ORANGE — LaunchSpace and the central Massachusetts-based Workers Credit Union have teamed up to educate 16- to 24-year-olds on personal finance in the event they want to start their own businesses.“It’s something that LaunchSpace has always really...

Greenfield Recorder - LaunchSpace, Workers Credit Union team up to teach personal finance to 16- to 24-year-olds in Orange

The 911 center is operated by the Orange County Sheriff’s Office ... This initiative falls squarely within the scope of telecommunications network and software technologies,” said Michael Rahman, chief intellectual property officer for Figgers ...

As scrutiny mounts, DeSantis’ Disney district cancels no-bid 911 contract

The "ICT & Telecommunications Industry in Botswana 2023" report has been added to ResearchAndMarkets.com's offering.

Botswana ICT & Telecommunications Industry Report 2023: Mobile Money and Innovative Offerings Fuel Botswana's Telecom Surge

Vodafone's bid on Tuesday to sell its Spanish business is the latest move by European telecom firms trying to strengthen their financial health by divesting assets, consolidating markets and selling stakes to investors.

Explainer: Why are European telecom firms selling assets?

The Oklahoma Department of Corrections announced today that the new Orange Alert notification system is operational and ready for citizens to opt in. ODOC worked with Rave

Oklahoma Corrections to launch Orange Alert notification system

Orange and MásMóvil have chosen Romania's Digi to acquire assets they must divest to address EU competition concerns over the proposed merger of their Spanish businesses.

MásMóvil and Orange choose Digi to buy post-merger assets

Orange said its overall 5G network reached a coverage of 83.2% of the Spanish population as of the end of September

Orange reaches 38 Spanish cities with 5G SA

Telecoms companies argue that Alphabet's Google, Meta's Facebook, Netflix, Microsoft and Amazon should bear some of the high-speed network rollout costs because they make up a huge part of internet traffic.

Italy backs calls for tech firms to help fund EU telecoms networks

BRUSSELS (Reuters) - French telecoms group Orange and Spanish peer MasMovil have picked Romania's Digi to buy assets they plan to divest to address EU antitrust concerns over their Spanish tie-up, two people with direct knowledge of the matter said on Tuesday.

Exclusive-Orange/MasMovil Pick Digi to Buy Assets Being Sold to Address EU Concerns - Sources

French telecoms group Orange and Spanish peer MasMovil have picked Romania's Digi to buy assets they plan to divest to address EU antitrust concerns over their Spanish tie-up, two people with direct knowledge of the matter said on Tuesday.

Exclusive: Orange/MasMovil pick Digi to buy assets being sold to address EU concerns - sources

