collect it.
It flows.
It moves.
It becomes alive.

This is the moment embedded systems evolve into the Internet of Things.

Now we can monitor systems remotely.
Now we can detect failures early.
Now we can see battery levels dropping before devices die silently.
Now we can act before complaints arrive.

And yet, most university labs stop just before this moment.

Students are taught how to blink LEDs, but not how to send data reliably.
They learn how to display values, but not how to secure data in transit.
They build devices, but not systems.

And systems are what the real world depends on.

A Real IoT Lab Must Teach Technology Layers

IoT is not a single skill. It is a stack.

In my lecture, I stressed that a proper IoT Lab must expose students to multiple technology layers, not in theory, but through hands-on work.

Layer 1: Hardware and Firmware

This is where universities are already strong.

Sensors. Controllers. Actuators. Firmware logic. Power management.

Students should continue learning this well.

But they must also understand that this is just one layer.

Layer 2: Connectivity and Protocols

This is where gaps start to appear.

Students must learn how data travels.

Wi-Fi. Cellular. LPWAN.
Bluetooth. ZigBee. RFID.
MQTT. CoAP. REST. HTTP.
LoRa. NB-IoT. Sigfox.

Not as a list to memorise.
But as choices with consequences.

Which protocol suits low power?
Which network works for long range?
What happens when connectivity drops?

Without this understanding, troubleshooting becomes guesswork.

Layer 3: Platform and Middleware

This is the heart of IoT.

Devices do not talk directly to dashboards. They talk to platforms.

An IoT platform manages devices.
Authenticates them.
Stores data.
Provides APIs.
Handles scale.

This is where students should learn about device identities, data ingestion, databases, and analytics pipelines.

This is also where they start to understand why platforms like FAVORIOT exist in the first place.

Not to replace learning.
But to enable it.

Layer 4: Analytics and Visualisation

Dashboards are not the end goal.

They are the beginning of understanding.

Students should learn how data evolves from descriptive charts to deeper insights.
They should see patterns.
Spot anomalies.
Ask better questions.

This prepares them for real projects, not demos.

Security Must Exist Across All Layers

Security cannot be an afterthought.

Devices must be authenticated.
Data must be encrypted.
Platforms must be protected.
Applications must be hardened.

Most labs barely touch this.

And yet, this is where real systems fail.

When Systems Break, Knowledge Is Tested

I often tell students this.

The real test of IoT knowledge is not when everything works.

It is when something breaks.

Data stops arriving.
Dashboards go blank.
Alerts do not trigger.

At that moment, students panic if they only know how to code LEDs.

But students who understand layers start asking better questions.

Is it the device?
Is it the network?
Is it the platform?
Is it the visualisation?

This is the mindset a real IoT Lab must build.

Research, AI, and the Future of IoT Labs

Universities are not just about projects. They are about research.

To do meaningful research, students need data. Lots of it. Clean data. Continuous data.

IoT Labs enable this.

Once data flows reliably, students can apply machine learning.
They can explore pattern recognition.
They can experiment with predictive models.

Today, this also means understanding edge AI.

Inference running on devices.
Decisions made locally.
Latency reduced.
Systems are becoming smarter as they operate.

This is where IoT Labs naturally evolve into AIoT Labs.

And this is where universities must go.

This Is a Call to Universities, Lecturers, and Policymakers

If we want graduates who can build real systems, not just academic projects, we must change how we teach IoT.

IoT Labs must move beyond embedded programming.
They must teach architecture, trade-offs, and responsibility.
They must reflect how systems are deployed outside campus walls.

I believe universities can do this.
I believe lecturers want this.
I believe students deserve this.

But it requires intention.

It requires investment.
It requires collaboration with the industry.
It requires courage to redesign labs that have been unchanged for years.

If you are a lecturer, start asking what your lab is missing.
If you are a dean, ask whether your graduates can troubleshoot real systems.
If you are a policymaker, ask whether our talent pipeline matches national ambitions.

And if you are a student reading this, ask yourself one question.

Am I learning how to build a device… or how to build a system people can trust?

I would love to hear your thoughts, experiences, and struggles in building or teaching IoT.
Drop a comment. Let’s talk.

Smart Cities in Malaysia – Between Beautiful Documents and Real Implementation

There is a kind of silence I remember very clearly.

Not the silence of an empty room.
But the silence appears when too many slides are shown, too many big words are used, and too few honest questions are asked.

I was sitting in yet another Smart City presentation. Everyone was talking about master plans, KPIs, ISO standards, indicators, and rankings. The slides looked polished. The diagrams were neat. The language sounded confident.

Yet inside, a quiet question kept repeating.

Who will take care of this after the launch?
Who will wake up when the system fails?
Who will face the public when citizens are angry?

Those questions rarely appear on slides.

And that, for me, is where the real Smart City problem in Malaysia begins.

Why We Talk About Smart Cities at All

Smart Cities are not about technology.

They are about people.

They are about parents stuck in traffic every morning.
Children breathe unhealthy air.
Small business owners suffer when flash floods arrive without warning.

I often say this, and I truly believe it.

The final goal of a Smart City is very simple.

People can live healthier lives.
People can live happier lives.
Cities can run without constant chaos.

That’s it.

Not rankings.
Not awards.
Not plaques on office walls.

Yet somewhere along the way, we lost that simplicity.

When Everyone Wants to Be “Smart”

Today, almost everyone wants the Smart City label. Some go further and call themselves “AI Cities”.

I usually smile when I hear that.

Which AI?
Where is the intelligence?
Or is it just a new signboard?

Many systems branded as AI are simply basic automation. This is not a technology problem. It is a misunderstanding.

What troubles me more is this.

Some cities work quietly, building systems that actually function, yet receive no recognition because they never submitted an application. Others receive recognition early simply because they know how to fill out forms.

That’s when we learn a hard truth.

Recognition does not always reflect maturity.

From 30,000 Feet to 3 Feet

To be fair, Malaysia does have a solid Smart City framework.

On paper, it makes sense.

At 30,000 feet, there is the national master plan.
At 3,000 feet, there are state-level blueprints.
At ground level, around 3 feet, there should be detailed action plans for local councils.

