Sylvia was supposed to start Secondary School at the beginning of 2026, but that didn’t happen. Right now she should be sitting her end-of-second-term exams, but instead she’s on the pitch, training in soccer.  

She knelt to greet me, as is the custom in Uganda. It’s rare in Kampala, but her respect and her smile were genuine.  

At 15, a girl out of school is vulnerable. But on the turf, Sylvia is fearless. Sprinting, turning, passing — you’d think it was a boy. She’s the only girl out there, and she plays like she belongs.  

That’s exactly why I was at Soccer Stars Soccer Academy with Isaac, the Founder and Head Coach. We were there to introduce the Global Fast Fit Fitness Benchmark — GFF’s simple test to measure strength, speed, endurance, and agility but it takes discipline determination in young athletes.  

The moment I explained the drill, Sylvia stepped forward. “Now,” she said. No fear. No excuses.  

Coach Isaac gathered the teenagers to share their stories. Unlike big academies that charge hefty fees, Soccer Stars exists to lift children from the slums of Katwe, Kisenyi, and Kampala suburbs. Coach Isaac trains them and leverages their talent to secure school scholarships. Six girls have already gotten that chance and joined elite teams. Sylvia is still waiting for hers.  

Then came the GFF test.  

Sylvia ran the benchmark and scored 2:10 — two minutes and ten seconds.  

In GFF, we don’t just record times. We look for heart.  

And Sylvia showed it.  

GFF is about determination. It’s about showing up when no one expects you to. It’s about a 15-year-old girl choosing the ball over idleness, training over risk, and hope over circumstances.  

At Global Fast Fit, we believe every child deserves a chance to be tested, to be seen, and to be supported. Sylvia reminded us why we do this work.  

Congratulations, Sylvia. The benchmark is just the beginning.

When I started working as a trainer, I thought knowing exercises and how to perform them correctly was enough. With time, I realised that training is much bigger than lifting weights, counting repetitions, or telling someone to “push harder.”

In the few years I have worked with Global Fast Fit, training has been a continuous learning process for me. It has changed the way I look at exercise. I now see it as a very wide field that requires understanding, observation and, most importantly, the willingness to keep learning.

A good trainer needs to understand exercises—not just how to perform them, but why they are being prescribed, who they are appropriate for, how they can be modified, and what adaptations they are supposed to produce.

But exercise knowledge is only one part of the job.

A trainer should have some understanding of first aid and basic safety, a reasonable understanding of nutrition and diet, good communication skills, and the ability to work with different personalities and abilities.

We also need to understand our limits. You don't have to know everything. In fact, knowing when something is beyond your expertise is part of being a good trainer. Sometimes the most professional answer is, “I don't know, let me find out.”

Your client may know more than you think.

We are training in an age where information is everywhere. A client can watch a hundred videos about squats before they ever meet you. They may know about calories, protein, mobility, progressive overload or different training methods.

That shouldn't threaten a trainer.

Instead, it should challenge us to become better.

Clients don't necessarily expect us to know absolutely everything, but they expect us to be competent enough to understand what they already know, correct misconceptions where necessary, and add something valuable to the conversation.

You should be able to sit across from an informed client and have a meaningful conversation—not simply rely on your position as “the trainer.”

Don't become rigid.

Fitness is full of recycled thoughts.

“This exercise is always good.”

“That exercise is bad.”

“Everyone needs this workout.”

“This is the only way to lose weight.”

The more I learn, the more I realise that exercise is rarely that simple.

Every client is different. Their goals, abilities, limitations, lifestyle, experience and response to training can all be different. A trainer therefore needs to observe, question and adapt.

And sometimes, we need to accept that the programme isn't always the problem.

Take responsibility but do not take all the blame 

As trainers, we have a responsibility to provide good programmes, educate our clients, monitor progress and give appropriate guidance.But we don't live with our clients.

We cannot monitor what they eat 24 hours a day. We cannot watch how long they sleep. We cannot control whether they follow the programme when they leave the gym. We cannot force someone to recover properly, manage their stress or make better choices every day.

Sometimes a client isn't getting the expected results because of habits outside the training session.

That doesn't mean we should immediately blame the client. We should first ask ourselves whether we have done our part properly.

But once we have genuinely done our part, we also have to accept that the client has responsibility for their own results.

Don't beat yourself up over every outcome you cannot control.

Your job is to provide the knowledge, structure, guidance and support. The client still has to live the lifestyle.