ORANGE

orange

The Story-book of Science by Jean-Henri Fabre, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE ORANGE-AGARIC CHAPTER LXV. THE ORANGE-AGARIC “MUSHROOM seeds, or spores, form on these gills, these points, and on the walls of the tubes of which these holes are the orifices. I recommend to Jules the following experiment. We will take some mushrooms whose caps are not yet thoroughly spread. We will place them this evening on a sheet of white paper. During the night the blossoming will be finished and the ripe seeds will fall from the gills of the agarics and the tubes of the boleti. To-morrow morning we shall find on the paper an impalpable dust, red, rose, brown, according to the kind of mushroom. Binocular Microscope “This dust is nothing but a mass of seeds, of spores, so fine that they cannot be seen separately without a microscope, so numerous they cannot be counted. There are millions and millions of them.” “A microscope,” interrupted Emile. “Is that the instrument with which you sometimes look at things so small that the naked eye can scarcely see them?” “Yes. A microscope enlarges the objects seen through it, and shows them to us in all their details of structure, although they would be hidden from the unaided eye by their smallness.” “Will you show us through the microscope the mushroom spores when I have collected them on a sheet of paper?” asked Jules. “I will show them to you. One spore is enough, under favorable conditions of heat and moisture, to germinate and develop into white filaments or mycelium from which will spring at the right time numerous mushrooms. How many mushrooms would be produced if all the spores that fall by myriads and myriads from the gills of a single agaric were to germinate? Here again we have the story of the cod, the louse, all the feeble creatures, in short, that reproduce their kind in such immense numbers.” “To have mushrooms, then, as many as we want, it is only necessary to sow the spores?” Jules again inquired. Spores “In that you are mistaken, my dear child. Up to this time mushroom culture has been impossible, because the care required by their excessively delicate seeds is not understood by us, or may even be beyond our power. Only one edible mushroom is cultivated, and even in growing this we use not the spores, but the mycelium. “They call it the hot-bed mushroom. It is an agaric, satiny white above and pale rose beneath. In the old stone quarries near Paris they make beds of horse manure and light earth. In these beds they put pieces of mycelium known to horticulturists under the name of mushroom-spawn. This spawn ramifies, pushes out numerous filaments, and from these finally spring the mushrooms.” “Good to eat!” “Excellent. Among the mushrooms we gathered are those that I am going to acquaint you with. “Look at this first of all. It is an agaric. The upper surface of the cap is a beautiful orange-red; the gills underneath are yellow. The stalk rises from the bottom of a sort of white bag with torn edges. This bag, called volva at first enveloped the whole mushroom. In growing and pushing above ground, the cap broke it. This kind, they say, is the best of all, the most appreciated. It is called the orange-agaric. “This other agaric, likewise orange-red, and also provided with a bag or volva at the base of the stalk, is called the false orange-agaric. Would you not, however, think it was the same kind?” “I don’t see much difference, for my part,” responded Claire. “Nor I either,” said Emile. “I see a difference,” Jules declared, “but it is very slight. The second agaric has white gills, while the first has yellow.” “Jules has sharp eyes. I will add that in the false orange-agaric the upper surface of the cap is sown with shreds of white skin, debris of the torn volva. The other one has not these shreds, or very few. “If one did not pay attention to these slight differences, one would commit a very fatal error. The first mushroom is a delicious viand; the second, or false orange-agaric, is a deadly poison.” “I am no longer surprised,” said Jules, “at your telling Simon that it is impossible for us, without long study, to distinguish the good from the bad kinds. Here are two mushrooms almost as much alike as two drops of water: one kills, the other is excellent.” “Not a year passes without its lamentable cases of poisoning, from a confusion of the two kinds. Remember carefully their characteristics, so as not to expose yourself some day to a terrible mistake.” “I will be very careful not to forget them,” Jules promised. “Both orange-agarics are orange-red and have a white volva or bag. The eatable orange-agaric has yellow gills; the poisonous one, white gills.” “Besides,” added Emile, “the poisonous orange-agaric has numerous shreds of white skin on the cap.” “Look at this other that I picked from the trunk of a tree. It is a large, dark-red boletus. It has no stalk. It fastens itself to old trunks by one of its sides. It is called the tinder-agaric boletus, because its flesh, cut in thin slices, dried in the sun, and made flexible by hammering, makes tinder.” “I did not dream that tinder came from a mushroom.” said Jules. “The truffle is the most important of eatable mushrooms. It grows under ground, like the mycelium that produces it. Its odor betrays its presence. A very keen-scented animal, the pig, is led into the wood. Enticed by the smell of the subterranean mushroom, the pig roots with its snout at the spots where the truffles are hidden. Then the pig is driven away, but to console him they throw him a chestnut; and finally the precious mushroom is dug up. In its shape the truffle bears no resemblance to ordinary mushrooms. It has a bulky round body, wrinkled, and black flesh marbled with white.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jean-Henri Fabre (2018). The Story-book of Science. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/56795/pg56795-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

THE ORANGE-AGARIC

The Adventures of Sherlock Holmes: Chapter V - The Five Orange Pips

Simple and rational: LTE Cat M1 for high power wide area networks (WAN’s) which are plugged into an AC power outlet. LoRa for battery powered WAN’s. More detail . here Quick takes: Body blow to , which is being dumped. Expect more defections in the EU, but not China. NB-IoT looks stillborn in N America. NB-IoT Correctly re-positions Cat M1 as a high powered WAN technology. It should be struck from the LPWAN conversation, really. Huge lift for Semtech and difficult to imagine them remaining independent now. Major scalability challenges remain for developers working to overcome the myriad weaknesses of Lorawan, which serious developers should . avoid If you liked this article, please hit the ❤ icon below and me. follow

Want To Know What Orange’s New LPWAN Strategy Is?

