Marketing & Content | Passionate about storytelling through video content creation | Financial Advisor-turned-Content Creator | TikTok Creator (50k followers) | Part-Time YouTuber

Hi, I’m Novie!

I create content on TikTok and YouTube about freelancing, book takeaways, and digital tools that make work easier.

I also love teaming up with startups and solopreneurs to bring their ideas to life through content that connects.

Novie Rose Rianna Dizon

Novie Dizon

marketing, copy, and sales strategy for solopreneurs

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In the ever-changing world of tech I've carved a niche for myself as a committed Software Developer. My love of the subject has not just resulted in me accumulating readers of over one million on a variety of blogs as well as to launch 2023's most sought after Data Science Bootcamp. My academic background is solidified by an Bachelors in Computer Science which has been crucial in my work.

My method of developing websites is multi-faceted, making sure there's the seamless integration of aesthetics for the front end and robust back-end functions. This balanced approach has enabled me to design user experience that stands out connecting with users and meeting the business goals.

Collaboration is at the core of any successful project. I've had the honor in leading, and working with some of the most skilled teams in the business. Together, we've tackled problems with aplomb, streamlined workflows and incorporated technology into our decisions that are in perfect alignment with the overall goals of our projects.

Performance is not a matter of negotiation. Each component I'm involved with undergoes rigorous optimization procedures to ensure efficacy, scalability and reactivity. Each of the projects I've completed has served as a learning experience and has provided me with valuable lessons and enhancing my method further.

The concept of AI has always fascinated me. I've been pursuing AI's capabilities to improve the offerings of our products. The aim? Improved user satisfaction. The process of infusing AI into the process of optimizing products has been thrilling and I'm always watch for the next major breakthrough in this field.

Some of the milestones I treasure in my heart are co-founding the world's most prestigious Data Science Bootcamp in 2023 getting an appearance in the UBC Dean's List in 2022 and training more than 1000 developers since the year 2015. My contributions and work have also been acknowledged by industry leaders, including appearances in respected publications like Forbes.

In the words of industry experts I've had the pleasure to collaborate with, my commitment to digital excellence is evident. As Entrepreneur puts it, my approach to digital solutions is both innovative and grounded, striking the right balance between vision and execution.

My professional path is a combination of technical proficiency, a team spirit, and an unstoppable determination to achieve excellence. I invite you to join me to collaborate and take part in this thrilling digital journey.

Vahe Aslanyan

Success in leadership rarely comes from reacting emotionally to problems. It comes from building the right pipelines, the right systems, and the right habits to respond intelligently.

A problem appears. One person freezes. Another person studies it, breaks it down, looks at what others have done, finds the right tools, builds the missing pillars, and moves forward.

That is often the difference.

Most problems are not impossible. Most are simply unanswered.
Not because the solution does not exist.
Not because the talent is not there.

But because too many people stop too early.
They do not search deeply enough.
They do not ask for help.
They do not upgrade their tools.
They do not upskill.
They do not build the infrastructure needed to solve the problem properly.

In business, and frankly in almost any field, progress comes from a more disciplined approach:

First, understand the problem clearly.
Then analyze it without ego and without drama.
Then identify the tools, knowledge, people, and systems required to solve it.
Then start building.

Pillar by pillar.
Layer by layer.
Step by step.
That is how the house begins to stand.

The truth is, even the strongest team cannot operate well without the right instruments. In the modern era, this includes data, computation, workflows, feedback loops, and a mindset of continuous improvement. If you want modern results, you need modern methods.

Take something practical: building a drone.

If someone does not know how to solder, do they stop there? Or do they look for tutorials, learn from others, practice, fail, adjust, and practice again?
That is how capability is built.

Not through wishful thinking.
Not through waiting.
Through repetition, analysis, and iteration.

This principle applies almost everywhere:
When you do not know, learn.
When you fail, analyze.
When something is missing, build it.

When the path is unclear, create the structure that makes clarity possible.
A lot of people want outcomes.

Fewer are willing to build the foundations that make those outcomes possible.
But in the end, foundations are everything.

#Leadership #Execution #ProblemSolving #Innovation #GrowthMindset 
#DataDriven #SystemsThinking #BuildInPublic

Founder of LunarTech, SeleneX, Nomad and Midnight

Most people give far too much weight to short-term thinking.