Everything looks structured.

But in reality, many councils are stuck somewhere in between.

Some are still writing plans.
Some are experimenting with pilot projects.
Very few are running systems consistently, day after day.

Not because they don’t care.
Not because they are lazy.

But because something fundamental is missing.

Delivery structure.

Command Centres That Feel Empty

I have visited many command centres.

The name sounds powerful. Command centre. It feels important.

But once inside, the scene is often the same.

CCTV screens.
Live video feeds.
A few officers are watching.

That’s all.

I quietly ask myself.

Where is the data integration?
Where is the analysis?
Where are the decisions driven by this data?

A command centre should be the brain of the city.

Not just its eyes.

Imagine traffic data, air quality, noise levels, parking systems, citizen complaints, legacy databases, all connected and analysed together.

First, we know what has happened.
Then, we understand why it happened.
Next, we anticipate what might happen.
Finally, we know what action to take.

This is where technology truly matters. Not to impress, but to guide decisions.

The Challenges We Rarely Admit

Since around 2015, I have seen the same issues repeat.

Budget That Is Never Enough

Local councils are not money-making machines.

They rely on limited revenue sources. Assessment taxes. Parking fees. Licenses.

With limited funds, choices become limited too.

Projects that generate revenue or reduce costs often get priority. Long-term social impact projects struggle to survive.

Risk-heavy concession models usually favour large companies with deep pockets. Smaller local players, often full of ideas and energy, get pushed aside.

Sometimes I ask myself quietly.

Do we really want local ecosystems to grow?
Or are we just choosing the easiest path?

Projects Without Guardians

This one hurts the most.

Many Smart City projects are launched with excitement. Press conferences. Posters. Promotional videos.

A year later, the system is silent.

No maintenance.
No monitoring.
No clear ownership.

The project becomes a white elephant.

Not because the technology failed.
But because no one was assigned to take care of it.

Skills Gap Is Real

Smart Cities demand new skills.

Data management.
IT infrastructure.
Commercial thinking.
Long-term contract handling.

Many council officers come from strong urban planning backgrounds. They are good at what they do.

But we cannot expect them to suddenly manage complex digital systems without proper support.

This is not an individual failure. It is a structural one.

Fragmented Governance

For solution providers, one simple question often becomes complicated.

Who should we speak to?

Without a clear focal point, discussions lose direction. Time is wasted. Trust slowly fades.

One Answer I Strongly Believe In

If there is one thing that matters most, it is not technology.

It is a Delivery Unit.

A dedicated team.
Given authority.
Given responsibility.
Given continuity.

This unit becomes the caretaker of the entire Smart City lifecycle.

From strategy.
To execution.
To daily operations.
For long-term maintenance.

When this unit exists, everything changes.

Communication becomes clear.
Ownership becomes visible.
Projects do not get abandoned.
Citizens start feeling real benefits.

Smart City stops being a document.
It becomes a service.

Why I Still Have Hope

I am not writing this out of frustration.

I am writing because I still believe.

I believe our local councils are capable.
I believe our local talent is strong.
I believe technology is just a tool, not the answer.

What we need is the courage to admit weaknesses and the wisdom to build the right structure.

Smart City is not a race.
It is a responsibility.

If we want public trust, we must start taking care of what we build.

And it begins with one honest question.

Who will make sure this city still works tomorrow morning?

If this piece made you pause and think, I would love to hear your thoughts.
Leave a comment. Let’s talk.

Building IoT Alone vs Building Together: Why Local Platforms Change Everything

I want to share something that has been sitting heavily in my heart for a while.

Every time I speak to engineers, lecturers, startups, or research teams, I ask a simple question.

“What IoT platform are you using?”

The answers came quickly.

From abroad.
From overseas.
From a big global brand.
Or… “We built our own server.”

I nodded. I smiled. But inside, something felt heavy.

Why are we still doing this to ourselves?
Why do we keep believing the best tools must come from somewhere else?

That moment stayed with me long after the talk ended

We Are Obsessed With Dashboards, But Forget the Foundation

Let me be honest.

Many IoT teams I meet are not obsessed with devices. They are obsessed with dashboards.

Big screens.
Live charts.
Green indicators that say “OK”.

Nothing wrong with that. Dashboards matter. Visibility matters.

But when I dig deeper and ask, “Who do you actually work with behind that platform?”
Silence.

They have never met the platform provider.
Never spoken to an engineer there.
Never sat down to plan a market together.

How do you build something meaningful when you do not even know who is behind the engine?

That is the first quiet weakness nobody talks about.

Depending on a Distant Platform Feels Safe. Until It Isn’t.

Using a foreign platform feels comfortable.

It feels established.
It feels global.
It feels like you are standing on something big.

But distance has a price.

No close collaboration.
No shared story.
No joint effort to help your product grow beyond a pilot.

When something breaks, you open a ticket.
When something stalls, you wait.
When you want to commercialise, you are on your own.

I thought to myself, is this really what building an ecosystem looks like?

Local Platforms Are Not “Second Choice”. They Are Strategic Choices.

This is where my heart always leans forward.

When a university, a startup, or a solution provider works with a local IoT platform like Favoriot, something changes.

You do not just get software.

You get people.
You get conversations.
You get arguments on whiteboards.
You get someone who cares because your success is their success, too.

We can sit together.
We can shape the solution together.
We can plan how it reaches the market together.

That closeness is not a luxury. It is a multiplier.

Cross-Marketing Is Not a Buzzword. It Is Survival.

Let me put this simply.

Your market is never big enough on its own.
Neither is mine.

But when we walk into each other’s markets together, something opens up.

Your customers see us.
Our users see you.
Stories start travelling.

If a project uses our platform, we talk about it.
We highlight it.
We share it across our channels.

And no, this is not charity.

It is shared growth.

I remember thinking, why should every company shout alone when we can amplify each other’s voices?

Bundling Is About Completing the Story, Not Selling More Stuff

Here is another truth most people avoid.

Almost nobody builds everything themselves.

You may focus on air quality.
But your hardware comes from overseas.
Your connectivity comes from someone else.
Your cloud might sit on Azure or AWS.

That is normal.

What matters is how these pieces come together for the customer.