This year, Bitcoin hit an all-time high - spirits soared as we had a presidential regime that supported crypto (and even launched a meme coin)! Portfolios pumped and Twitter timelines once again filled with laser eyes as bullish calls for “$200k being the next stop” and “$1m guaranteed this bull run” headlines emerged. But just as quickly, the mood shifted and since then, prices have slumped and many investors who bought at the peak are now looking at numbers in the red. So what (the hell) just happened? And more importantly, is the bull run over? Wait, what did just happen? Wait, what did just happen? did Let’s start with the (orange) elephant in the room: Trump Hopium. Markets were riding high on the narrative that a pro-crypto Trump administration could spark widespread, sector-defining change. Trump was headlining crypto events, promised crypto chiefs in the White House and there was the fantasy of $1M Bitcoin being purchased for a national reserve. But reality simply didn’t match the hype. The rally was a textbook case of ‘buy the rumour, sell the news’ - savvy traders saw the wave of optimism, rode it and then bailed before things got bumpy. To be fair, some progress has been made. There’s a strategic reserve proposal, a friendlier regulatory tone, SEC cases being dropped and actual stablecoin legislation on the cards - which is arguably more than anyone expected 12 months ago. But expectations, supercharged by memecoin mania and election-season adrenaline, simply blew past anything policy could deliver and the problem wasn’t that Trump did nothing - it's that the market had already priced in a crypto miracle. has We all came into this thinking that the Trump administration would be pro business. Then came the tariffs. To keep things high level: new US tariffs announced on ‘Liberation day’ were higher and targeted more countries than expected. China was particularly hard hit, however no one (not even close allies) was immune.  History tells us tariffs are bad for global trade and generally make everyone poorer. This has triggered fears not only of a US recession, but a global recession.  Because everyone is impacted, the effect has been felt across  global markets. Businesses who understand the impact of tariffs are rightly fearful and when business confidence drops, risk appetite tends to follow and crypto is often one of the first assets to feel this chill. Suddenly, the story has changed from bullish momentum to bearish caution and investors have moved from ‘risk-on’ to ‘risk-off’, favouring safer bets like gold and US dollars. Crypto’s Fear and Greed index has been beaten back to Extreme Fear (at this moment) and Google search volume on crypto terms is bouncing along the bottom with consumers simply tapped out. Markets melt. You don’t have to. Markets melt. You don’t have to. When the headlines turn red and the charts follow suit, it’s easy to panic. But it’s well worth zooming out. The crypto market is used to big pullbacks, even during bull runs. The  2017 bull had six drawbacks of over 30% while 2021 had fewer but they were even larger.  And while this correction feels uncomfortable, it’s not unprecedented - we’ve seen this movie before. The reality is that markets move in cycles…they always have and when markets take a hit, everything slows down and recession fears are raised. Central banks then step in with stimulus and investors start hunting for returns again. That’s when crypto typically gets very interesting - especially for those who’ve stayed calm and positioned themselves for moments like this. Just remember the maxim, it's always darkest before dawn. At Easy Crypto, we’re seeing the signs of consolidation (and not a full blown bear market). Despite the headlines, our trading volumes are holding steady. We are also seeing heavy buy activity because for some, this is the discount buying opportunity that they were hoping for and dreaming of back in January. Others are cashing out, but right now we can see clearly that buying activity still outweighs selling. The bottom line is that the market’s breathing, not broken - acknowledging that things can change rapidly and we are in a period of wild uncertainty and we endure the ‘whiplash’ from changes made by politicians and presidents. Bitcoin’s not alone in its volatility Bitcoin’s not alone in its volatility While it’s tempting to zoom in on Bitcoin as the bellwether for crypto sentiment, this is bigger than Bitcoin. US equities are down across the board. Meta, Amazon, you name it - it’s red out there and Bitcoin’s price drop is part of a broader risk-off wave. Can it reverse? Yes, history says so.Can crypto decouple from tech stocks? Also yes, again history says so.Will it happen tomorrow? Probably not. It's also worth noting that some coins have actually increased in value recently, despite broader market volatility. For example, XRP (the digital currency associated with the company Ripple) has risen by approximately 230% since November.This was driven by Ripple and the U.S. Securities and Exchange Commission (SEC) recently settling a long-running legal battle. The SEC had accused Ripple of selling XRP as an unregistered security which created a lot of uncertainty around the coin. Now that the case has been resolved, investor confidence has returned and pushed the price up significantly (another nod to the fact that pricing can be cyclical.) Perspective, please: we’re still up Perspective, please: we’re still up It might not feel like it right now, but zoom out and things start to look better. If you bought Bitcoin as recently as January last year, you’d still be up ~80% today. That’s not nothing, in fact - in traditional finance terms that is amazing. Let’s also remember that the 2021 bull run topped out around $64k and that we’re still well above that level now. This time around, however, it’s becoming clear that this cycle is less about hype and more about good discipline turning into habits. Instead of reading about crypto, people are using it. Instead of debating ETFs, investors are allocating into them. Discipline is replacing drama and that’s a good thing for everyone. So… is it over? So… is it over? Short answer? We don’t know. Many in the know are skewing towards this being a blip; but everyone is guessing.Better answer? It doesn’t matter - if you’re thinking long-term.  Stick to your plan. Now that tensions are up, having a plan and executing it is even more important. If your plan was to take some of your profits to reduce risk and recover your principal investment, then do that. If your plan was to buy into any drawdown greater than 25%... you get the idea.Crypto isn’t dead, it’s just digesting. And in the absence of clearer macro signals - like a defined Trump policy platform, a change on tariff policy, or a turn in the GDP data - we’ll probably stay in chop mode for a while. But here’s the important thing: even if we are in a downtrend, we know what happens next - the cycle turns. It always does! And when it does, those who stayed steady are usually the ones who come out ahead. know If you’re feeling rattled, zoom out. If you’re feeling stuck, focus on what you can control: your habits, your time horizon and your strategy. can Because while nobody can time the market, you can beat it - with discipline, not drama. can Investing in crypto carries risk; always do your own research. Investing in crypto carries risk; always do your own research. Investing in crypto carries risk; always do your own research.

