“Mulkin sama zamu je…” — these were the lyrics of the Hausa gospel song that seeped through my AirPods into my ears as the clock struck 12 a.m., marking the beginning of Christmas, 2025. That song was especially nostalgic. It reminded me of my childhood, when my siblings and I would dance and vibe to its beats as it blasted from my father’s stereo set.

But one thought kept returning to me that night. It was the thought of how different this Christmas would be for my family. This was the first Christmas we would celebrate with a member of our family not present.

My elder brother — the eldest of us six children — had been away from home for some months now, and from the looks of it wouldn’t be home for Christmas. He had had a fallout with Dad over some lifestyle change and habits, and things went south from there.

I laid in bed, thinking about how different it must be for him too. I didn’t know how to feel about his leaving. Part of me felt deeply concerned for him, while another part wanted to blame him for leaving us behind.

I understand that there comes a time in one’s life when one needs space and freedom, but the least, I thought, one could do is leave the right way. Then again, I guess there is no right way to say goodbye.

We still chat and call each other on the phone, but that could never make up for the joy of having him physically present. I knew that, whether I liked it or not, a day would come when all of us would leave to start our own lives and families — but I definitely wasn’t prepared for it to come this early.

As I lay there, more thoughts flooded my mind. Would we ever get to play PUBG Mobile as a team again, or take turns completing missions in Grand Theft Auto V? Who was I going to argue with over whether Samsung or Apple had the better ecosystem? Or debate with on whether medicine was more relevant than engineering? Or argue over who between Carlos Alcaraz and Jannik Sinner would win the Roland Garros?

More importantly, who would he be doing all these things with now?

Then that other side of me took over — maybe he had found someone better to do all these with.

Someone better than me? Definitely not!

But then I remembered that although we may share the same starting point, our paths and destinations in life are not the same.

At that point, all I could do was wish him well and pray that he found purpose and a sense of self-achievement wherever he may be — and that I did.

It was Christmas already, a day meant for joy. And that was exactly what I chose to do: be joyful and thankful to God for everything.

Growing apart from the people we love is never easy. Sometimes it happens quietly, without closure or clear explanations, yet it reshapes us all the same.

As young people, how do we learn to let go without losing connection? And as parents, how do we balance love with freedom — care with control?

Perhaps your story mirrors mine, or perhaps it is entirely different. Either way, I’d love to hear it. Have you experienced separation within your family? How did you handle it , and what did it teach you?

When We Grow Apart was originally published in Write A Catalyst on Medium, where people are continuing the conversation by highlighting and responding to this story.

Here’s everything you need to know about HITL, so you stay relevant in the inevitable AI take-over.

Human-In-The-Loop, HITL is simply a way of incorporating human intelligence and expertise into Machine learning and AI systems. It is effectively a way of evening out the relevancy trade off that would exist between humans and AI in the nearest future.

How does HITL work?

In one word, Data; HITL aims to provide quality input data for AI systems and supervise its learning process.

HITL works in 3 stages:

• Data annotation: Humans label the input data on which AI systems learn.
• Training: These labelled data are then fed to an AI algorithm so it learns from it and is able to make accurate decisions when given new data.
• Testing and Evaluation: Humans identify and correct mistakes made by the AI algorithm.

How’s all this important?

Well, HITL reduces the likelihood of job displacement, by integrating humans into the development of AI systems.

It also leads to enhance productivity and efficiency of AI systems because an AI model is only as good as the data it’s fed.

How can you become a part and stay relevant?

By becoming a provider of such data- a miner of the new oil (data) fueling the AI regime.

Introducing PublicAI, a platform that allows anyone from anywhere, at any time in the world to earn rewards by contributing to AI development. Users are rewarded for mining high quality data on the internet or verifying collected data as validators.

To become a part of PublicAI all you need to do is click on the link at the bottom of the article, sign up, complete your profile and start submitting or validating submitted data, all in a matter of minutes.

No previous experience is required, it’s totally free plus you get rewarded.

What are you waiting for?

Join the fray of data providers ensuring the integrity of AI systems.

Use the link below to sign up and get started right away.

https://beta.publicai.io/?r=JU6zE

Recently, I was reading Mark Manson’s “The Subtle Act of Not Giving a F*ck” and I was on this chapter where he wrote on the “Tyranny of Exceptionalism”. This got me thinking.