And because of that, they often end up experiencing the exact consequences of their short-sightedness: lost opportunities, stalled growth, and unrealized potential.

One clear example? Learning.

Learning new tools.
Learning new skills.
Learning how to operate in a new field.

People try once.
They fail once.
And they stop.

But failure rarely comes out of nowhere. It usually comes from lack of information, lack of structure, or lack of repetition. Yet instead of adjusting and continuing, many give too much power to the discomfort of the short term.

We are living in an era of noise, hype, and instant gratification. Quick wins are glorified. Long-term investment is underrated.

But everything meaningful operates on foundational principles.

If you consistently take the right steps, structured around solid principles, the cumulative effect will lead to progress. That’s not motivational talk — it’s mathematics. Repeated good inputs compound into good outputs. Repeated bad inputs compound into losses.

The key is balance.

If you focus only on the long term without short-term execution, you drift.
If you focus only on short-term wins, you collapse strategically.

Sustainable success comes from balancing both.

Take artificial intelligence as an example.

Many people say they want to learn AI tools. They try one tool for two or three hours. It feels confusing. They give up.

But today, access to information has never been greater. Access to powerful assistance has never been greater. The leverage available to individuals has never been greater.

Information is power.
Applied information is transformation.

If you fail for three hours, five hours — that is not a signal to quit. It is a signal to refine your learning process.

Learn how to learn.

Short-term discomfort is the price of long-term capability.

And long-term capability is what wins.

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No matter what you do for some people, they will always ask for more.

It doesn’t matter if you build their daughter a hotel, or create the perfect venue for their “historic” meetings with fellow prime ministers. It doesn’t matter if you build the most beautiful buildings in the most beautiful places. 

For some personalities, none of it is ever enough—because the goal was never appreciation. The goal was extraction.
And as time goes on, the demands don’t just increase—they get more illogical.

These are often the same people who try to “bend gravity” by lying. But you can’t interpret your way out of gravity. You can’t manipulate reality forever. Eventually, the mental energy required to maintain the lie becomes exhausting—and you start seeing it in their faces.

Meanwhile, the other side—the side dealing in reality—keeps gaining an “infinite” set of skills: negotiating, building, solving, adapting, creating real partnerships.

You can see the same pattern at scale between nations, too. If a country repeatedly changes standards, rewrites rules, and behaves inconsistently, partners start treating it as unreliable.

For example, if the United States constantly introduces new standards and shifts the playing field, Europe can begin to view the U.S. as an untrustworthy partner—and respond by taking tighter control of its own position and reducing dependency.

The principle is the same everywhere:

When you become difficult to deal with, people move on.

They choose partners who get the job done—because the unreliable partner delivers more problems than results.

And in the end, the maintenance cost of the lie becomes too heavy to carry.

You see it in national debt. You see it in low revenue versus high obligations. You see it in infrastructure failures, system breakdowns, and compounding inefficiencies.

At first, it looks manageable.

But that’s the trap: it’s not the end—it’s the beginning.

When you hold yourself for a fool, you gently open the door for fragmentation. 

It begins softly: you step back from what feels heavy, create a quiet distance, and soften reality with gentle thoughts—“perhaps it was never truly mine” or “this doesn’t really affect me.” Over time that distance grows, one small compromise leads to another, and wholeness quietly begins to ease apart.

This gentle distancing, whether in people or nations, often signals a quiet admission that something has been lost. Losing half one’s real estate remains a real change, even if we reassure ourselves it was never ours to keep. There is no need for celebration or blame—only a calm recognition that land, resources, and possibilities have slipped from the whole. From there, manufacturing may slow, true innovation grows rare, and new technologies sometimes quietly drain a country’s strength. 

The same fragmentation appears most clearly in how leaders relate to their groups. Strong leaders stay whole under pressure. They remain connected, flexible, and open to many paths, giving themselves time to adapt without breaking. 

Yet when a leader begins to draw smaller circles—thinking “these are not my people,” “this group is not mine,” “that part no longer belongs”—the center softens. Starting to lose groups or allowing internal division through such quiet disowning is not clarity or protection; it simply invites fragmentation. This truth holds only for the leader of the group. It does not apply to those outside or unconnected, who carry no responsibility for the whole.