Bitcoin Crashed Again—But This Time, It’s Not the End of the World

This Color Tool Turns Any Image Into a Wes Anderson Fever Dream

Hierdie kleur gereedskap verander enige prentjie in 'n Wes Anderson-koors droom

Tento barevný nástroj promění jakýkoliv obraz ve sen o horečce Wes Anderson

Esta herramienta de color convierte cualquier imagen en un sueño de fiebre de Wes Anderson

このカラーツールはあらゆるイメージをウェス・アンダーソン熱の夢に変える

Оваа алатка за боја ја претвора секоја слика во сон на Вес Андерсон

Alat warna ini mengubah apa-apa imej menjadi impian demam Wes Anderson

Deze kleurtool verandert elk beeld in een Wes Anderson-koortsdromen

Bu Renk Aracı Herhangi Bir Görüntüyi Wes Anderson Fırtına Rüyasına Dönüyor

یہ رنگ آلہ کسی بھی تصویر کو ویز اینڈرسن گرمی کے خواب میں تبدیل کرتا ہے

I-Color Tool Yenza Yonke Iifoto Enye I-Wes Anderson Fever Dream

1. Introduction Any deep learning model needs to have some sort of numeric input to work with. In computer vision its not a problem as we have pixel values as inputs but in Natural language processing we have text which is not a numeric input. One common way to convert text to numbers is to use one-hot representation. For example, let’s say we have a 10,000-word vocabulary, so any word can be represented by a vector of length 10,000 with 0’s everywhere except a one ‘1’ in words position in vocabulary. 2. Problem with One-Hot Encoding The problem with One-Hot encoding is that the vector will be all zeros except one unique index for each word as shown above in fig 1. Representing words in this way leads to substantial data sparsity and usually means that we may need more data in order to successfully train statistical models. Apart from the above issue, the main problem is of identifying similar words using one-hot encoding vectors. Let’s see what I mean by this with help of 2 sentences. Examples are from Deep learning specialization - Coursera. Examples are from Deep learning specialization - Coursera. Let’s say we have a trained language model and one of the training examples is I want a glass of orange Juice. This language model can easily predict Juice as the last word by looking at the word orange. Let’s say our language model never seen the word apple before. So, When our language model encounters the sentence I want a glass of apple to predict the next word, it will be able to predict the word Juice if representations for both words orange and apple are similar. But in our case using one-hot representation relation between apple and orange is not any closer(similar) as the relationship between any of the words a or an or zebra etc. It’s because the dot product between any 2 different one-hot vectors is zero. So it doesn’t know that apple and orange are much more similar than let’s say apple and an or apple and king. But the point is apple and orange are fruits and the representations for these should be similar. I want a glass of orange Juice Juice orange apple I want a glass of apple Juice orange apple a an zebra apple orange apple an apple king apple orange So, we want semantically similar words to be mapped to nearby points, thus making the representation carry useful information about the word’s actual meaning. We call this new representation Word Embeddings. Word Embeddings 3. Word Embeddings helps Transfer Learning As we have seen above in the example, even though our training data for language modeling does not have the word apple, if representation for apple is similar to the word orange (both are fruits so we expect most of the characteristics of them are same, so similar representation) then our language model is more probable to predict next word as juice in sentence I want a glass of apple. apple apple orange juice I want a glass of apple 4. How Word Embeddings Look Word embeddings are continuous, vector space representations of words. Word embedding vectors usually have dimensions around 300(in general embed_size). So each word in the vocabulary is represented by a vector of size 300. But what does each value of this vector of embed_size represent? We can think of them as representing certain characteristics like age, food, gender, size, royal, etc. Let’s go through an example to understand this in detail. Even though the below example shows that embeddings are represented by characteristics like age, food, gender, etc., embeddings which we learn won’t have an easy interpretation like component one is age, component two is food, etc., Important point to understand is that embeddings learn certain characteristics which will be very similar for similar words and different for dissimilar words. age food Important point to understand is that embeddings learn certain characteristics which will be very similar for similar words and different for dissimilar words. If we look at word embeddings of words Apple and orange, they look almost identical. So dot product of these two vectors would be high. Apple orange Now that we have a decent understanding of word embeddings, let’s see how we can produce these vectors of embed_size size for every word in the vocabulary. 