Keep learning 

For me, this has been one of the biggest lessons from my time at Global Fast Fit. I have stopped seeing training as simply a collection of exercises. It is a field that demands curiosity.

Read. Ask questions. Observe other trainers. Study the human body. Learn from your clients. Challenge your own beliefs. Keep up with new information. And don't be afraid to change your mind when better knowledge comes along.

Most importantly, remember that being a trainer doesn't mean you have finished learning. It means you have taken responsibility for someone else's learning and physical development.

To my fellow trainers 

Don't let the fact that you are called a trainer convince you that you have finished learning.

Your clients are not your experiments, and your confidence should never be greater than your competence.

Keep learning. Keep questioning. Listen to your clients. Accept when you are wrong. Know your limits. Take responsibility for what you can control—and don't destroy yourself over what you cannot.

The best trainer isn't the one who knows everything. It's the one who is committed to knowing more tomorrow than they knew today.

There is a common saying in data science:

"A model is only as good as the data it learns from."

As artificial intelligence becomes increasingly integrated into our daily lives, one question becomes more important than ever:

Where does high-quality data come from?

Recently, I completed the DataUniversa Data Ingestion Training System and was honored to receive certification after achieving a 10/10 score in the Functional Fitness dataset assessment.

While receiving the certificate was rewarding, the real value was understanding the discipline required to transform human experience into structured knowledge that AI systems can actually learn from.

Data Is More Than Information

Many people think data collection simply means filling in forms or recording numbers.

In reality, meaningful data ingestion is about preserving context.

It means capturing not only what happened, but also:

• the original problem,
• the starting conditions,
• observations,
• reasoning,
• interventions,
• failures,
• outcomes,
• supporting evidence,
• and the lessons that others can apply.

Without that context, data becomes isolated facts. With context, it becomes knowledge.

Applying These Principles in Practice

The timing of this certification could not have been better.

Over the past several months, I have been redesigning the Global Fast Fit (GFF) organizational data system, transforming multiple independent Google Sheets into a centralized, governed information system.

That work involved:

• designing permanent unique identifiers,
• improving data integrity,
• implementing centralized governance,
• creating provenance tracking,
• automating synchronization using Google Apps Script,
• and documenting the complete engineering journey as a HOSI (Human-Originated Solution Intelligence) case study.

The DataUniversa training reinforced something I had already begun to appreciate: Good AI starts with good human documentation.

If the reasoning behind a solution is never recorded, AI can only learn the outcome—not the process that produced it.

The Importance of Human-Originated Solution Intelligence

One of the most exciting ideas introduced through DataUniversa is that people should not only contribute data—they should contribute solutions.

Every solved problem represents valuable knowledge.

Whether the challenge involves healthcare, education, engineering, business, agriculture, or organizational systems, documenting how a solution was discovered allows both humans and AI systems to learn from real-world experience.

This philosophy aligns closely with the HOSI framework, where complete solution journeys are preserved rather than simply recording successful outcomes.

Receiving this certification is not an endpoint.

It marks the beginning of a deeper commitment to building systems that are:

• reliable,
• auditable,
• evidence-based,
• reproducible,
• and designed for long-term learning.

As organizations increasingly adopt AI, the quality of their future systems will depend heavily on the quality of the knowledge we preserve today.

I'm grateful to the DataUniversa team for developing a training program that emphasizes structured thinking, evidence, and responsible data ingestion.

I look forward to applying these principles in future projects and contributing additional HOSI case studies that help build better knowledge for both people and intelligent systems.

Certificate Achievement

• Certification: DataUniversa Data Ingestion Training System
• Dataset: Functional Fitness
• Score: 10/10
• Certificate Awarded: July 18, 2026

This achievement reminds me that the future of AI isn't built solely by better algorithms—it's built by better knowledge, carefully documented by people who solve real problems.

One of the most difficult challenges in human performance research is comparing results across different populations.

Fitness data is collected everywhere. Schools, sports programs, military organizations, health systems, wearable devices, and fitness applications all generate enormous amounts of information. Yet meaningful comparison remains surprisingly difficult because the underlying measurements are often different.

Different tests measure different things. Different organizations use different standards. Different countries collect different types of data. Even when two groups appear to be measuring the same concept, the methods used may not be comparable.

As a result, many discussions about population fitness rely on assumptions rather than direct comparison.

Cross-Population Comparison is one of the core objectives of the Global Fast Fit project.