A single product often feels incomplete.
A bundled solution feels finished.

Your sensor plus our platform.
Your analytics plus our alerts.
Your service plus our visibility.

The customer does not want components.
They want relief.
They want clarity.
They want answers.

Bundling is not about pushing more.
It is about removing friction.

Ego Is the Silent Killer of IoT Ecosystems

This is the part that makes people uncomfortable.

Ego.

The belief that “we can do everything ourselves.”
The fear that collaboration means losing control.
The worry that sharing space means shrinking your brand.

I have seen this mindset slow down brilliant teams.

I told myself, collaboration is not surrender.

Working with partners does not make you smaller.
It makes you reachable.

It gives you angles you cannot create on your own.

Universities, Startups, Platforms. We Need Each Other.

Universities have ideas.
Startups have hunger.
Platforms have structure.

Separately, we struggle.
Together, we move.

When a university builds a project on a local platform, that project does not end as a report.
It becomes a case study.
A reference.
A stepping stone to something real.

When a startup launches on a local platform, it does not just deploy.
They learn how to sell.
How to explain value.
How to survive their first customers.

I often whisper to myself, this is how ecosystems are supposed to feel.

Why This Matters More Than Ever

We talk about national capability.
We talk about digital sovereignty.
We talk about nurturing local champions.

But these words mean nothing if we keep outsourcing belief.

Supporting local platforms is not about patriotism.
It is about practicality.

Local platforms understand local constraints.
Local regulations.
Local customers who call you at 2 a.m.

And when you grow, they grow with you.

A Quiet Invitation

If you are building IoT solutions today, pause for a moment.

Ask yourself:

Who do I actually collaborate with?
Who knows my product beyond a ticket number?
Who will walk with me to the market?

If the answer feels distant, maybe it is time to rethink.

Not to abandon global tools.
But to anchor your growth closer to home.

I believe ecosystems are built by hands that reach out, not by fingers that point outward.

Let us talk.
Let us partner.
Let us bundle, cross-promote, and craft stories that travel beyond dashboards.

Contact Favoriot and let’s build IoT solutions together.

I would love to hear your thoughts.
Share your experience in the comments.

When Data Leaves the Country, Control Leaves With It

A personal reflection on why I insisted on an Enterprise IoT Platform with on-premise deployment

There are moments in a founder’s life when you stop talking about features.

You stop talking about dashboards.
You stop talking about protocols.
You even stop talking about scale.

And you start talking about control.

This was one of those moments.

I remember sitting quietly after one of our internal briefings, staring at the whiteboard filled with arrows, boxes, and deployment diagrams. Everyone had left the room. The air was still.

Why am I pushing so hard for this Enterprise Plan?
Why does this feel heavier than just another pricing tier or feature release?

Then it hit me.

This was not about software.
This was about sovereignty.

And once you see it that way, you can never unsee it.

From owning a kitchen to owning the whole restaurant

For years, I used a simple analogy when explaining IoT platforms.

If you are on a shared cloud platform, you are like a chef renting a kitchen. You can cook. You can serve. But you do not own the space. You follow house rules. You live with limits.

The Enterprise Plan is different.

It is not about owning the kitchen anymore.
It is about owning the whole restaurant.

The building.
The keys.
The doors.
The data flows.
The servers are sitting quietly in your own premises.

When you own the restaurant, no one tells you when to close. No one caps how many customers you can serve. No one decides where your ingredients come from.

That is the mindset behind the Enterprise IoT Platform.

The moment I realised cloud is not always the answer

For a long time, cloud felt like the default answer to everything.

Fast.
Flexible.
Convenient.

I believed in it. I still do, for the right use cases.

But over the years, as I spoke to large organisations, city operators, government agencies, and critical infrastructure owners, a pattern kept repeating itself.

“We don’t want our data outside the country.”
“We need to know exactly where the servers are.”
“We cannot afford external dependencies for this system.”
“This data is too sensitive.”

At first, some people dismissed these concerns as paranoia.

I did not.

Because when you are dealing with traffic lights, water systems, energy grids, and public safety sensors, paranoia is just another word for responsibility.

What happens if the platform is outside the country and something goes wrong?
Who takes control when connectivity is lost?
Who answers when an entire city goes dark?

These are not theoretical questions. These are operational nightmares waiting to happen.

Data sovereignty is not a buzzword when infrastructure is involved

Data sovereignty sounds abstract until you put real consequences next to it.

Imagine a critical infrastructure monitoring system managed by a platform hosted overseas. One day, there is a major failure.

Power outage.
Network disruption.
Access blocked.

The local operators are standing there, staring at blank screens, unable to take control because the system that runs their infrastructure is not physically within reach.

That is unacceptable.

This is why on-premise deployment matters.

Not because it sounds serious.
Not because it looks impressive in a proposal.

But because control must stay with those who are accountable.

This thinking shaped every part of the Enterprise IoT Platform plan.

AI made the stakes even higher

If IoT data is sensitive, AI makes it explosive.

AI models learn from data.
Patterns.
Behaviours.
Weak points.

When AI touches critical infrastructure data, the question is no longer just “where is my data?”

It becomes “who understands my system better than I do?”

That was the turning point for me.

If AI is going to sit on top of IoT data, then the data must never leave the country.

This is not about fear.
This is about governance.

Every country I speak to says the same thing, whether it is Malaysia, Indonesia, the Middle East, or Europe.

“We want AI. But we want our data at home.”

The Enterprise Plan was designed to respect that reality.

Unlimited API is not a luxury; it is a survival

One detail that often gets overlooked is API limits.

People ask me, “Why unlimited API? Isn’t that excessive?”

Let me paint you a picture.

A manufacturing line monitors machines every second.
One sensor. One data point per second.
Multiply that by hundreds of machines.
Multiply again by shifts, days, months.

Suddenly, 500,000 API calls per day is not generous. It is restrictive.

The Developer Plan has limits because it should. It is built for builders, experimentation, and controlled scaling.

But enterprise environments do not experiment. They operate.

If you throttle data in an industrial environment, you are not saving costs. You are introducing blind spots.

Unlimited API is not about indulgence.
It is about continuous visibility.