Could this be the biggest flaw in our present world?

Exceptionalism is the belief that things that are “out of the normal” are what should be desired in order to live a happy life.

Every day we consume content (information) in one form or the other and we take these information to be a quintessential of how our world is, but this is not true! What we consume everyday are just the extreme sides of the bell curve of reality: the best good and the worst bad. Everything else in between is out of our vision.

In reality, most of us lead, generally speaking, average lives- lives well within the bell curve of reality.

The consequence of all this is that we tend to base our values and judgments on these tail events. Such that if our lives look nothing like what we see over the social media or whatnot, we feel bad and unfulfilled about ourselves; if I don’t get to lose that weight within 2 weeks then I am a failure and never might; if my story doesn’t sell on the first, second or even third try then it’s probably never meant for me, after all this person nailed it on their first try. This is how toxic the effects of Exceptionalism are on individuals and society at large.

Exceptionalism is one of the major causes of an ever-increasing rate of unhappiness in our present world. Mark explains in his book that:

The key to our happiness is primarily dependent on the values we base our judgments on.

It is very easy to see then that if our values are based on the superficial, extreme cases of reality, then we are bound to live unhappy lives- even if we are, in reality, successful.

Exceptionalism makes the normal feel mediocre and changes how we view failure, making it seem dreadful and undesirable.

The antidote to Exceptionalism then lies in us lowering the stakes and reframing our perception of failure. This is something Ali Abdaal, the world’s most followed productivity expert, wrote in his book, “Feel Good Productivity”. We shouldn’t see failures as just failures, but as an invitation to try something new, Ali says.

We would learn to appreciate failure more if we viewed life as a maze where our aim is not to find the path that leads to our destination (end goals) but to cross out all the other paths that don’t. This way, every time we fail, we don’t just see it as a failure but as a step advanced towards success.

Exceptionalism has easily seeped into almost every aspect of human endeavors. It is thus essential to curb its effects in our lives.

If you love articles like these where I distill facts and opinions from various books I’ve read, then be sure to leave a comment. Don’t forget to follow me as well!

Exceptionalism: The BIGGEST Flaw In Our Reality was originally published in ILLUMINATION’S MIRROR on Medium, where people are continuing the conversation by highlighting and responding to this story.

It was a dream come true!!!

I made my first $26 writing online a month ago. I know $26 doesn’t sound like a lot, but the fact that I made that from writing at my leisure time at school just goes to show how resourceful writing could be in the long run.

So, the platform is called Superteam earn. It’s basically a place where writers can participate on various writing bounties and get reward in cash- crypto actually.

Bounties are posted by different projects with their evaluation criteria, prize, deadline and other additional resources, your job as a writer is usually to read and research about the project and write a Twitter thread on the topic of interest.

Once you’re done writing the thread, you copy the twitter post’s link and submit it on the bounty’s page, alongside your Solfare (a digital crypto currency wallet) wallet address into which you would be paid.

The caveat to this is that you’re actually competing with other writers for the grand prize(s), so you have got to do the most you can to stand out.

It’s very easy to get started; it requires no experience or qualification. All you need to do is visit https://earn.superteam.fun/ and sign up, then go ahead and download the Solfare app from Apple or Google play store, setup your wallet and start hunting for bounties.

Happy hunting!!!

So recently I went to the university library where I schooled to borrow some books. I get to the shelf where the books were and carried the books in my hands to the librarian’s desk. As was the custom, my library card had to be vetted and signed by the librarian before I could borrow the books.

The librarian, on seat that day was an elderly woman, probably in her late fifties to sixties, so I walked up to her desk and after greeting, I told her that I had come to borrow some books, and she requested for my library card. I slid my hand into my pocket and brought out the library card, and with the books still in one hand, I handed her the card with my free hand.

For a moment she just stood still and stared bluntly at me. And in my mind, I was like “I know that look!” And then it clicked, I had messed up!

The books were in my right hand!!

I had inadvertently handed her the card with my left hand. Realizing this, I immediately dropped the books on the desk and switched the card to my right hand and handed it to her but she wouldn’t accept it, at least not until she had instilled the morality she so much felt she was obliged to instill in a seemingly wayward youth as myself.