The idea is straightforward: if individuals from different populations are evaluated using the same benchmark, under the same rules, with the same verification requirements, meaningful comparison becomes possible.

This principle was one of the primary reasons the GFF Standard was developed. Rather than relying on country-specific testing protocols or organization-specific fitness assessments, participants are evaluated using a common benchmark that can be performed across diverse environments. The goal is not to eliminate differences between populations, but to create a common reference point through which those differences can be studied.

Cross-population comparison is not limited to geography. The same methodology can be applied to age groups, genders, occupations, training backgrounds, and other population segments. A benchmark becomes more valuable when it can support comparison across multiple dimensions rather than within a single group.

This capability has become increasingly important as the Global Fast Fit dataset has expanded. Thousands of benchmark submissions and exercise records collected across multiple countries have created opportunities to examine performance patterns that would be difficult to observe within smaller or isolated datasets. Questions about how fitness varies by age, training history, location, or demographic group become easier to explore when all participants are measured against a common standard.

The broader Human Performance Intelligence (HPI) initiative was built in part to support this type of analysis. While individual benchmark results provide value on their own, larger datasets make it possible to study trends, distributions, and relationships across populations. Cross-population comparison transforms isolated fitness tests into a growing body of comparative human performance data.

The objective is not to declare one population stronger, healthier, or more capable than another. Human performance is complex and influenced by many factors. Instead, the goal is to provide a framework through which meaningful differences can be measured, studied, and understood.

In many fields, progress begins with the ability to compare. The same is true for human performance. Without common benchmarks, comparisons become difficult. Without comparison, patterns remain hidden.

Cross-Population Comparison is therefore more than a statistical exercise. It is one of the foundational reasons global benchmarks exist in the first place. By establishing a common standard and a common methodology, Global Fast Fit seeks to make meaningful comparison possible across the populations that make up the world.

Most fitness systems measure only a small portion of human performance. A running test may measure cardiovascular capacity. A strength test may measure muscular performance. A wearable device may measure activity levels. A health assessment may focus on biomarkers. Each provides useful information, but each captures only a fragment of the larger picture.

Human Performance Index (HPI) was developed to address this challenge.

HPI is an effort to create a more comprehensive view of human performance by integrating multiple forms of evidence into a single framework. Rather than focusing on one exercise, one test, or one device, HPI seeks to understand how different measurements relate to one another and what they collectively reveal about an individual's capabilities.

The idea emerged from a simple observation: human performance is multidimensional. A person may possess excellent cardiovascular fitness but limited strength. Another may demonstrate impressive strength while struggling with mobility or endurance. Looking at a single metric often produces an incomplete understanding of overall performance.

The Global Fast Fit benchmark became one of the foundational components of HPI because it provides a standardized, verifiable measure of functional fitness. However, HPI extends beyond GFF alone. It is designed to incorporate additional forms of performance evidence, including exercise records, movement assessments, activity data, and other measurable indicators of physical capability.

A key objective of HPI is creating comparability. Human performance data is often fragmented across devices, applications, fitness programs, and health systems. Measurements may be collected using different standards, making meaningful comparison difficult. HPI seeks to provide a framework through which diverse performance data can be evaluated within a common structure.

Verification also plays an important role. Many performance systems rely heavily on self-reported information or proprietary scoring methods that are difficult to examine independently. HPI places greater emphasis on observable, measurable, and verifiable performance whenever possible. The goal is not simply to generate scores, but to create confidence in what those scores represent.

The broader significance of HPI extends beyond individual fitness assessment. As larger datasets are collected and standardized, opportunities emerge to study patterns across populations, age groups, training methods, and environments. Questions that are difficult to answer using isolated records become more accessible when performance data can be evaluated at scale.

This is one reason HPI is closely connected to the larger Global Fast Fit ecosystem. The benchmark provides a common reference point, while the surrounding data infrastructure makes it possible to analyze performance across thousands of observations rather than isolated individual results.

Human Performance Index is ultimately an attempt to move beyond isolated fitness metrics toward a more integrated understanding of human capability. It recognizes that performance is complex, that meaningful measurement requires multiple perspectives, and that better data creates opportunities for better insights.

As the collection of human performance data continues to expand, HPI represents an effort to transform individual measurements into a broader system of knowledge—one capable of helping researchers, coaches, organizations, and individuals better understand how people perform, improve, and age over time.

The Global Fast Fit Pro benchmark was the original version of the GFF system.