5. Generating Word Embeddings: I will go over one of the earliest architectures. Below is the architecture we will go through. Currently Word Embeddings are learned as part of training process Word2Vec Skip-Gram model Word2Vec Skip-Gram model 6. Word2Vec Skip-Gram Model The main idea behind the Word2Vec Skip-Gram model is that words that show up in similar context will have similar representation. Context = Words that are likely to appear around them. Let’s understand this with an example: I play cricket every alternate day


Children are playing football across the street


Doctor advised me to play tennis every morning. I play cricket every alternate day I play cricket every alternate day Children are playing football across the street Children are playing football across the street Doctor advised me to play tennis every morning. Doctor advised me to play tennis every morning. Above, if we see words cricket, football and tennis all have the word play surrounding them(similar context). So these 3 words will have similar word embedding vectors which are justifiable as these 3 words are names of the sport. For any word, we can think of context as surrounding words. So our task is to predict a randomly selected surrounding word from the word of interest. Let’s understand this with an example Children are playing football across the street. Let’s pick our word of interest as the word football and playing as the surrounding word. So we give word football to our neural network(which we are going to train and build) and this neural network should predict with high probability the word playing. But context can include surrounding words that come before and also after and not just one word. So authors of paper used window size to select surrounding context words as shown below. Children are playing football across the street football playing football playing paper Model architecture Below is the model architecture we will be using Each training input as shown in fig 3 will be first represented as one-hot vectors that go in to model as shown in fig 4. Then we have an embedding layer with the number of neurons = embed_size(usually this is set to 300) and the output layer will be 10,000 neurons softmax(here vocabulary size = 10,000) that outputs the probability of each word in the vocabulary. For example, when the word football is sent as input to the trained Word2Vec model above, then the final layer will output high probabilities for the words are, playing, across, the as these are output words for input football in the training set. football are playing across the football Model is trained like any other neural network where we update coefficients using backpropagation and loss function. Hidden layer The hidden layer is where all the magic happens. The coefficients of this hidden layer are the actual word embeddings. For example, let’s say we are learning word embeddings of dimension 300 for every word. Then coefficients or weight matrix of hidden embedding layer will be of size 10000(vocab_size) X 300(embed_size). The output of the hidden layer will be of size 300(embed_size) X 1. This output will pass through the output layer with softmax to generate 10000 values. Instead of matrix multiplication, as shown above, we can directly use embedding weight matrix as a look-up table. Each row in the embedding weight matrix gives an embedding vector for a word(the result would be the same) in the vocabulary. Intuition As we have seen above, the main idea behind the Word2Vec Skip-Gram model is that Words that show up in similar context will have similar representation Words that show up in similar context will have similar representation So if two words have similar context(i.e., same words surrounding them) then our model needs to output similar output for these 2 words. For example, in our 3 sentences, the words cricket, football, tennis have similar context as the word playing is the surrounding word. Generally speaking, if we take large text corpora there is a very high chance that these 3 words(cricket, football, tennis) will have playing as a surrounding word. So our model has to output playingas output with a higher probability for these 3 words. This can only happen if embedding vectors for these 3 words are similar because embedding vectors are the ones that go into the output softmax layer as shown in Fig 4. cricket football tennis playing playing playing

