I help startups and scale-ups hire great software engineers 📈 | Building Remotesome as a Remote First Company 🌍 | Helped 100+ companies hire great software engineering talent 🚀

Founder and product builder. Previously built Remotesome, helping companies hire engineers globally before remote work was mainstream.

Now exploring new ideas and opportunities to build something new. Interested in early-stage products, hiring, and modern work. Always open to good conversations.

Andrej Fodor

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

Doing less is not how you stay competitive. Doing more—better, faster, and more intelligently—is.

There was a time when “doing one thing only” was enough. Today, it is not.

The market has changed. Customer expectations have changed. The speed of business has changed. And companies that want to remain competitive are not being asked to deliver less. They are being asked to deliver more:

more speed
more precision
more value
more personalization
more innovation
more consistency

So why do some people still act as if the answer is to reduce capability?

That logic does not hold.

If a company must provide more to its customers, then the force behind that company must also become more powerful. Stronger systems. Better tools. Broader capability. Deeper execution.

This is simple physics, simple business, and simple common sense:

You do not get greater output from weaker input.

Take mining as an example. A drilling machine does not become more effective by becoming less capable. It becomes more effective through greater power, better engineering, more refined components, and a system designed to perform under pressure. (This principle also applies to learning and re-education).

The same applies to people, teams, and companies.

If you want stronger outcomes, you need stronger internal capacity.

That does not mean random busyness. It does not mean shallow multitasking. And it certainly does not mean trying to do everything without structure.

It means building real capability.

It means using the right tools to expand what you can do.

It means becoming more efficient, more adaptive, and more powerful in execution.

Because the reality is this:

The world is not rewarding those who do less.
It is rewarding those who can handle more complexity, solve more problems, and create more value.

Specialization still matters. Of course it does.
But specialization alone is no longer enough.

Today, the highest-performing individuals and organizations combine:

depth + range
focus + adaptability
expertise + leverage

That is where real advantage is built.

If you want better outcomes, you must create a stronger engine behind them.

That is true in business.
That is true in technology.
And that is true in life.

If you want more results, invest in the force behind it.

#Leadership #BusinessStrategy #Execution #AI #Innovation #Productivity #Growth #Startups #Entrepreneurship

Founder of LunarTech, SeleneX, Nomad and Blackfyre

The most valuable skill you can build is learning how to learn. Not just learning one subject, tool, or profession, but mastering the process of learning itself.

Too many people treat learning as if it has an expiration date. They finish school, enter a job, and slowly begin to stagnate. But the world does not stagnate with them. Technology keeps evolving. Industry keeps changing. Business keeps moving. So anyone who stops learning is not standing still. They are falling behind.

That is why continuous learning is no longer just a nice trait to have. It is one of the strongest forms of career security.

Learning how to learn is what keeps you adaptable, competitive, and relevant. It is the difference between reacting late and staying ahead. In today’s world, the “safe” path is not always the safest one. Often, the safer path is becoming someone who can continuously adapt, upskill, and grow.

This is also why working with startups can be one of the best ways to accelerate your growth. In startups, you do not just observe. You build. You solve. You adapt. You take ownership. You are exposed to execution, problem-solving, decision-making, and real-world complexity in a way that many traditional paths simply do not offer. That kind of environment forces growth and builds practical, lasting capability.

At its core, learning is a feedback loop: you try, observe, adjust, and try again. Real progress comes from active iteration, not passivity. People who know how to learn do not panic when something is unfamiliar. They understand that unfamiliarity is simply the first stage of mastery.

This is also why AI does not reduce the value of real expertise. It increases it. AI is an amplifier, but it still depends on people who can think clearly, ask the right questions, apply judgment, and adapt quickly. The people who benefit most will be those who have trained themselves to learn fast and evolve consistently.

The people who keep rising are rarely the ones who decided they were finished learning. They are the ones who stayed curious, disciplined, and open. They keep building new capability, and because of that, they keep creating value.
Learn how to learn, and you make every future skill easier to acquire.

#Learning #CareerGrowth #Startups #AI #FutureOfWork #Upskilling #Leadership #GrowthMindset #Innovation

Recent events have made one thing very clear: the current infrastructure is unreliable. It is not secure, not sustainable, and increasingly incapable of supporting even basic operations. If energy is unstable, nothing connects. Systems fail, coordination breaks, and those responsible for maintaining stability simply cannot do their job.

The modern world runs on connection — and connection runs on energy.

And today, that foundation is under pressure.

Global electricity demand is rising fast, driven by AI and digital infrastructure. Data centers already consume ~1.5% of global electricity and are expected to more than double by 2030. This is not linear growth — it’s exponential pressure on already weak systems.