And so the lecture began: she went on and on about how we, the younger generation, failed to accord the respect that is due to our elders, and about how the decorum and virtues of her generation and generations past seem to have faded away in we the present generation. I stood there all the while with a somewhat repentant face. When she finally came to a pause, I apologized dearly for my mistake. She collected the card, signed on it and returned it to me with a face that spelled pity on her part for me. I received it with both hands, thanked her, took the books and left as respectfully as I could.

Just to clarify, I am and have always been left-handed and so was not new to this custom: I had been spanked times without number as a child for using my left hand to either receive or give out anything to an elder. The moral of the story is you always gave or received something from an elder with both hands or — to a lesser degree — your right hand, but never your left. I had known that very well by now and it had never felt strange, but that day something fell completely off about it.

What is wrong in being left-handed?

I mean both right and left hands are very much the same only that one is a lateral inversion of the other — permit my scientist self.

So why is one treated as if some misfortune or bad aura was associated with it and the other with so much loftiness and virtue?

Why, of all discriminations in our present world, do we still see the need to discriminate between members of our own body?

If you have been through a similar situation or experience, please do share your thoughts.

Is There Anything Wrong with Being a Lefty: African Mythology was originally published in ILLUMINATION on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article is not written to critique the US education system in any way. It is meant to share my experience as a first generation international going through US education applications.

My name is Godwin Musa, I am from Nigeria and this is my story.

Back then in high school I had had one dream: to become a Robotics Engineer. It had been what had kept me going. I never knew how I would achieve that, it wasn’t a thing in my home country or my continent as a whole- at least at the undergrad level- but I still believed I could, was I wrong to believe? I would never know.

I had always seen myself as a joker card- a card chosen not for what it is but for what it could be.I had always believed I was capable of achieving my dream against all odds and I trusted that other people saw that in me too.

After high school a friend of my brother’s introduced me to EdUSA.

I had just gotten a glimpse of the feasibilty of my dream and I was exuberant. I applied for EdUSA and got accept 3 months later, in my mind this was it. I was finally going to make it. Was I though?

I would never know why I got selected out of the thousands of applicant that applied but I could guess it was because of my story I had shared in my essay and because someone in EdUSA had seen the potential in me that not everyone sees. Why do I say so? a requirement for getting selected was a minimum of 5A’s on the WASSCE (West African Senior School Certificate Examination) and my WASSCE was certainly not screaming all A’s.

This is the point where I have to talk about a defect in the way students in my part of the country (the north) and probably the whole part of the country are trained. Students are meant to take the Unified Tertiary Matriculation Examination alias JAMB as the ultimate determining factor for admission into college/university over the WAEC; which is true if you wanted to study in Nigeria, but that was where I missed it, my dream was different. By custom, I studied hard for my JAMB and aced it getting a score of 338/400 which was pretty awesome but I faded my WAEC and this is where I was wrong. Now, before you judge me, remember I had no awareness on how the US education system works, I didn’t even have a clue that I could ever come so close to achieving my dream or what it would require.

I did get accepted any ways to EdUSA with a GPA equivalent of 3.11, as I will later learn. Through the EdUSA program, I joined my first book club where we would read books each month and have a discussion on it at the end of the month. Through the EdUSA program I got to know my first Black hero, Miss Maya Angelou, I even wrote my first poem ever, inspired by the story of my dream.

I eventually wrote the SAT which was somewhat a requirement in the US education system. Now, before my SAT score reveal, I will want to shed light on another aspect of the US education system. Schools in the US were divided into general colleges and HBCUs (Historical Black Colleges and Universities). The former didn’t care much about your GPA- in my case, WASSCE equivalent GPA. The latter did care about your WASSCE equivalent GPA, it was a requirement. And since I knew my WASSCE wasn’t all rainbows, I aimed for the general US universities. Remember, my dream was to study Robotics engineering (probably naïve of me as an international student, but that was my dream) and no HBCU offered that as a major, more reason why I focused on making it to a normal university. And faded HBCUs another thing I got wrong.

So back to the SAT. I studied for my SAT, from paper practice tests, to Bluebook, and it was finally time for me to be registered for the SAT. I felt so much closer to my dream than I ever did.

The SAT registration fee was expensive at least in my local currency and I wouldn’t have been able to afford it but I had performed well on the mock exams which I had taken virtually through EdUSA program and so my fee was paid for me by the generous EdUSA program because they believed against all odds that I could.Here’s another point where I would want to shed light on what I feel was the most challenging part of my whole application process: Distance.