The benchmark consists of four components:

• 30 Pushups

• 30 Plank Leg Lifts

• 30 Squats

• 500 Meter Run

While GFF Standard was ultimately adopted as the flagship benchmark for broad population use, GFF Pro remains an important part of the Global Fast Fit ecosystem.

The relationship between the two benchmarks reveals one of the most important lessons learned during the development of GFF.

A benchmark is only useful if people can actually perform it.

Early testing showed that GFF Pro was highly effective at differentiating fitness levels among active individuals. However, it also revealed that many participants who considered themselves reasonably fit were unable to complete the benchmark successfully. Pushups proved to be a particularly significant barrier, especially among older adults, women, and individuals with limited training backgrounds.

This finding was important because the long-term objective of Global Fast Fit was not simply to identify elite performers. The goal was to create a benchmark that could be deployed globally and serve as a common reference point across diverse populations.

Rather than abandoning GFF Pro, the project evolved into a two-tier system.

The GFF Standard routine became the primary benchmark for broad population assessment and comparison. GFF Pro remained available as a more demanding benchmark for individuals seeking a higher standard of performance.

This distinction allows the system to evaluate functional fitness at different levels while maintaining consistency in methodology and verification.

Like the Standard benchmark, GFF Pro is designed around observable physical performance rather than self-reported fitness. Participants demonstrate their abilities through a structured sequence of exercises that can be reviewed and verified through video evidence. This emphasis on verification is one of the characteristics that distinguishes GFF from many fitness assessments that rely heavily on estimates, questionnaires, or indirect measurements.

The existence of GFF Pro also provided valuable information about global fitness itself. One of the most significant outcomes of the project was not the benchmark design, but the insight gained from observing how different populations performed when asked to meet a consistent standard. The gap between perceived fitness and demonstrated fitness was often larger than expected.

Today, GFF Pro serves several purposes. It provides a more challenging benchmark for highly motivated participants, creates additional differentiation among stronger performers, and continues to contribute valuable data to the broader Global Fast Fit research and benchmarking effort.

The development of GFF Pro ultimately influenced the creation of the GFF Standard routine, making it one of the most important components in the history of the project. Without Pro, the team would not have discovered where the balance between rigor and accessibility truly existed.

In that sense, GFF Pro is more than a harder benchmark. It is the benchmark that helped define what Global Fast Fit would become.

The Global Fast Fit Standard is the flagship benchmark of the Global Fast Fit system.

It was designed to answer a simple question: 'Can a person demonstrate a practical baseline level of functional fitness using a small number of exercises that can be performed almost anywhere in the world?'

Many fitness assessments require specialized equipment, laboratory testing, expensive devices, or lengthy protocols. While those approaches can provide useful information, they are often difficult to deploy at scale.

The GFF Standard was built around a different goal: creating a benchmark that is simple enough to be performed in diverse environments while still measuring multiple aspects of physical capability.

The benchmark consists of four components:

• 15 Pushups

• 15 Plank Leg Lifts

• 15 Squats

• 250 Meter Run

Together, these exercises evaluate upper-body strength, core stability, lower-body function, and cardiovascular capacity. Rather than measuring a single fitness attribute, GFF Standard is intended to assess an individuals level of functional capability across several major categories of movement.

The choice of exercises was not accidental. One of the challenges in building a global benchmark is balancing accessibility with difficulty. A benchmark that is too easy provides little information. A benchmark that is too difficult excludes a large portion of the population.

The GFF Standard routine was developed after extensive testing and comparison against alternative approaches. Early versions of the benchmark were significantly more demanding. While those versions provided greater differentiation among highly fit individuals, they proved impractical for broad population deployment. The Standard emerged as a compromise between rigor and accessibility, allowing meaningful comparisons across age groups, genders, countries, and fitness backgrounds.

Another important feature of GFF Standard is verification. Unlike self-reported fitness questionnaires or estimated scores, GFF is designed to support video-based validation. Participants can submit evidence of performance, allowing results to be reviewed and verified. This creates a stronger foundation for comparison than systems that rely solely on self-reported data.

Over time, GFF Standard has become more than a fitness test. It serves as a common reference point within the broader Global Fast Fit ecosystem. Results can be compared across populations, used in longitudinal tracking, incorporated into health and performance studies, and connected with other human performance data.