Two very different enterprise realities

As I refined this plan, two clear deployment models kept emerging.

1. The white-label service provider model

Some organisations do not want to sell hardware.
They want to sell managed IoT services.

They do not want to build a platform from scratch. That path is expensive, slow, and painful.

So they white-label the Enterprise IoT Platform.

Their brand.
Their customers.
Their business logic.

They plug in their agricultural sensors, industrial devices, and vertical solutions, and run everything on their own enterprise platform.

Thousands of customers.
One controlled system.

I have seen how powerful this model can be when done right.

2. The smart city and government deployment model

Then there are cities.

Cities are different.

They already have many solutions. Parking. Flood sensors. Air quality. Lighting. Waste.

The problem is not a lack of data.
The problem is fragmentation.

Every system has its own dashboard. Its own vendor. Its own silo.

Local councils want a single platform, deployed on-premises, where everything comes together.

In some cases, councils cannot do this alone.

That is where state-level deployment makes sense.

One enterprise platform owned by the state.
Local councils connect their data.
Data stays within the country.
Visibility scales across regions.

It is pragmatic. It is cost-aware. It respects sovereignty.

This is bigger than one platform

As I reflect on this journey, I realise something.

The Enterprise IoT Platform is not just a product decision.
It is a philosophical stance.

It says:

You should own your data.
You should control your infrastructure.
You should not outsource accountability.

In a world rushing towards convenience, this is a reminder that responsibility still matters.

A quiet call to builders, cities, and leaders

If you are building systems that people depend on, ask yourself one simple question.

When things go wrong, who truly has control?

If the answer is unclear, it might be time to rethink how your platform is deployed.

I did.
And that rethink led us here.

I would love to hear your thoughts.
Where do you draw the line between convenience and control?
Share your reflections in the comments.

Owning the Kitchen: Why We Built the Favoriot Platform Developer Plan for People Who Build for Others

There are moments in this journey where I stop, lean back in my chair, and ask myself a very simple question.

Who am I really building this for?

Not the slides.
Not the feature list.
Not the pricing page.

The real humans.

The ones wiring sensors late at night.
The ones deploying devices in places nobody wants to visit twice.
The ones whose customers call at 7 a.m. asking, “Why is the dashboard blank?”

This blog is about those people.

This blog is about why I insisted we needed something called the Favoriot Developer Plan.

And why, for some builders, it changes everything.

Not Everyone Is Just “Using” an IoT Platform

Early on, I realised something uncomfortable.

Many people assumed Favoriot was just another place to view charts.

Log in.
See a graph.
Log out.

But that was never the real story.

I thought to myself, if that’s all we become, we’ve failed the serious builders.

Because some users are not there to monitor one device.

They are there to build services.
They are there to serve clients.
They are there to run a business on top of the platform.

System integrators.
Solution providers.
Engineers who sign contracts, not tutorials.

And these people don’t need toys.

They need a kitchen.

When One Dashboard Is Not Enough

One of my favourite real-world examples comes from a team offering indoor air quality monitoring.

Simple problem on the surface.

Measure CO₂.
Measure temperature.
Measure humidity.
Show the data.

But reality is never that clean.

Each customer wanted their own view.
Each building had different thresholds.
Each management team wanted different access.

So the question became obvious.

How do you serve many customers without creating chaos?

This is where multi-tenancy stopped being a buzzword and became a survival tool.

With the Developer Plan, each customer lives in their own space.

Their own dashboards.
Their own logins.
Their own sense of ownership.

No accidental peeking.
No data leaking.
No shared confusion.

Just clean separation, built for trust.

Analytics Is Not Just Pretty Charts

Let me be honest.

Most people stop at charts.

Real-time lines moving left to right.
Historical graphs with sliders.

That’s descriptive analytics.

Useful. Necessary. But incomplete.

I kept asking myself, what happens after people stare at the dashboard?

This is where things get interesting.

Diagnostic: Understanding the “Why”

At some point, users ask deeper questions.

Why did the spike happen at 3 p.m.?
Why does Monday look different from Friday?
Why did last week feel off?

Diagnostic analytics gives context.

Averages.
Minimums.
Maximums.
Patterns across time windows.

Suddenly, the dashboard stops being a screen and starts becoming a story.

Predictive: Seeing What Comes Next

Now we cross a line.

Instead of reacting, we begin anticipating.

Using machine learning models trained on sensor data, the system can estimate what might happen next.

An hour from now.
Later tonight.
Tomorrow morning.

This is the moment many users go quiet and just stare.

Because the platform is no longer waiting for events.

It’s whispering what might be coming.

Prescriptive: Acting When It Matters

This is where responsibility enters the room.

If the system knows something is heading toward risk, what should it do?

Do nothing?
Notify someone?
Trigger an action?

With prescriptive logic combined with our rule engine, the platform can respond.

Send a Telegram alert.
Email management.
Notify an engineer on duty.

And yes, in some cases, trigger actuation directly.

But I’ll say this clearly.

Some decisions still belong to humans.

Technology should support judgment, not replace it blindly.

Teaching Machines to Understand Risk

One feature I care deeply about is state classification.

Instead of raw numbers, we classify conditions.

Low risk.
Medium risk.
High risk.

This is not about making data fancy.

It’s about making data usable.

Because when someone is responsible for people’s safety, they don’t want to interpret charts at 2 a.m.

They want clarity.

When Devices Are Far Away, and Silence Is Expensive

There’s a painful truth in IoT.

Devices don’t live in offices.

They live on rooftops.
In factories.
In rural areas.
Across states.

Bringing them back just to update firmware is not a plan. It’s a liability.

That’s why over-the-air firmware updates matter so much in the Developer Plan.

You push updates remotely.
You fix bugs without travel.
You sleep a little better.

I’ve seen teams save weeks of work because of this.

The Quiet Hero: Edge Gateway

This is one of those features that few people talk about, but everyone feels.

Devices speak different languages.
Different payloads.
Different structures.

Instead of forcing developers to rewrite firmware again and again, the edge gateway steps in.

It maps data formats.
It normalises inputs.
It makes integration smoother.

Less friction.
Less rework.
More momentum.