I resided in the north eastern part of the country and EdUSA was based in the central part of the country. Meaning I had to join every event, test and meeting virtually which was no burden at all, I enjoyed those moments. But when it was time to write the SAT, the nearest testing centered was in ABUJA the same state EdUSA was based in. When the time came for me to write the test, I travelled for 12 hours to arrive at Abuja 2 days before my test to stay with my aunt who lives in Abuja. The day came and I wrote the SAT and came out of the testing center head high that my dream was finally fitting together. That very day I went back to the Terminus and stayed there till 3 am in wait for an earlier bus back to my place. Why the rush you may ask? Well I had gotten admission into a federal university at my place studying Electrical and Electronics Engineering. And during the time I went to write my SAT I was to have my test the next week, so I wrote my SAT on a Saturday morning and by 3am, the next day, Sunday, I was on my way back to Maiduguri, another 12 hour journey. I arrived safely that Sunday and continued with my test the next day.

Then the anxiety phase began. I waited and anticipated the day SAT results would be out. It finally came and it was a 1480, my heart sank too deep for me to feel its beat. Don’t get me wrong, 1480 was in no way a bad score, but for an international student like me with the expectation I had, it wasn’t enough. I was so drained I questioned everything about me, was I truly worthy of my dream? Will I be able to make it? Why did God allow me to pass through all this struggle just to leave me at this crossroad which I could venture into no more? My SAT score wasn’t enough for my dream school and major, my CGPA also wasn’t enough to get me to an HBCU either. I began to wonder if I was ever good enough in the first place; I questioned every choice made by me and my parents in my academic career. If only we had not relocated from my other school which was in the more central part of the country, if only I had stayed at my other school I would have gotten exposed to the system early. But then I remembered most of our relocations was hardly my parents’ fault, we had relocated from my birth state as a result of the ISWAP crisis and had once again relocated from my sate of origin as a result of the all too rampant indigene- settler crisis back then.

All these only hurt more as I thought about them.

In the end I came up with an unrealistic college list, had uncompleted common app applications, lost hope of ever making it to my dream major, dream school or any school at all.

Now don’t get me wrong, I didn’t get admitted to any school in the US not because the schools were inconsiderate (I had hardly put in any application in the first place). I didn’t get admitted because I was a naïve, elusive international student applying for a major that only a few colleges in the US offered, add that to the fact that I needed full financial aid and you have a beautiful, unrealistic dream. I didn’t get admitted because I stubbornly believed in a dream bound so close to my heart that I didn’t see all those loop holes and at some point even ignored some. But was I wrong in believing so strongly in my dream, was I impatient? Was I uncontended? Again, I might never know. I had read once that if you believed in something the whole universe conspires to help you achieve it? Why is my case different? Where is my beginners luck? It was probably short lived.

So there you have it, the story of my US education application experience, definitely not the most blissful. I wrote this to take the burden of keeping all these adventure to myself from my heart. And so that it hopefully serves as caution for anyone like me who dares to dream different, not to dissuade such person but to make them make better decisions than I did.

Is that the end of my dream? Do I stop chasing my dream then? Definitely Not!

I will rise everyday proud of all I was able to achieve throughout these adventures, with the bitter sweet hope that every day I am coming one step closer to the fulfilment of my dream, however impossible it may seem.

Welcome!! You have made it to Day 2 of 30-day learning journey. Yesterday we saw an overview of Machine learning, its uses and the types of ML systems.

Today we will be taking a dive into Supervised Machine Learning system.

We will discuss:

Its Types

Difference between Regression and Classification

A manual example of Linear regression technique

Supervised learning is a machine learning technique where the model (the program that is learning) learns from labeled data, that is input data and its required output. The model is fed with features that determine the final output or label and also the output (label). The model’s job then is get a mathematical function or expression that when given the features (data) is able to predict an output. It just like learning from examples: you see the answer to each question and what is expected of you is to learn the steps. Same applies to a model in the supervised learning. The model is trained on data called the Training data and is then tested on a different dataset unknown to it called the Test data.

How does the model learn?

The model learns at first by generating a random mathematical function to relate the features (input data) to the label (output) and then consequently it corrects and modifies that mathematical function till it has exhausted the training data. So we see that in the end the model learns by coming up with a generalized mathematical function based on the training data. It then fits the test data into that generalized function to get a prediction.