The value of GFF Standard is not that it perfectly measures every aspect of fitness. No single benchmark can do that. Its value lies in providing a practical, repeatable, and globally deployable reference point that allows people from different backgrounds and locations to be measured against the same standard.

In a world where fitness is often assessed using incompatible methods, the Global Fast Fit Standard was created to provide a common language for functional fitness.

The Global Fast Fit benchmark did not begin as an attempt to create another fitness challenge.

It began with a much larger question:

Is it possible to create a practical fitness benchmark that can be used across countries, populations, and environments while still producing meaningful, verifiable results?

At the time, the fitness landscape was already crowded with tests, assessments, scoring systems, and performance standards. Some focused on strength. Others emphasized endurance, body composition, athletic performance, or health outcomes. Many required specialized equipment, laboratory testing, trained personnel, or facilities that were not universally available.

What was missing was a benchmark that could be deployed broadly, verified consistently, and compared across diverse populations.

The earliest versions of Global Fast Fit focused on functional movement and observable performance. Rather than relying on questionnaires, estimates, or indirect measurements, the goal was to create a benchmark based on tasks that people could actually perform and demonstrate. Every exercise had to satisfy several requirements. It needed to measure a meaningful aspect of physical capability, require little or no equipment, be practical in different environments, and be suitable for video verification.

This process led to the development of what would later become GFF Pro.

The original benchmark was intentionally demanding. The objective was to establish a standard that represented a meaningful level of functional fitness rather than a minimal participation threshold. Early testing demonstrated that the benchmark successfully differentiated performance levels among active individuals and athletes. However, it also revealed something unexpected.

Many people who considered themselves reasonably fit could not complete the benchmark.

Pushups emerged as a particularly significant obstacle. Across different age groups, populations, and training backgrounds, performance dropped more quickly than anticipated. What initially appeared to be a reasonable standard proved substantially more difficult for the general population than expected.

This discovery became one of the most important findings of the project.

The challenge was no longer designing a rigorous benchmark. The challenge was designing a benchmark that could function globally.

A benchmark that only a small percentage of the population can complete may be useful for evaluating high performers, but it is less useful as a global reference point. The project therefore shifted from creating a single benchmark to creating a benchmark system.

GFF Pro remained as the higher-level standard, while a second benchmark was developed to support broader participation. This eventually became the GFF Standard.

The Standard preserved the core philosophy of the project while making the benchmark accessible to a much larger percentage of the population. The objective was not to make the test easy. It was to establish a baseline level of functional fitness that could be applied consistently across different countries, age groups, and backgrounds.

As deployment expanded, the benchmark design continued to evolve. Video verification became a central component of the system, helping create a stronger evidentiary foundation than self-reported performance alone. Data collection efforts expanded across multiple countries, creating opportunities to observe how the benchmark performed in different environments and populations.

These experiences influenced the development of the broader Global Benchmarking Methodology and eventually contributed to the creation of Human Performance Intelligence (HPI). What began as a fitness benchmark gradually became part of a larger effort to understand human performance through standardized, verifiable measurement.

Perhaps the most important lesson from the benchmark design process was that accessibility and rigor are not opposing goals. A successful benchmark must balance both. Too easy, and it provides little information. Too difficult, and it excludes much of the population it is intended to measure.

The history of the Global Fast Fit benchmark is therefore not simply the history of a fitness test. It is the history of an ongoing effort to create a common reference point for human performance—one capable of supporting meaningful comparison across populations, countries, and generations.

The benchmark that exists today is the result of years of testing, refinement, deployment, and observation. More importantly, it is the result of learning what happens when a benchmark is subjected not just to theory, but to the realities of the real world.

How twelve women over thirty in Nyandarua are climbing a dynasty of African queens — one skip at a time.

It started with a walk

Early this year, in our capacity as Nyandarua County's official fitness partner, we were asked to help condition Rufas — the county's immunization champion — for a 62-kilometre walk to get people talking about immunization. He made the distance. And afterwards, in the speeches, someone handed us a challenge that wasn't quite a contract and wasn't quite a joke: make Nyandarua the fittest county in the republic.

That was the genesis of everything that followed.

The first question wasn't what exercise — it was for whom. Here a doctor's eye had to step in. Our outpatient clinics are full of people carrying lifestyle diseases that movement could have softened, or prevented entirely. And one group keeps getting squeezed out of the conversation: women over thirty. This is the season where you're raising children, holding down work, and running a home all at once — and exercise is the first thing to fall off the list. Not because these women don't care, but because there's no hour left to give. So we built for them, on purpose: the hardest group to reach, chosen deliberately. We called it the Nyandarua Women 30+ Movement Series.