Scaling Without Counting Every Call

One subtle pain point system integrators face is limits.

Counting API calls.
Worrying about overages.
Splitting usage by customer.

So we raised the daily API quota.

From fifty thousand to five hundred thousand calls per day.

That change alone unlocked new confidence.

Now builders can focus on serving customers, not watching counters.

The Restaurant Analogy That Still Makes Sense to Me

I still like using food analogies because they are honest.

A free plan is walking into a restaurant to see the menu.
A lite plan is tasting something simple.
A beginner plan is enjoying a full meal.

But the Developer Plan?

That’s owning the kitchen.

You decide the menu.
You cook for different tastes.
You serve customers your way.

And with ownership comes responsibility, pride, and freedom.

Why This Matters to Me

I didn’t build this plan to show off features.

I built it because I’ve been that engineer.
I’ve been that system designer.
I’ve been the one answering tough calls.

I wanted builders to feel supported, not boxed in.

If you are building for others, managing clients, and turning ideas into services, this plan was made with you in mind.

And if you’re still unsure, that’s okay.

Explore.
Ask questions.
Build slowly.

The kitchen will be there when you’re ready.

I’d love to hear from you.
What are you building, and what’s holding you back right now?

Why We Want Students to Struggle a Little When Learning IoT

I want to be honest with you.

Every time I walk into a university or polytechnic to talk about IoT, I already know the answer before I ask the question.

“What platform are you using for your final year project?”

The answers come fast. Almost automatic.

One particular platform. That has …
A mobile app.
Lots of YouTube tutorials.
GitHub samples.
Forums everywhere.

I nod. I smile.

I’ve heard this story many times.

And then comes the follow-up question.

“Why that platform?”

The answer is almost always the same.

“Because it’s easy.”
“Because there’s a mobile app.”
“Because seniors used it.”
“Because everything is already there.”

At that moment, a quiet thought always crosses my mind.

Easy today, but are you ready for tomorrow?

This blog is not about criticising students. Far from it.

It is about explaining why I believe students need to struggle a little when learning IoT. And why Favoriot was built the way it is.

The Comfort Trap Students Fall Into

I understand why students choose platforms that feel comfortable.

You connect a sensor.
Data appears on your phone.
You show it to your supervisor.
Demo done.

No need to build dashboards.
No need to understand protocols.
No need to worry about servers or data pipelines.

It feels good.

But comfort can be deceptive.

I asked myself, are we training students to build IoT systems, or are we training them to click buttons?

In many universities, IoT projects are still treated as extended embedded-system exercises.

Arduino.
ESP32.
Blink an LED.
Trigger a relay.

Nothing wrong with that.

But IoT is not just about making something move.

IoT is about data travelling.

From sensors.
Through networks.
Into platforms.
Across dashboards.
Into decisions.

If students never see that journey, they never really understand IoT.

The Chicken and Egg Problem Nobody Talks About

Let me share a frustration I rarely say out loud.

Favoriot is still new compared to global platforms.

Students don’t know us because there are fewer tutorials.
There are fewer projects online.
There are fewer YouTube videos.

And because of that, students don’t choose us.

Then I stop and think, how do you ever break this loop?

No users means no content.
No content means no discovery.
No discovery means no users.

Classic chicken and egg.

This is why I spend time going to universities.
Why I accept invitations to serve on industry advisory panels.
Why I keep introducing Favoriot in syllabus discussions.

Not because I want students to use Favoriot blindly.

But because someone has to be the first builder.

“But, Sir, Favoriot Has No Mobile App”

I hear this a lot.

And yes, for a long time, Favoriot did not have its own mobile app.

But here’s something many students don’t know.

There are MQTT mobile clients on Android and iOS.
They already have dashboards.
You configure them.
Connect to Favoriot.
And instantly see live data on your phone.

When I explain this, I see faces change.

“Oh… we didn’t know that.”

That sentence tells me everything.

Sometimes adoption is not about missing features.
It is about missing awareness.

And that is on us.
We need to explain better.
Show better.
Document better.

The Part Students Rarely See: The Full IoT Stack

This is where I get passionate.

IoT is not one skill.
It is many skills stacked together.

When students use platforms that hide everything, they only learn the top layer.

Here’s what real IoT demands.

Hardware
Sensors. Microcontrollers. Power. Battery life.

Connectivity
MQTT. CoAP. REST. WiFi. Cellular. LPWAN.

Platform
Device management. Ingestion. APIs. Storage.

Visualisation
Dashboards. Alerts. Rules.

Analytics
Understanding what happened. Why did it happen? What might happen next? What action to take?

Security
From device firmware to cloud access.

I sometimes ask myself, how can someone troubleshoot what they never learned existed?

Why I Don’t Believe in “No-Code IoT”

This may sound unpopular.

But I don’t believe IoT can be fully no-code.

IoT is physical.
It touches hardware.
It touches networks.
It touches real environments.

You need to understand flows.
You need to debug failures.
You need to trace where the data stops.

If there is no data on the dashboard, is it the platform’s fault?

Or is the sensor dead?
Is the firmware corrupted?
Is the network unstable?
Is the battery empty?

If everything is hidden, you don’t know where to look.

That’s dangerous.

PaaS Is Harder, and That’s the Point

Favoriot is closer to a platform than an app.

Some people say it’s harder.
They are right.

But difficulty is not a flaw.
It is a teacher.

When students use a platform that requires them to configure devices, protocols, dashboards, and rules, they are forced to think.

Why did I choose MQTT here?
Why is my data not arriving?
Why does this alert trigger late?

Those questions build engineers.
Not button-clickers.

Analytics Is Where IoT Becomes Meaningful

I always remind students.

Collecting data is not the goal.
Understanding data is.

IoT analytics moves in stages.

First, you look back.
What happened?

Then you ask why.
What caused it?

Then you look ahead.
What might happen next?

Finally, you decide.
What should I do about it?

This is why we built Favoriot Intelligence inside the platform.

Not as a separate system.
Not as an external tool.

One pipeline.
End-to-end.

Data comes in.
Insights come out.
Actions happen.

This is where IoT starts to feel alive.

AIoT and the Future Students Will Walk Into

Things are shifting fast.