Your AI Can’t Understand Language Until It Learns This Trick

This Simple App Lets You See How Hollywood Uses Color to Mess With Your Emotions

هذا التطبيق البسيط يمنعك من رؤية كيف تستخدم هوليوود اللون لتناقش عواطفك

Ova jednostavna aplikacija omogućuje vam da vidite kako Holivud koristi boju da meša s vašim emocijama

Diese einfache App lässt Sie sehen, wie Hollywood Farbe verwendet, um mit Ihren Emotionen zu verwechseln

Esta sencilla aplicación te permite ver cómo Hollywood usa el color para mezclar con tus emociones

Esta aplicación sinxela permítelle ver como Hollywood usa a cor para confundir coas súas emocións

このシンプルなアプリは、ハリウッドがあなたの感情と混同するために色を使用する方法を見せてくれます

ეს მარტივი აპლიკაცია საშუალებას გაძლევთ იხილოთ, თუ როგორ Hollywood გამოიყენებს ფერი თქვენი რეპუტაციაზე

Na na na na na na na na na na na na na na na na

Deze eenvoudige app laat je zien hoe Hollywood kleuren gebruikt om je emoties te verwarren

This Simple App lets you see how Hollywood uses color to mess with your emotions இந்த எளிய பயன்பாட்டை நீங்கள் உங்கள் உணர்வுகளை எப்படி பயன்படுத்துகிறீர்கள் என்று பார்க்கலாம்