Then — why rope? Because it costs almost nothing, it can be done in your own yard, and, this part matters, it's already ours. Most women in Nyandarua grew up skipping. I was born and raised here, so I can say that with confidence. We weren't importing a foreign fitness fad; we were handing back something familiar — and rope skipping, done properly, pays you back out of all proportion to how simple it looks: real cardiovascular fitness, real coordination.

Why run it on WhatsApp? Same logic. Everyone already has it. No new app, no learning curve, nothing to figure out — you skip, you record, you send, on the tool already in your hand.

And underneath all of it, one more aim: to give these women something most programs forget — a community. Not a solo grind, but a place to belong, built around movement.

Meet the women

None of them had the time. All of them found it.

Nancy. At 56, she's one of our oldest skippers — soft-spoken, with a heart of gold and the quiet, relentless work ethic of an ant. She's only recently found her way back into fitness, and in May alone she logged 11,435 verified skips. That's more than 350 a day, every single day of the month. It puts her at number four on the board, and anyone watching her climb can see the top three aren't as far off as they look.

Dr Jane. She skips as Invicta — unconquered — which is the only handle that would fit her. She's a doctor: brilliant, with the heart of a lion, carrying her own family and everyone else's, because that's what the work asks. She's an athlete besides — she has represented Nyandarua in inter-county badminton, and runs a racket-stringing business on the side. Her morning begins like the others' — child up, ready, off to school — and then she's at the hospital: ward rounds on expectant mothers, then theatre, where on an average day three of her cases turn into emergency C-sections. Some nights she's pulled from bed at midnight for a delivery that won't wait. And in the gaps between the lives she saves, she found time to skip her way to number three and — for now — into the Order of Nzinga, the unconquerable. Invicta is right.

Carol. Carol came to win. One of the most fiercely competitive people I've ever met — and she runs Runda Academy, the school I trust with my own children. You'll catch her skipping in the schoolyard with her pupils cheering her on, a grown woman showing a generation of kids that movement is something you carry for life, not something you leave behind. She sits at number two with 32,710 skips, barely fifteen hundred behind the leader. And she's competitive enough to have broken the app: she kept hitting the video upload limit because she wanted to submit more, and forced us to raise the cap. Carol outgrew the tool, so we rebuilt the tool.

Ruth. Resilience with a timetable. Six days a week her job sits sixty kilometres away, round trip, on public transport. She has a small child. Her day starts at five: get herself ready, get her child ready for school, the long commute, a full day's work, the commute home, homework at the kitchen table — and then, in whatever the evening has left, she skips. After that she sits down to prepare content for her radio show. Ruth found her way through depression in the boxing ring, alongside her coach Lemid, who heads the Nakuru Amateur Boxing Club, and she carries that same fight into her Sunday show, Kaihuri ka Ugi. When Ruth uses the word resilience, she isn't borrowing it. And look at the board: right now, she's first. 34,160 skips — more than anyone else in the county — squeezed into the cracks of the most punishing day on this whole list. Whether she's still first by midnight is another question entirely.

How you climb

Here's what hooks people: you don't just pile up a number. You rise through an order of African queens, and every rung carries a name — and a woman — behind it.

You join the moment you register, in the Order of Moremi, for Moremi Ajasoro of Ile-Ife, who gave herself up to save her people. Your first verified skip makes you Idia, the Benin queen mother who led armies and counselled kings. After that you climb on your skips alone:

• Nandi — 500 skips. The Zulu queen who raised Shaka from nothing. Resilience.
• Makeda — 2,000. The Queen of Sheba, whose wisdom was legend. Knowledge.
• Amina — 5,000. The warrior queen of Zazzau, who pushed her borders for thirty-four years. Warrior spirit.
• Nzinga — 10,000. The Angolan queen who fought the Portuguese for thirty years and never bent. The unconquerable.
• Yaa — 25,000. Yaa Asantewaa of the Ashanti, who at seventy led her people into war against an empire. Fearless eldership.

Yaa is the highest anyone in Nyandarua has reached. On the 29th of May, two women crossed twenty-five thousand skips to claim it: Ruth and Carol, the rivals you've already met, locked at the top of the board and crowned together. Sit with whose name that order carries — a woman of seventy who refused to sit down — and then look at who earned it here: women over thirty, with children, jobs, and midnight shifts.