Downplaying our own strength can invite the same gentle fracture. It is like a diamond passing through iron as smoothly as a Stanley knife through paper. By softening our sense of power, we make what was solid feel more fragile than intended.

In the end, behavior that leads to the loss of half one’s real estate—or half one’s sovereignty, productivity, or future—does not reflect enduring strength. Strong leaders remain whole.

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We are just getting started.

Claude Code: The Complete Handbook — a three-part enterprise whitepaper covering AI-assisted development from first session to full agent orchestration. Most developers are using AI coding tools at 10% of their capability.

Not because the tools are limited. Because the inputs are. This handbook exists to help you become a capable participant in that transition.

Read more: https://lnkd.in/eVvj6fqU

LUNARTECH, SeleneX, LaFamilia are hiring!

Data science and data analysis aren’t overrated — they’re just misunderstood. Every now and then you’ll hear someone say, “data science is dead” or “analysis doesn’t matter anymore,” and it usually comes from the same mindset that treats math like it’s only “1 + 1” and treats data like it’s just a messy spreadsheet you pass around.

In reality, data scientists remain in high demand for a simple reason: most people still don’t know how to handle data properly. Clean, reliable data isn’t a “nice to have.” It’s the foundation of good decisions, scalable products, and trustworthy AI. When data is collected and structured well, everything moves faster — teams stop arguing about what’s true, leaders can trust the numbers, and organizations stop bleeding time on confusion and rework.

A strong data scientist brings order to chaos. They make data cleaner, more organized, reproducible, and usable across teams. That discipline shows up in their working style, and senior stakeholders recognize it immediately because it reduces risk and increases execution speed. That’s why good data people keep getting hired — and why they will continue to get hired.

What’s actually happening is that the field is evolving. The bar is rising. Data analysts and data scientists need more skills than before, especially around experimentation, automation, and production-level thinking. But that doesn’t make the role less valuable — it makes the best people in the role even more valuable.

So don’t let the loud noise distort your view. Data science is still one of the most powerful careers for one core reason: it builds a mindset. It trains you to think in fundamentals, to reason clearly with evidence, and to build AI and decision systems on solid ground. That’s not going away — it’s becoming more important.

Google has long been regarded as one of the world’s most formidable companies. Lately, though, it feels like that reputation is being tested—especially in AI—because the scale of investment and the clarity of results don’t always seem to match.

Here’s what’s striking: the publicly reported capex numbers tied to AI infrastructure (data centers, compute, networking) have become enormous:

2024 capex: ~$52.5B
2025 capex: ~$91.4B
2026 capex (forecast): $175B–$185B

When a company is allocating capital at that magnitude, the expectation is straightforward: breakthrough capability, consistent execution, and clear leadership. If the perceived output feels uneven compared to peers, the disappointment isn’t “hate”—it’s simply the gap between what should be possible and what’s actually shipping.

And this isn’t a “Google-only” pattern. We’re seeing similar whiplash elsewhere.

Take xAI: it entered the public imagination as an “all-in” frontier AI company. Yet recent reporting suggests xAI (alongside SpaceX) is competing in a Pentagon contest focused on voice-controlled autonomous drone swarming software. (Really?)

Even if you’re supportive of defense innovation, it raises a fair question: why divert attention from the core AI roadmap when the product and research race is moving this fast?

It’s a bit like owning a Ferrari and choosing to use it for errands around the block—technically valid, strategically confusing.

What's next in this circus? US using Defense act to order Anduril to ditch drones for the ultimate stealth tech: AI condoms that deploy faster than a Falcon 9, shielding us from humanity's worst?

Unlike many beautiful things in life, long-term thinking gives you exponential returns. Short-term thinking gives you very little.

Many people assume that when a new technology is released, the adoption curve is equal for everyone — that all countries, companies, and individuals benefit at roughly the same rate.

In reality, that’s not how it works.

What truly determines impact is not the technology itself, but the **system behind it** — the infrastructure, the scale, and the ability to operationalize it.

Take China as an example. With around 1.4–1.5 billion people and massive investment in robotics and automation, they have built systems that manufacture at extraordinary speed and scale. Their shipbuilding capacity is among the fastest in the world. When they decide to build something, they build it — quickly and at scale.