Bu Basit Uygulama Hollywood'un Duygularınızla Nasıl Karıştığını Görmenizi Sağlar

Disclaimer: Parts of this article are purely fictional. ZBlockBuilders Inc., its CEO, and all accompanying scenarios are made up to help explain complex blockchain concepts in a fun, relatable way. Any resemblance to real companies or people is purely coincidental. Disclaimer: Parts of this article are purely fictional. ZBlockBuilders Inc., its CEO, and all accompanying scenarios are made up to help explain complex blockchain concepts in a fun, relatable way. Any resemblance to real companies or people is purely coincidental. Disclaimer: Parts of this article are purely fictional. ZBlockBuilders Inc., its CEO, and all accompanying scenarios are made up to help explain complex blockchain concepts in a fun, relatable way. Any resemblance to real companies or people is purely coincidental. Don’t lose the farm. Every CEO and their team needs to understand how blockchain nodes work to keep their revenues and respect secure. Don’t lose the farm. Every CEO and their team needs to understand how blockchain nodes work to keep their revenues and respect secure. Don’t lose the farm. Every CEO and their team needs to understand how blockchain nodes work to keep their revenues and respect secure. Congratulations, Reader. You have just been invited to join the team, as Chief Executive Officer, at ZBlockBuilders Inc., a Web3 technology company looking to extend financial coverage for 1.5 billion financially underserved people across the globe. There are several targets on your mind and, of course, a few concerns about how you’ll accomplish your company’s immediate objective of bringing these into the fold. In other words, you want to democratize the financial system by first educating your potential market.  You’ve been offered a fat paycheck and a tall order so you’ve hired me as your adviser to help with this task. Node is the Codeword Our first marketing campaign is to introduce the idea of nodes to our target market. I’ve come up with an interesting idea: let’s show them how dangerous the current fintech system could get. Bands of bad actors roam the internet, empowered by sophisticated hacking software, snooping on customers, hijacking entire banking apps, and carting off troves of customer data. We develop an ad that shows how we can prevent this cataclysmic conclusion by sharing that our technology depends on independent decentralized computers (known as nodes), all functioning as “checkers-and-balancers” on each other. No transaction is added to the blockchain unless and until all nodes approve that it is valid: a parliament of protocols if you like. So, if they’re out on a beach in Mexico chilling, half-drunk, with a Piña Colada in hand, they can be sure that no unauthorized transactions are added to the blockchain (or approved) until they give the word. This strikes you as really important because you recall that when you were CFO at LibertyWonk Inc., a Web-2 based fintech, some sleazy fellow with passable tech credentials capered away with $ 30 million through a Business Email Compromise scheme, ruining lives and LibertyWonk Inc. itself. You laugh, telling me about a certain Hushpuppi, who commandeered atleast $24 million from his mastery of BEC and other cybercrimes. a parliament of protocols a parliament of protocols Hushpuppi, Hushpuppi Copy That! Now, you’re ensconced in your aubergine-colored leather armchair grinning at me. You’re starting to like this. “Tell me more,” you say. I go on sharing that we’ll introduce a series of social media posts reiterating that each of our nodes stores a copy of the entire node system, allowing anybody to verify the provenance of every transaction. In other words, there’s no hiding behind forged records. I flash a web page from my mobile device in front of you, taking you back on memory lane to remind you of the lessons learned from the investigations of the Bank of Credit and Commerce International (BCCI). Investigations which commenced in 1992 and concluded in 2012, cost $656 million paid to firms of lawyers and accountants and resulted in innumerable heartbreaks in victims of the humongous heist. Bank of Credit and Commerce International (BCCI). Bank of Credit and Commerce International (BCCI) Calling all Computers! Nodes (computers, remember?) are linguistic linkers. In other words, they like to communicate with each other just as humans do. Beneath the humming sounds of their hardware, they are asking each other questions about the state of the blockchain just the same way you, as the CEO, would ring the CFO to make yet another inquiry about the financial health of ZBlockBuilders Inc., on a Friday afternoon as you’re getting set to proceed on vacation. While nodes may not go on holiday, they’re still super concerned about how well records are being kept, and believe me, they’ll ask. Something like “hey, are there any new transactions and blocks on our chain, and what are their details?” They constantly check on each other to see how things are going and this involves a process called the consensus mechanism. This simply means that they use a special mode of confirmation to check the state of the blockchain. I’ll share two types of consensus mechanisms. How do Consensus Mechanisms Function? Proof of Work This is native to the Bitcoin core and what it does is require a block miner to solve some truly complex piece of math to create a new block. Once the enterprising mathematician unlocks a new block, it immediately is broadcast to the entire blockchain (just like when Rafiki proudly held the newborn lion cub for all the jungle to see in The Lion King). It more or less says “A new block is born” but without the drama of lions and other animals involved. Proof of Stake Now, this is native to Ethereum and is somewhat different from how things play out in the Bitcoin core. With the proof of stake mechanism, you’re a validator and not a miner (note the distinction). So you’re not actively mining as such. Instead, you’re expected to stake 32 ETH (thirty-two Ethereum) and have a validator software ready and running in the hopes that you’re selected to propose new blocks to be added to the blockchain. If you act right, you’ll get some rewards for it. If you’re considered irresponsible (acting badly or staying offline for too long: slashing) you’re punished by having to lose some of your ETH (Ethereum). Decentralized and Defended The overwhelming beauty of nodes in the blockchain is that each node is a separate entity of its own, acting independently by itself but unanimously with its peers.  Nodes cannot be compelled to approve transactions or fooled by the witty disguises of questing cybercriminals with suggestive emails, until certain rules are collectively adhered to. There is no central system, as we have in current fintech configurations, that can be manipulated or harassed to relinquish value. Nodes function in a trustless environment. That does not mean that they abhor trust, rather they do not rely on trust or goodwill to function. They are ruthless and reliable executors. In Conclusion Essentially, nodes are the librarians, accountants and security guards of the blockchain. They communicate with each other to keep records of the particulars of the blockchain, ensuring that all transactions are properly accounted for and blocking (no pun intended) any unauthorized interactions. Lunch Break,Chief I see that we’re getting somewhere with my marketing campaign proposals because you’re handing me a glass of orange juice. I’m getting quite hungry too. So, if you like, I’ll come back to offer some more advice after the break. Featured Image courtesy of Lalmch on Pixabay Featured Image courtesy of Lalmch on Pixabay Lalmch on Pixabay

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