But here's the thing: I'm writing this on the last day of the month, and nothing is settled. Ruth leads Carol by barely fifteen hundred skips — a gap Carol could erase before midnight and seize the top crown for herself. And Invicta, our unconquerable doctor, sits just under the line, close enough to claim the Order of Yaa tonight and make it three. By the time you read this, it may already be decided. Or it may be happening right now, in the dark, one skip at a time.

And Yaa is not the top. Three more orders stand above her — higher, and still unclaimed. No one in the county has touched them. We won't tell you their names. But they're up there, waiting for the first woman bold enough to reach them.

This is only getting started

Here's the strange part: most of Nyandarua still doesn't know this is happening. In May alone, twelve women logged 115,595 verified skips — up from under six thousand the month before. Women are climbing a dynasty of queens, outgrowing the app with sheer effort, being crowned in the Order of Yaa — and it's stayed one of the county's best-kept secrets. That ends here.

Because this was never a one-off with a winner and a closing ceremony. It's a recurring monthly contest for women over thirty, built around a community that actually shows up for each other. Every month the board resets. Every month there's a fresh climb — from the Order of Moremi all the way up, with three orders at the summit that no one has reached yet, still waiting.

So it doesn't matter that you're only hearing about this now. Nancy began again at 56. Next month's leaderboard is empty. The only question left is whether your name is on it.

Ready? It's free. Save +254 140 823802 on WhatsApp, send the message JOIN 018 QUEENS, and follow the prompts. That's it — you're in.

A few years ago—before AI, before smartphones became extensions of ourselves—technology was still confusing to many of our parents. As millennials, we found ourselves in an unusual position: we were the translators of technology.

We taught them how to save contacts, send messages, and make calls on simple feature phones.

I remember one particular neighbor—Jairo (Jairus).

He would show up at our house late in the evening, often slightly drunk, holding what he believed was a “broken” phone. I must have been around six years old at the time, but to him, I was something special—a small technician with extraordinary skills.

He never came empty-handed. A packet of mandazis was his way of paying for the service.

He would explain the issue, frustrated: he couldn’t make calls. My mother, without hesitation, would point at me and say, “Fundi wako ako hapo”—your technician is right there.

I’d take the phone, and almost immediately, I’d notice the small airplane icon at the top of the screen. Flight mode.

Simple problem. Simple fix.

But where’s the fun in that?

Like any “professional,” I had a reputation to maintain.

Jairo would say, “Ona, dũgakorwo na ihenya”—take your time.

And I did.

I’d walk to a quiet corner, open my favorite game—Snake Xenzia—and start playing. I’d chase high scores while occasionally checking the battery level. When it dropped to one bar, that’s when the real “work” began.

I’d disable flight mode, remove the battery dramatically, then ask him to give it at least ten minutes.

When I finally handed it back, he would immediately make a call—usually to his wife—and proudly announce, “Gore ni mwaki!” (Gore is fire). That was my childhood nickname.

Payment confirmed. Mandazis enjoyed.

At the time, it didn’t feel like a big deal. It was just a small win, a harmless trick, maybe even a child’s creativity at play.

But looking back now, it means something different.

I wasn’t fixing phones. I was operating in a gap—between knowledge and ignorance, between exposure and unfamiliarity. Jairo wasn’t incapable; he simply didn’t know.

And today, I can’t help but see the parallel.

We are now standing in a similar moment in history—only this time, the gap is called Artificial Intelligence.

AI is no longer a futuristic concept. It’s here. It’s moving fast. And just like back then, there are two groups forming: those who understand it, and those who don’t.

Some people will adapt early. Others will hesitate. Many will ignore it—until it becomes unavoidable.

And then, just like Jairo with his phone, they’ll find themselves locked out of something that seems simple to others.

The difference?

This time, the stakes are much higher.

Careers, businesses, and entire industries are being reshaped. The next generation will grow up with AI the way we grew up with mobile phones—it will be natural to them.

But for us, this is a transition.

We have to unlearn, relearn, and push through the discomfort.

We are the bridge generation.

We carry the responsibility of understanding this shift—not just for ourselves, but for those who come after us.

Because one day, someone will look at AI the way Jairo looked at that phone—confused, frustrated, and locked out.

The question is: will you be the one holding the mandazis, or the one fixing the problem?

Learn AI.

If not for yourself, then for the next generation.