AI is no longer optional.
Edge intelligence is becoming real.

Models trained in the cloud.
Inference pushed to devices.
Decisions made closer to reality.

Students who only learned drag-and-drop dashboards will struggle here.

Students who understand flows, stacks, and constraints will adapt.

That difference matters.

Why I Care So Much About This

People sometimes ask why I spend so much time with universities.

The answer is simple.

I have seen graduates enter the industry confused.
Overwhelmed.
Afraid of real systems.

I don’t want that.

I want students who can walk into a telco, a factory, a startup, or a smart city project and say,

“I don’t know this tool yet, but I understand how IoT works.”

That confidence changes careers.

Practical Tips for Students Learning IoT

Let me leave you with a few honest tips.

Learn beyond your demo
A working demo is not the finish line. Ask what breaks when conditions change.

Trace data end-to-end
Always ask where data starts and where it stops.

Understand at least one protocol deeply
MQTT, CoAP, or REST. Know how it behaves when networks fail.

Build dashboards yourself
Dragging widgets teaches more than screenshots.

Make something fail on purpose
Turn off WiFi. Drain the battery. Observe what happens.

Learn one platform properly
Not ten platforms shallowly.

Document your struggle
Others learn from your mistakes.

A Quiet Invitation

If you are a student reading this and you feel slightly uncomfortable right now, that’s good.

It means you are growing.

If you are a lecturer, consider whether your students are learning embedded systems or IoT.

And if you are one of the early builders willing to share your Favoriot project, you are not just building a system.

You are building a community.

Someone has to be first.

I hope you’ll be one of them.

I would love to hear your thoughts, your struggles, or your stories.
Leave a comment. Let’s talk.

We Gave Our Favoriot IoT Platform Away for Free—Here’s What Actually Happened Next

A quiet, honest story about how Favoriot found its way

I remember the early days of Favoriot very clearly.
It started with a simple belief. If we make it free and easy, people will come. They will build. They will stay.

So we did exactly that.

We built the platform.
We opened the doors.
We told the world, “Come in. Try it. It’s free.”

And people did come. Students. Lecturers. Curious engineers. Friends I met during talks at universities. I would personally invite them. Sometimes I would even hand out complimentary access codes for a full year of the Beginner Plan.

I thought to myself, this is how adoption works.

But something felt off.

When subscribers were not really users

On paper, the numbers looked comforting.
Subscribers were growing. Accounts were created. Dashboards were viewed.

But deep down, I knew something was missing.

Why are so many accounts quiet?
Why do I see logins, but no devices connected?
Why do dashboards stay empty?

That was my first hard lesson.

A subscriber is not always a user.
And a user is not always a builder.

Many people came just to look around. They clicked. They browsed. Then they left. Some even won vouchers but never built a single IoT project.

It hurt a little. Not because of revenue. But because I wanted Favoriot to be used. I wanted it to matter.

The wrong assumption about behaviour

I used to think users would log in every day, tweak dashboards, run experiments.

Reality taught me otherwise.

A typical IoT builder behaves differently.

Once the device connects and the data flows, they step back. They look at the dashboard occasionally. They only return when something breaks or when the project evolves.

Students behave differently, too.

They come intensely during one semester. Final year project season. Late nights. Panic. Excitement. Then, silence.

And to make it harder, many of them already knew other platforms. Some popular. Some free. Some are recommended by seniors.

Favoriot was often an unfamiliar name.

So ,how do you enter the education space when choice is already wide open?

Teaching before selling

I stopped pushing plans and started focusing on learning.

We introduced public IoT training. Beginner. Advanced. Mastering IoT.
Lecturers started attending. Some became trainers themselves. They went back to their universities and taught students using what they learned.

That felt good.

Then we went a step further.

Professional certificates.
Either embedded into the curriculum or offered as short intensive training. Students could finish the course and receive a certificate, or sit for an exam and earn a more formal credential.

Interest grew. Enquiries came in.

But adoption was still slow.

Universities move carefully. Curriculum changes go through committees, boards, and senate meetings. Nothing moves overnight.

I had to learn patience.

Labs instead of just logins

That’s when we bundled everything.

Not just software.
Not just subscriptions.

We created labs.

An IoT Lab with devices, Beginner subscriptions, training, and ready-to-use kits like indoor air quality monitoring.

An AIoT Lab with more advanced tools. Edge devices. Developer Plan access. Machine learning features. Analytics. A space for research, experimentation, and deeper thinking.

Suddenly, Favoriot was no longer just a platform.
It became an environment.

That changed the conversation.

Why Favoriot stayed a platform, not an app

People sometimes ask me, why not make Favoriot simpler? Why not hide everything?

Because IoT is not simple.

If everything is hidden, nothing is learned.

Favoriot is a Platform-as-a-Service by choice. Builders can see the flow. Devices. Protocols. Data ingestion. Visualisation. Rules. Actions.

When something fails, they learn how to troubleshoot.

When they graduate, they carry understanding, not just button-clicking habits.

That’s the skill that survives in the real world.

The restaurant analogy that finally made sense

One day, while explaining our plans, I caught myself using a food analogy. And suddenly, everything clicked.

The Free Plan is peeking into a restaurant.
You look at the menu. You walk around. Then you leave.

The Lite Plan is tasting the food.
You sit down. You try a dish. You smile.

The Beginner Plan is a full meal.
You are satisfied. You build. You complete your project.

The Developer Plan owns the kitchen.
You cook. You create menus. You serve your own customers.

The Enterprise Plan owns the whole restaurant.
You decide everything. Security. Scale. Who gets served and how.

When I explained it this way, people finally nodded.

Overseas users and a quiet mystery

Here’s something that still amazes me.

Favoriot has users from more than 130 countries.
Yet most revenue comes from Malaysia.

How did they even find us?

Blogs.
E-books.
Social media posts.
YouTube. TikTok. Facebook groups.

They came. They explored. Most stayed free.

And that taught me another lesson.

Free users overseas were often explorers. Platform shoppers. Comparing interfaces. Looking around.

Not every visitor is ready to commit.

And that’s okay.

Personas matter more than pricing

Over the years, I stopped blaming pricing.

Instead, I studied personas.