Types of Supervised ML

Supervised machine learning is divided into 2:

• Regression
• Classification.

In Regression, the model predicts a continuous numerical value (or just numbers) such as price of a house, temperature, price of stocks etc.

In Classification, the model predicts a class group. It learns by transforming the input data into numbers and making a prediction of an object’s class, although it outputs a number, the number is representative of a class. For example, 1 for a dog and 0 for a cat. A common example is Image classification, Spam email filter, Cancerous cell detection etc.

Difference between Regression and Classification.

Regression predicts continuous numerical values while Classification predicts categorical output.

Regression is further divided into:

• Linear regression
• Logistic regression
• Random forests
• Decision trees
• K- nearest neighbors etc.

Now we will look at a simple linear regression example, so we see what it means for a model to learn.

We will take a simple case of house price prediction.

From the table above we see we have only one feature the house age represented by x1, and the house price represented by y1. Our job as the ML model is to find a linear function

Y = mx1 + b

Where Y is our predicted price value and x1 is the feature we are provided with. b a constant or bias term.

What we need for our function (model) to be able to make predictions is the value of m and b which we will get from the data provided in the table using something called Cost function.

Join me tomorrow to finish our first manual ML model.

Congratulations on completing Day 2 of our 30-day ML journey, hope you learnt a lot, I know I did!!

See you in Day 3!

Welcome to the first day of our ML journey. Today we will discuss:

What is Machine Learning?

How useful is machine learning?

Brief overview of the type of machine learning systems

Ready? Let’s dive in!

WHAT IS MACHINE LEARNING?

Machine learning is the science of programming computers to learn from data. From this definition we see that data is the backbone of every Machine learning system and without data there would be no Machine learning.

When we hear Machine Learning we tend to think of it as some futuristic invention, but Machine Learning has been here for a while now, even dating back to the 1900s. Optical Character Recognition, email spam filters are all examples of early forms of Machine Learning.

Basic terminologies

• Models

A model is a computer program that can learn from data. It is this model that we feed the data to and from it we get our predictions on future data.

• Data

Is any form of input we want the model to learn from. It is what is fed to an ML model so that it’s able to make future predictions.

• Labels

are the expected outcomes of an ML model (used alongside the data in training a model) and predictions are the actual output from an ML model.

Why is Machine Learning useful?

Machine learning has a lot of benefits, but its ultimate advantage is autonomy- its ability to make decisions without human intervention ( after it has been trained on data).

Common application of ML models include:

• Image recognition
• Voice recognition
• Chatbots or Personal Assistants etc

ML models are divided into different categories based on the task they perform. We will take a brief look at each type of ML system and tasks they perform before taking a deep dive into them in coming days.

Brief overview of the type of Machine learning systems

• Supervised learning: models that are fed data alongside the expected outcome (labels). For example, if you want to predict the price of a house, the data you need will be things like the size of the house, how old the house is, the setting of the house (urban or rural). All these are the data and then the actually price of the house is the label. A supervised ML model requires you to feed it the data of a lot of different houses (in this example) and also the price of each house (the label) for it to be able to predict the price of other houses it has not ‘seen’.
• Unsupervised learning: the opposite of supervised learning. With an unsupervised learning model you need not provide the expected outcome (label). A good example is an image clustering model, it does not need to be told what the image is, what it does is create a group for each unique image it is fed for example, it is able to put the picture of a dog and a cat in different group without being told that the image was for a dog or cat and then each time it is fed a new image it compares it with the image in all its existing groups and places that image in the class it is most related to.

NOTE; Clustering is most of the time confused with Classification. So what’s the difference? The difference is: in Clustering the model is not told what the images are (no labels provided) while in Classification, the model is told what each image is. So Clustering is an unsupervised learning system while Classification is a supervised learning system.

• Semi supervised learning: as the name implies is a blend of both supervised and unsupervised learning. This is used when there is a small amount of labeled data and a large amount of unlabeled data.
• Reinforcement learning: relies on the principle of trial and error, the model is expected to learn from experience. There are predefined rules (or policies) the model, or agent as it is called, is expected to make. The model makes its own random decision (at first) and is rewarded if it made the right choice and penalized if it didn’t. The model is therefore then motivated, so to speak, to learn to make the right decisions.