If a single chokepoint can disrupt global energy supply, it exposes how fragile the system really is. No country should depend entirely on external flows for something this critical. Energy must become local, resilient, and controlled.

And this is where the current model breaks.

Continuing to rely on oil-heavy infrastructure in a world that requires stability and adaptability is becoming harder to justify. Clean energy already delivers ~40% of global electricity, and the shift is accelerating.

China produces ~30% of global renewable energy. Europe is nearing ~50%. Others are scaling aggressively.

This is what winning looks like: not more extraction — but better systems.

Solar, in particular, changes the equation. With modern technology, countries can generate energy locally at scale. Combined with nuclear for stability and AI-driven grids for optimization, this creates a system that is secure by design.

Because in the modern world, energy is not enough.
It must be secure, scalable, and intelligent.

Otherwise, nothing connects — and everything breaks.

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.

LaFamilia is live!

https://lafamilia.capital/

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.

Nomad and Rosendahl will be introduced at the same time.

LaFamilia will soon start accepting founders! 

LUNARTECH, its team, fellows and partners are welcome to apply!

A founder’s greatest risk is not always poor execution. Often, it is connection.

The more capable or less capable you become, the easier it is to treat your own standards as ordinary. You begin to assume that others see what you see, think with the same precision, or operate with the same urgency. That is where distortion enters. You overestimate certain people, underestimate others, and gradually make decisions against an internal model of the world rather than the world as it actually is.

In AI, this problem is even more pronounced because the market moves with exceptional speed, and the cost of misreading signals compounds quickly. Founders do not merely need more information. They need cleaner information. If the signals you are processing are unverified, low-quality, or shaped by noise, then your judgment deteriorates no matter how strong your intellect may be. Strategic energy is finite. Spending it on false signals, weak assumptions, or untested narratives is not just inefficient. It is expensive.

The most disciplined founders therefore become highly selective about inputs. They seek evidence, not comfort. They rely on case studies, market behavior, primary sources, customer signals, and hard numbers that withstand scrutiny. When information is incomplete, they do not rush to close the gap with confidence alone. They investigate. They compare. They challenge. They go deeper until the signal is strong enough to support action.

This matters even more in an era where AI can generate highly persuasive output at scale. Fluency is not the same as truth. A well-structured response is not the same as grounded analysis. Without domain expertise, external validation, and proper retrieval of relevant context, AI can produce confident error just as easily as useful insight. The principle is clear: any system, human or machine, performs better when it is grounded in reality.

Strong founders also understand that perspective itself is a strategic asset. Looking at a problem only through your own lens may feel efficient, but it often creates blind spots. The customer, the operator, the investor, the technical lead, and the market may each reveal something your own view cannot. Not every outside perspective should drive the decision, but every serious decision should be tested against more than one frame.

The founders who remain competitive are not necessarily the loudest, nor the most certain. They are the ones who continuously refine how they process feedback, how they filter noise, and how they update their internal models. Inflexibility in this environment is not conviction. It is fragility.

Staying connected to reality is not a soft skill. It is one of the highest forms of strategic discipline a founder can build.

The U.S. Bureau of Labor Statistics released its February 2026 Employment Situation report on March 6, confirming a sharp and unexpected deterioration in the labor market. Total nonfarm payroll employment edged down by 92,000 jobs, far below economists’ consensus forecast of roughly +50,000 to +60,000 and a stark reversal from January’s revised +126,000 gain.

At the same time, the unemployment rate jumped to 4.4% (up from 4.3% in January), with the number of unemployed persons holding steady around 7.6 million.

Job losses were widespread across nearly every major industry category. Health care shed 28,000 positions (largely due to strike activity), leisure and hospitality dropped 27,000, manufacturing, construction, and federal government all posted declines, while only financial activities and “other services” showed modest gains.

Many sectors have yet to feel any meaningful pressure. Those already experiencing cuts—health care, information, retail, and government—are still in the very early stages of what appears to be a broader adjustment. Revisions to prior months, strike effects, and seasonal factors notwithstanding, the underlying trend points to softening demand, cautious hiring, and the first clear cracks in a labor market that had been surprisingly resilient through late 2025.

Bottom line: February’s data is not an anomaly—it’s an early indicator. Expect further softening in the months ahead as the ripple effects reach untouched industries and deepen in those already impacted. The labor market is no longer just cooling; the first real signs of contraction are here.

At LUNARTECH and our other companies, we do as much as our partners allow to take care of this situation through a deliberate, multi-layered operational framework: AI-driven upskilling, fellowships, venture studios and many other programs.

New superpowers updates are available!

https://lnkd.in/eP5X9wwP

https://lnkd.in/e--mEBTe

LUNARTECH | LaFamilia

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Andrej Fodor

Vahe Aslanyan

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The Battle

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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);}Vahe Aslanyan's LinkedIn Performance

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