Even in AI, despite chip restrictions, they continue training powerful models. Why? Because systems matter more than isolated constraints.

Now look at AI adoption in the United States. Adoption is also strong. But here’s the key point:

If a country with 200+ million people adopts AI at even a small percentage, that can result in millions of deployed AI agents across the economy.

Now compare that to a country with 3 million people. Even if adoption rates are similar percentage-wise, the absolute number of deployed systems will be dramatically lower.

Scale compounds.

So when a technology is open-sourced, it does not mean everyone benefits equally.

The ones who benefit the most are those who already have:

* Systems
* Infrastructure
* Talent pipelines
* Capital
* Operational discipline

Open access does not create equal outcomes. It amplifies existing capability.

And this brings us to something important:

There is a fundamental difference between system builders and schemers.

A system builder:

* Thinks long term
* Invests in infrastructure
* Builds durable mechanisms
* Plays compounding games

A schemer:

* Thinks short term
* Looks for arbitrage
* Exploits temporary gaps
* Plays extraction games

They are fundamentally different.

A system builder never becomes a schemer.
A schemer never becomes a system builder.

Their goals, incentives, and time horizons are completely different.

And in the age of AI, systems win.

There’s this popular idea that success comes from choosing one path and committing to it. But when you look at what actually works in the real world, the pattern is different: hybrids win.

Hybrid cars outperform single-approach engines across use cases. Hybrid UAVs extend range and resilience. And the same logic shows up in how the strongest modern tech companies operate.

Big tech doesn’t grow purely by “deciding” and executing one internal roadmap. They run a hybrid capital strategy:

Venture-style behavior: placing many bets, exploring new products, spinning up new teams, testing emerging tech

Private equity–style behavior: consolidating, integrating, optimizing, cutting waste, scaling what works

That’s why they can accelerate so aggressively. They don’t just build—they acquire. Over time, that becomes a compounding machine: distribution + product + talent + IP + market access, repeated again and again.

And there’s a third force in the background that makes the whole thing even more powerful: banks and credit markets. With financing available at the right scale, the system becomes a three-part engine:

Venture (discovery and options)

Private equity (execution and efficiency)

Banks (leverage and velocity)

That “triquetra” is what lets a single entity behave like an ecosystem—constantly absorbing new capability, strengthening the core, and repeating the cycle. And when you do that well, the outcome is exactly what you see in the markets: not a fragile one-time spike, but a long compounding curve.

You can only tell whether something truly makes sense—and you can only make a good decision—if you have enough data.

At some point, you have to step outside your own perspective, suspend judgment, and genuinely consider another person’s viewpoint. Then you reconnect that viewpoint to the facts. When perspective and evidence align, you can finally tell whether you’re looking at a hallucination—a persuasive but false narrative—or something grounded in how the world actually works.

That’s why data matters. And that’s why data engineering, data analysis, and data science aren’t buzzwords—they’re the practical machinery that turns messy reality into decisions you can defend.

Data is captured reality: events, behaviors, measurements, outcomes. But raw data is rarely decision-ready. It’s often incomplete, noisy, delayed, biased by how it was collected, or missing context. A metric without a clear definition, timeframe, or denominator can create confidence where none is deserved. That’s how people end up “connecting dots” that were never connected in the first place.

Data engineering is what makes data trustworthy and usable. It ensures the same entity means the same thing everywhere, the same metric is computed the same way every time, and the pipeline is stable enough that you’re not rebuilding the truth each week. Without solid engineering, analysis becomes a fragile story built on inconsistent inputs.

Data analysis is where reliable data becomes understanding. It forces you to define the question precisely, choose the right comparisons, and separate signal from coincidence. It helps you see what changed, what stayed stable, and what evidence would actually change your mind.

Data science goes one step further: it makes prediction and testing systematic. It helps you estimate impact, quantify uncertainty, and decide based on expected outcomes rather than anecdotes. Instead of “I feel like this will work,” you move toward “If we do X, we expect Y, and here’s how we’ll measure it.”

When these layers work together, your thinking changes. You stop arguing opinions and start arguing definitions. You stop chasing narratives and start testing claims. You become harder to manipulate, because your confidence is tied to evidence, not emotion.

