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Crypto and blockchain: What the Brazilian market can expect for 2021

Cointelegraph Brasil invited some of the country’s top crypto and blockchain experts to chart the next steps for the market.

The post Crypto and blockchain: What the Brazilian market can expect for 2021 first appeared on Blockchain Consultants.




2020 will be remembered as one of the most difficult years for contemporary societies: Countries and entire populations have faced lockdowns and economic crises, financial markets still suffer from the severe impacts of the economic recession, and more than 2 million lives have been taken by COVID-19.

Despite this, other sectors have been impacted in other ways during the severe global health crisis — which still seems far from over, even though vaccines are beginning to be distributed in wealthy countries. Economies have radically digitalized, hedge assets have attracted mistrust, and the crypto market has had one of its most important years since 2009, the year of Bitcoin’s (BTC) launch.

In fact, the crypto and blockchain markets have stood out in the face of a crisis that has spared almost no sector. Cryptocurrency funds are among the most profitable of the year, Bitcoin and the biggest altcoins reach new historic highs, large institutions and investors in the financial markets have allocated investments in Bitcoin, and blockchain technology has broken down barriers in the financial sector and in the production chains of the most varied of sectors.

Faced with a year of profound changes, what is to be expected for the future? Cointelegraph Brasil invited some of the country’s top crypto and blockchain experts to chart the next steps for the market.

Institutional investment

Institutional investment was highlighted in 2020, finally reaching the cryptosphere, and it promises another year of growth in 2021.

According to Rodrigo Borges, founding member of the Oxford Blockchain Foundation, large Bitcoin contributions by institutional investors — which have even bought more BTC than the production capacity of miners — will intensify in 2021: “Regarding Bitcoin, I imagine that there will be an increase in demand for institutional investors, enabling the emergence of new products with exposure to Bitcoin,” analyzed Borges. He also sees “2021 as a year of consolidation and strong development in the sector.”

As for Tatiana Revoredo, MIT blockchain expert and Cointelegraph Brasil columnist, the custody of cryptocurrencies by traditional financial institutions and the adoption of stablecoins will be key in the new year:

“In the financial sector, we will see applications for custody of crypto assets being launched in Brazil, with the possible participation of the traditional market. And if the regulatory authorities allow it, stablecoins will have an expressive role in the Brazilian market, with the turnover being able to quadruple in size.”

Crypto markets

Crypto markets experienced a year of extreme optimism — or greed, as demonstrated by the Crypto Fear & Greed Index. Bitcoin reached a dramatic bottom close at $3,800 in March, and it beat its 2017 historic high of $20,000 on Dec. 16. In Brazil, the currency set a new historical record in November when it reached $106,000 Brazillian reals.

Cointelegraph Markets reporter Marcel Pechman highlighted the behavior of the market despite the setbacks suffered during the year. He recalled: “The Bitcoin and Ethereum markets developed in 2020 as never before imagined, both in terms of trading volume, price and the contribution of renowned investors like Paul Tudor Jones and Stanley Druckenmiller.”

Pechman said that despite the crypto market suffering some setbacks, the impact of those setbacks on market performance was not so significant: “We had, for example, the US Department of Justice suing BitMEX — at the time, the largest derivatives exchange — and KuCoin’s $280 million hack, and none of those affected the market.”

Pechman also recalled that the 2020 DeFi race led to expensive transaction costs on the Ethereum network but did not impact market sentiment.

OriginalMy CEO Edilson Osório agreed with the promising future of the DeFi sector, but he cautioned against fraud:

“This is an experimental and very promising market, but it must be given extra attention because of malicious groups applying scams and fraud in general. As it is a very new market, platforms may have problems with hacks, and due to the great centralization that exists (even with many platforms presenting themselves as decentralized), there is still a risk of exit scams.”

About 2020’s innovations, and the digitalization imposed by the COVID-19 crisis, Pechman also said that it will go even deeper in 2021:

“Successive innovations, which include Taproot, Schnorr and Lightning Network in Bitcoin, in addition to the launch of Ethereum 2.0 phase 0, pave the way for the next wave, with increasingly larger, scalable applications, and interconnected with traditional finance. The final proof? Fidelity offers loans covered in cryptocurrencies.”