A good example of reinforcement learning model is Deepmind’s AlphaGO, a computer that was famous in 2017 when it defeated a world champion at the board game of GO.

Other examples of ML systems include:

• Model based and Instance based learning
• Batch and Incremental learning.

Congratulations on completing Day 1 of our 30-day ML journey, hope you learnt a lot, I know I did!!

See you in Day 2!

30 Days of learning Machine Learning

I have always found the thought of machines “learning” fascinating, that these man-made inventions are able to share in human abilities and be even better at it.

So I got an ML course off Udemy and I am excited to start this learning journey.

I also got Machine Learning Best selling author Aurélien Géron’s ML Book.

And I am inviting you too!

Join me every day for the next 30 days as we (I hope) unravel this novel field called Machine Learning.

Each day I will share thoughts on each lesson: what I found exciting, easy and also what I found difficult at first and how I got to understand it better. I will also be sharing any additional resources that helped me better understand each concept.

Join me on this adventurous expedition: two newbies finding their way in the vast world of AI.

At the end of the 30 days we will take on a week’s challenge to apply everything we have learnt in a single project.

So, gear up and get ready with me on this fun learning journey.

If you’re new to Medium, you’re welcome! Don’t forget to sign up to Medium and follow me to get onboard.

Feel free to drop your comments so we clarify one another’s thoughts.

See you in Day One!!

https://medium.com/@godwinmusa338/30-days-of-learning-machine-learning-b0afa3251b1d

dVIN aims to revolutionize the $1 trillion wine asset market by leveraging data, decentralized public services, and blockchain integration to unify the industry.

dVIN seeks to empower wineries by incentivizing wine consumers to share consumption data with winemakers. This data-driven approach aims to bridge the communication gap between producers and consumers.By leveraging data-driven insights, wineries can reach new consumers more effectively, moving away from the traditional one-size-fits-all marketing strategy. Thereby making the process of wine making and consumption more transparent and cost efficient.

In total, the global wine market holds about $1trillion worth at any point in time. dVIN plans to bring that amount on-chain enabling wine makers to be exposed to on-chain liquidity and allowing consumers to access luxury and investment grade wines.

How does it plan to do that?

Through Decentralized physical infrastructure network (DePIN) and Real World Assets Tokenization. dVIN hopes to achieve this through the introduction of 2 NFTs in addition to the $VIN token:

1. Digital Cork Token which would serve as a deed of ownership for a wine bottle allowing for authentication of wine , prove and transfer of ownership. A DCT contains all the specific details of a wine in addition to chain of custody and provenance data.

The DCT takes from the future by implementing one of the European Union market’s future agenda of a Digital Product Passport. DPPs are to be tools for the collection and distribution of data throughout a product’s life cycle.

DPPs would be a mandatory attribute of all products to be sold on the EU market from as early as 2030. The Digital Cork Token acts as the DPP of the future for Wines.

2. Tasting Token: a soul bound token that would serve as an authentic proof of experience that a holder has tasted a particular wine bottle.Once a wine bottle is opened the DCT is burned and the TT is minted alongside some $VIN as a reward.

The dVIN protocol doesn’t hope to achieve this alone. Through a harmonious blend of partnership, the dVIN hopes to accomplish this Innovative feat:

• Read bite Solutions, an IoT software company that turns real objects into smart objects. Red Bite will assist with the tokenization and integration of each wine bottle into the digital realm further assisting the digital product passport process.
• Protokol, a key provider of the European Union aligned Digital Product Passport solutions. dVIN’s partnership with protocol hopes to deliver a holistic DCT/DPP solution for luxury wine industries.
• Jupiter Exchange’s GUM an alliance that aims to bring together investments like meme coins, real world assets stocks etc into a single market. It would play an important role in integrating tokenized wine bottles alongside coins into a single market under the Solana Blockchain.

• Deloitte, being the largest provider of professional services to wine industries, would along side AMAZON MANAGE BLOCKCHAIN aid the distribution and tracking of wines from wineries around the world.
• Real Vision being a long time partner of dVIN will help with knowledge, tools and networking required in building the future envisioned by the dVIN protocol.
• And of course Solana , which would serve as the Blockchain on which everything would be built.
• dVINLabs’s unique perspective of a revolutionized Wine market is one that deserves accolades 👏 and definitely one to anticipate.