Think of the world as a library. Some people walk through it once and call it boring. Others learn how to use it—how to extract signal from noise—and they upgrade their ability to decide. Most bad decisions aren’t caused by lack of intelligence; they’re caused by missing data, weak structure, or refusing to do the work.

So the real question is simple: if the tools are everywhere, why do so many people still avoid the library?

People don’t realize this: once a technology reaches a critical core, the game changes.

At that point, it’s no longer about whether development can continue — it’s about how it will continue. The capabilities, tooling, talent, and infrastructure are already in place. As long as people can operate logically, progress doesn’t “stop.” It reroutes, adapts, and often accelerates.

And here’s the part that gets misunderstood:

If your intention (explicit or implicit) is to “box in” AI through pressure-heavy regulation, you might be doing the opposite of what you think you’re doing.
Pressure doesn’t always weaken a system.

Sometimes it compresses it into something denser.

If you build a rigid block around a force that’s trying to expand, you don’t eliminate the force — you increase internal strength. And once it breaks through, it doesn’t come back gently. It comes back with momentum.

That’s why the real conversation shouldn’t be “regulate or don’t regulate.”
It should be:

Are we correctly modeling the action → consequence chain?

Because a lot of policy debates assume a simple relationship:

add rules → slow progress → reduce risk

But once the critical core exists, the relationship often looks more like:
add pressure → concentrate capability → increase asymmetry → escalate outcomes

So yes — governance matters.

But governance built on the wrong assumptions can create the exact future it was trying to prevent.

Actions and consequences are real.

The dangerous part is thinking they work the way we wish they did.

Startups do not grow in isolation. They grow inside systems, and the quality of that system determines the quality of outcomes.

A startup ecosystem is, at its core, an economic structure. When the right inputs are present, activity compounds. When they are missing, growth slows, capital becomes less efficient, talent becomes underutilized, and promising companies struggle to scale.

That is why ecosystem-building should be treated not as a symbolic exercise, but as a matter of financial and institutional design.

If a country wants its startup sector to mature, it needs more than ambitious founders. It needs the mechanics that allow innovation to move from idea to company, from company to revenue, and from revenue to scale. That includes aligned incentives, access to early-stage capital, strong technological capacity, and institutions that reduce friction rather than add to it.

And this is exactly why it is important to recognize that results are already beginning to show.

We are already seeing momentum. We are seeing stronger startup formation, more serious engagement around innovation, increasing attention to technology, and clearer signs that the ecosystem is gaining pace. These developments do not happen randomly. They are the result of people doing the work, building the foundations, and making the right decisions over time.

That deserves recognition.

The people who are helping shape the ecosystem, whether through policy, infrastructure, education, investment, or direct founder support, should be given proper credit. When positive results begin to emerge, it is important not only to analyze them, but also to acknowledge those who helped make them possible. Strong ecosystems are reinforced not only through capital and incentives, but also through credibility, consistency, and recognition of effective contribution.

At the same time, the fundamentals remain unchanged.

Without venture capital, Armenia’s startup ecosystem will continue to face limitations in how far and how quickly it can scale. Without strong technology infrastructure, commercialization pathways, and a sustained focus on reducing friction, momentum will be harder to convert into durable economic value.

So the path forward is clear:
recognize what is working,
give credit to the people and institutions producing results,
continue improving the financial mechanics,
and keep reducing the structural barriers that slow growth.

Because ecosystems scale when good decisions are repeated, when contributors are recognized, and when capital, talent, and infrastructure are aligned around long-term value creation.

Armenia is already showing promising signs. The task now is to build on that momentum intelligently, reward what works, and continue strengthening the system so that progress becomes compounding rather than temporary.

We love the narrative of the visionary founder scaling a company purely on gut instinct. But the reality of business growth isn't a flash of genius—it's data. It’s the ability to run rigorous experiments and operate like a scientist.

This is why data scientists remain your most powerful asset. However, they fall into two distinct camps:

The Task Executors: They pull the requested numbers, deliver the report, and stop there.
The System Thinkers: They see the entire machine. They don't just look at isolated metrics; they map the interconnected nodes to understand how the business actually functions.

To truly scale, you have to abandon rigid, static formulas—the kind of narrow algorithmic thinking that forces you to miss the forest for the trees. Instead, true system builders operate on a dynamic, continuous loop: Action → Result → Prior Data.