Browsers.
Tasters.
Builders.
Integrators.
Operators.

Each one needs a different message.

Each one enters the journey at a different door.

And that was the missing piece all along.

Partners instead of long walks alone

I also realised something else.

We cannot do everything ourselves.

We do not have endless arms and legs to reach every market.

So we shifted.

System integrators.
Hardware partners.
Domain experts.
Universities.

They already speak the language of their customers. We just give them the kitchen.

That felt right.

AI as my late-night thinking partner

I will admit this honestly.

AI changed how I think.

When I was in corporate life, clarity came from meetings, workshops, and committees.

Today, clarity comes at night. Quietly. One question at a time.

I talk.
I reflect.
I get challenged.

Not every suggestion is usable. But every session sharpens my thinking.

Sometimes, you just need a friend who listens without ego.

Community as the long game

Lately, I spend more time on LinkedIn.

I see students from India proudly showing their projects. Some use other platforms. Some barely send data to the cloud.

I comment. I encourage. I invite.

“Try Favoriot.”
“Show us your project.”
“We will feature your story.”

Because visibility matters.

When builders are seen, they stay longer.

And when they grow, they remember.

This journey is still unfolding

Go-to-market favoriot

Favoriot did not arrive here overnight.
It took years of confusion, wrong assumptions, quiet learning, and small corrections.

But today, the path feels clearer.

Free curiosity has a place.
So does tasting.
So does building.
So does owning the kitchen.

If you are just browsing, welcome.
If you are ready to build, stay.
If you want to serve others, let’s talk.

And if you are a student building your first IoT project somewhere in the world, remember this.

The platform you choose today might become the one you trust tomorrow.

I would love to hear your thoughts.
Where are you in this journey?
Peeking, tasting, cooking, or running the whole place?

Leave a comment. Let’s talk.

Favoriot’s Journey: Lessons from Lord of the Rings

The journey of Favoriot, from its earliest days to where it stands today, mirrors The Lord of the Rings Trilogy in a way that feels less like fantasy and more like lived experience.

Not because of epic battles or dramatic villains, but because both stories are really about endurance, pivots, and choosing to continue when the original plan no longer fits the road ahead.

A Journey That Did Not Start With a Grand Map

When Frodo left the Shire, there was no detailed map to Mount Doom. Gandalf did not hand him a ten-year plan. The mission evolved as dangers revealed themselves.

Favoriot began the same way.

The early vision was simple. Build an IoT platform that works. One that local engineers, researchers, and institutions could rely on. What came next was not a straight line. The platform did not arrive fully formed. It grew through experiments, false starts, and product decisions that looked right at the time but later needed rethinking.

Like Middle-earth, the terrain kept changing.

Products as Paths, Not Destinations

In The Lord of the Rings, the Fellowship does not walk a single road. They split. They detour. Some paths fail. Others reveal their purpose much later.

Favoriot’s products followed the same rhythm.

Early versions focused heavily on basic device connectivity and dashboards. That was the Shire phase. Simple. Familiar. Necessary.

As real customers arrived, the needs shifted. Monitoring alone was not enough. Scale introduced complexity. Rules became more complicated to manage. Alerts became noisy. What worked for a pilot did not hold up in production.

That forced pivots.

  • From simple dashboards to structured data models
  • From manual rules to more intelligent behaviour detection
  • From pure IoT to AI-assisted decision support
  • From cloud-only thinking to edge-aware architectures

Each pivot felt like leaving a known path and stepping into uncertainty. Some features were retired quietly. Others were reshaped instead of discarded. Just as characters outgrow their early roles, products evolve because the journey demands it.

The Cost of Carrying Too Much

Frodo’s burden was not the distance. It was the Ring.

For Favoriot, the “Ring” often took the form of technical debt, early assumptions, and customer expectations set too soon. Decisions made for speed later demanded patience to untangle. Features built for one market created friction in another. Supporting early users while reworking the core tested both systems and people.

Letting go was hard.

Just as Frodo struggled to release the Ring, teams struggle to let go of products they worked hard to build. Yet progress required accepting that not everything belongs in the final version.

Splitting the Fellowship to Survive

The Fellowship did not stay together because it looked nice. It split because survival required it.

Favoriot’s journey did the same. Engineering focused on stability, while product teams listened closely to users. Business teams dealt with timing, cash flow, and long sales cycles. Partnerships opened doors while internal teams strengthened the foundation.

At times, it felt fragmented. In reality, it was a focus.

Each group carried a different part of the burden. No single team saw the whole picture at all times. Trust became the glue.

Long Stretches Without Applause

Middle-earth did not pause to celebrate milestones. Neither did the market.

There were long periods where progress was invisible from the outside. No launches. No announcements. Just refactoring, rewriting, rebuilding. Customers rarely see this phase, yet it defines whether a platform survives.

Favoriot lived in this space for years.

Quiet work. Fewer shortcuts. Many trade-offs. The kind of progress that feels slow until one day it becomes evident that the platform is stronger, calmer, and more reliable than before.

When the Mission Changes the People

By the end of the trilogy, Frodo was not chasing adventure. He was carrying wisdom earned through pain and persistence.

Favoriot’s journey shaped its people the same way.

Engineers learned restraint, not just speed. Product teams learned when to say no. Leaders learned that timing matters as much as vision. The company knew that building trust outlasts chasing trends.

The platform today is not just more capable; it is also more capable. It is more deliberate.

Not Glory, But Completion

Destroying the Ring was not a victory parade. It was relief. Completion.

Favoriot’s goal has never been to build everything or to shout the loudest. It has been to finish what was started. A platform that can grow with its users. A system that learns instead of overwhelming. A foundation that can support the next chapter without collapsing under its own weight.

That goal shaped every pivot.

The Quiet Parallel

Frodo was not the strongest.
Favoriot did not have the most significant budget.
Neither took the shortest route.

Yet both stories prove the same point.

Lasting impact rarely comes from perfect plans. It comes from adjusting without losing purpose, letting go without giving up, and continuing to walk when turning back feels easier.

That is the shared truth between Middle-earth and Favoriot’s journey.
A long road.
Many pivots.
One mission that refused to be abandoned.

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.