When you stop fixating on a single isolated event or a one-off anomaly, you finally see the entire concept. This holistic view is a massive competitive advantage. It ensures you don't waste precious time and resources over-indexing on a specific "node" that ultimately has zero impact on the broader machine.

Too often, founders filter out this broader data to react emotionally to a single event. Growth accelerates when you replace emotional reactions with numerical realities. You have to start asking system-level questions: If I tweak this specific variable, what is the exact downstream effect on the rest of the ecosystem?

It’s fine to have zero systems on day one. Scrappiness gets you off the ground. But the startups that actually survive are the ones that evolve into system builders.

Relying on individual heroics or isolated events will eventually cause you to stagnate. Building systems is what allows you to scale.

This is especially critical as AI disrupts traditional, siloed expertise. If your business relies solely on the isolated "genius" of a few individuals, you are vulnerable. System builders win because they engineer repeatable processes that don't rely on a single point of failure. They scale faster, build bigger, and ultimately have the freedom to do more.

LunarTech is just getting started—and the best is yet to come.

We’ve spent countless hours building the foundation: refining our technology, testing ideas, and learning from every step. Now, it’s time to move forward with purpose.

Our mission isn’t just growth—it’s progress. Every line of code, every product iteration, every strategy we implement is designed to create solutions that matter, push boundaries, and redefine what’s possible in our industry.

To everyone following our journey: this is more than just progress—it’s a movement. LunarTech is here to build systems that scale, solve real problems, and leave a lasting mark. And we’re just getting started.

Jack Dorsey just fired a warning shot across the bow of the global workforce, and the message is clear: adaptability is the new currency, and inflexibility is a liability no one can afford. When Block (owner of Square, Cash App) announced it was slashing 40% of its 10,000-person workforce because of AI, it became the largest percentage layoff of an S&P 500 company ever. People freaked out. 

The company’s stock jumped 20%.

But beneath the headlines, a profound structural shift is happening. This isn't simply a story about technology replacing us; it’s about a new landscape where those who hold too tightly to the old ways will face a difficult uphill climb, while those who learn to flow with the changes will find incredible new ways to thrive.

Here is my balanced, constructive take on what this actually means:
1) The Canary in the Evolving Workplace Jack has consistently been ahead of his time, and he’s early here, too. What he’s doing is the canary in the coal mine for businesses that struggle to pivot.

2) The Shifting Flavors of Layoffs We’re going to see AI-related restructuring accelerate, and it will clearly show the contrast between agility and resistance:

The Reactive: Using AI as a cover to reorganize teams that were already struggling to keep pace.
The Protective: Cutting costs to survive because a legacy business model no longer fits the modern market.
The Empowered: Pairing highly adaptable, curious employees with AI to beautifully amplify their natural potential.

3) Fluidity is the Foundation A transition this significant only works for companies built on a foundation of flexibility. It requires a flat structure, a focus on merit, and a culture that supports continuous learning. If a company relies heavily on rigid hierarchies and a "this is how we've always done it" mindset, forcing a pivot like this will just create overwhelming friction.

4) The "Inflexibility Boomerang" We will inevitably see highly bureaucratic companies try to copy this trend too quickly, resulting in a painful recoil. A rigid company might cut staff to plug in AI, but without a culture of adaptability and the right human talent to guide the tech, quality and customer care will dip.

5) The Market is Pricing in Agility The market rewarded Block, and people worried Wall Street was cheering against knowledge workers. It wasn't. The stock jump is simply the market placing a premium on a company’s ability to completely rethink its operating model and adapt its cost structure overnight.

6) The True Focus Isn't the Tech Does this worry me? Not if we approach it with a growth mindset. The structural impact of AI on digital work is real, and it is entirely valid to feel uneasy about it. But the greatest risk isn't the algorithm itself—it's the comfort zone. 

AI will undoubtedly disrupt the paths of those who resist change, but it is also opening up beautifully unexpected, unprecedented avenues for those willing to learn, unlearn, and grow.

Nomad and Rosendahl will be introduced at the same time.

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Novie Rose Rianna Dizon

Vahe Aslanyan

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The Battle

Novie Rose Rianna Dizon

Vahe Aslanyan

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Novie Rose Rianna Dizon's LinkedIn Performance

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