Reflecting on a Grounded 2025: Lessons from Favoriot’s Journey

I am writing this ten days before 2025 comes to an end.

When I look back at the year, it does not feel loud. It does not feel dramatic. It feels focused. Demanding. Grounded. A year where most of my time, energy, and thinking revolved around one thing only: Favoriot.

If I am being honest, 2025 was not a year of balance. It was a year of commitment.

Most of my days were spent at the office. And when I was not physically there, my mind was still working on Favoriot. Nights. Weekends. Quiet moments that could have been rest often turned into planning or problem-solving. I did not spend much time on myself personally. There were no real holidays. The only breaks I had were during overseas business trips, and even then, work followed me closely.

I told myself more than once, This is not a sacrifice. This is a choice.

And I am at peace with that choice.

Fewer Invitations and a Shift in How We Connect

One noticeable change in 2025 was the drop in invitations from universities and public conferences. Many engagements that used to be physical moved online. Meetings became links. Conversations became scheduled time slots on screens.

I realised something about myself quite clearly this year.

I do not enjoy online meetings anymore.

They are convenient, but they remove the human layer. The casual chats before meetings start. The spontaneous conversations after sessions end. The subtle signals that build trust faster than formal presentations ever can.

I still prefer face-to-face meetings. They feel more honest. Better for networking. Better for understanding people beyond their titles.

Public conferences were fewer as well. Part of it could be the current spotlight on AI. IoT felt quieter this year, almost like it had stepped back from centre stage. I was not bothered by it. I was observant.

Trends move quickly. Real work moves steadily.

Why Panel Sessions Still Matter to Me

While formal speaking invitations slowed, one format still felt right to me: panel sessions.

No slides. No heavy preparation. Just conversations.

Sitting on stage, exchanging views, listening, responding, sometimes disagreeing politely. That feels closer to how decisions are made in real life.

I often think that insight shows up better in dialogue than in bullet points.

That belief stayed strong in 2025.

A Year Focused on Partnerships

Behind the scenes, 2025 was anything but quiet.

We spent a significant amount of time building partnerships. By the end of the year, we had signed MOUs with more than 40 partners across 15 countries. Our original target was 25 countries, so on paper, we fell short geographically.

But numbers do not tell the whole story.

I have learned that more partners do not automatically mean more revenue or more projects. Partnerships only matter when they are actively engaged, aligned, and nurtured.

Signing is easy. Building trust takes time.

Some partnerships moved faster. Some are still warming up. Some will likely take longer to show results. That is the nature of building across borders.

This year reminded me that ecosystems are built patiently, not collected quickly.

When People Find You on Their Own

One encouraging pattern this year was how people and companies started approaching us unexpectedly.

Each time, I asked the same question. “How did you find us?”

The answer was often simple. They searched online. They did their own research. They were surprised to discover an IoT platform company operating from this region.

That always made me pause.

Years of writing, sharing, and building quietly compound over time. Visibility does not always arrive with announcements. Sometimes it comes as an unexpected email or message.

That is when you realise the work has travelled further than you thought.

Fewer Projects, Fewer Trainings, a Cautious Market

Not everything grew this year.

Real IoT projects were fewer compared to previous years. IoT training numbers dropped as well. In-house training, which used to scale better, became harder to secure. We relied more on public training sessions, which are always challenging when it comes to attendance.

The market felt cautious.

Budgets were tighter. Decisions took longer. Interest was still there, but commitment required more patience.

There were moments when I questioned the pace. Is this a temporary slowdown, or is the market resetting itself?

Perhaps it is both.

Shifting My Focus Between Industry Associations

This year, I was less active in the Malaysia Smart City Alliance Association.

At the same time, I became more involved with the Malaysia IoT Association, partly due to my role as Vice-Chairman.

More importantly, MyIoTA’s Smart City Nexus activities align closely with the reasons I joined the association. The Nexus focuses on bringing members’ solutions directly to local councils. It creates a practical space for business matching, not just discussion.

That matters to me, and I plan to be more active there moving into 2026.

Favoriot Sembang Santai Podcast: Keeping Conversations Human

Another meaningful chapter in 2025 was the start of the Favoriot Sembang Santai.

We started the podcast in February 2025, and by December, we had reached Episode 38.

The reason was simple. I wanted a space for honest conversations. No scripts. No slides. No pressure to sound formal. Just honest discussions about Favoriot’s journey and what we were seeing in the IoT space.

The primary host is Zura Huzali, and I serve as the primary guest and speaker. The chemistry works because it feels natural. Curious questions. Straight answers. Occasional debates. Plenty of laughter.

The topics evolved naturally from Favoriot’s story into broader themes such as AI, robotics, satellite IoT, and Ambient IoT. Not as buzzwords, but as technologies we were trying to make practical sense of.

Anyone who misses the live sessions can catch the recordings on YouTube, Spotify, and Amazon Music.

The podcast will continue throughout 2026. For me, it is a long conversation, not a series.

TikTok Live on IoT Man: Ask Me Anything

Alongside the podcast, we also started doing casual TikTok Live sessions on the IoT Man channel.

The central theme is simple. “Ask Me Anything.”

No agenda. No slides. Just live questions and real-time answers. What excites me about these sessions is that they capture a different segment of listeners. Shorter attention spans. Younger audiences. People who may not sit through a long podcast but are curious enough to drop in and ask.

It feels raw. Immediate. Human.

Sometimes the most honest questions come without preparation.

Working With AI to Prepare for 2026

As 2025 draws to a close, one personal highlight has been working with my AI companion.

Not to replace thinking, but to sharpen it.

I spent many late nights shaping 2026 playbooks. How we approach the market. How we engage customers. How we manage partners. New business models. New IoT solution ideas.

The picture ahead feels clearer now.

Looking Forward

2025 was not flashy. It did not come with loud milestones.

But it mattered.

It tested focus. It strengthened conviction. It prepared the ground.

I am genuinely excited about 2026. The plans are clearer. The energy feels different. I hope the long-standing plans around IoT certifications with universities will finally become a reality.

Here is to a better, steadier, and more rewarding year ahead.

I would love to hear how your 2025 has been. Share your reflections in the comments.