On the domestic markets, Osório is betting on the tokenization market in Brazil, which is already used by the country’s largest crypto exchange, Mercado Bitcoin. According to him, 2021 will be a year for “maturing the security tokens market.”

“Existing protocols are beginning to be well regarded by regulators, since most of them provide for greater participation and visibility on the part of the regulator itself and allow the mitigation of various risks inherent in this market. In this race, there is a great chance that Brazil will gain prominence because the local regulator has established a regulatory sandbox and the first projects are already beginning to mobilize to have their applications running in a more legally secure environment,” – noted Osório.

Another player at the Brazilian crypto markets, João Paulo Mayall — head of operations at QR Asset Management — is also optimistic about the tokenization market in 2021. He highlighted the role of regulators in the sector’s expansion in the South American country: “I believe that the future is the tokenization of assets, debentures, court bonds, government debts. Brazil is very advanced in its banking system and we will have many surprises in this sector, so I am very optimistic. Tokenization is a billion-dollar market, but it lacks the infrastructure. Innovation came in front of the regulators, but I think they are open to listening and working on it. I think [the regulation] will happen next year, even before March 2021.”

Finally, blockchain expert Tatiana Revoredo argued that crypto adoption in Brazil, which saw its currency melt in 2020, will intensify, with Bitcoin once again asserting itself as an economic-protection asset. She believes that the crypto markets will see “an increase in the interest of Brazilians, with consequent increase in the Brazilian market, with a prominent role for Bitcoin being adopted as a protective asset.”

CBDCs and national governments

The digitization of economies has placed the discussion of central bank digital currencies, or CBDCs, at the center of debates by financial authorities around the world. One of the countries that has definitely entered this race is China, which is already conducting real tests of the digital yuan in the country. Its main geopolitical rival, the U.S., announced that for the time being, it does not intend to digitize the dollar, but it is already seeing internal pressure from not following the Chinese leadership in the sector.

The Central Bank of Brazil has also commented on the transformation of the Brazillian real into a digital currency a few times, although there are no concrete plans for that in the short term.

Osório believes the European Union will join the hype soon, further accelerating the global race for CBDCs: “Although China appears to be leading the CBDC race, other countries are also beginning to move in this direction. Among them, Estonia, which recently started an internal consultation for the launch of its currency in the digital version. In particular, I believe that in Europe a more comprehensive and organized movement should take place in this sense, given the incentives promoted by the European Union.”

Many experts try to predict the impacts of CBDCs on economies — one of the main concerns of economic regulators. Governments, which largely study the adoption of blockchain in their public processes, should also enter the debate on privacy and the digitization of money.

According to Tatiana Revoredo, “in the government sector, the forecast is for the growth of [blockchain] applications in document registration and health applications, as well as a greater concern, by the citizens, regarding the relationship between privacy and CBDC.” She also claims that payments processors should closely monitor this innovation:

“Those who should be more attentive to these movements are the means of payment, such as PayPal and their peers. They will have to look deeply into their business models as soon as governments start issuing their currencies digitally. ”

Blockchain adoption

Governments have also viewed blockchain technology through a positive lens. In Brazil and Latin America, several state entities already use the technology to certify documents, including customs and notary offices. Big companies are also adopting blockchain to certify production, with use cases that are only expected to grow going forward.

Borges said that the acceleration of blockchain adoption by large companies and governments can positively impact crypto assets:

“Within the scope of blockchain technology, I see the development of interesting solutions, with the increasing involvement of traditional players, especially in the financial and agribusiness sectors, which may result in increased liquidity for certain assets.”

Revoredo agreed and highlighted the advancement of technology in the agricultural sector: “There has been a significant advance in agribusiness, with use in the identification of devices (drones, for example), integration with IoT and artificial intelligence to provide greater reliability and certify quality of agricultural production.”

Osório defended the growth of the blockchain market in 2020 and its prospects for the near future: “When we look at advances in blockchain with applications beyond digital currency, we see a growing market in the area of ​​decentralized digital identity, including with the approach of governments. We have seen movements in governments in the US and Japan, interested in modernizing their digital governance models. And the pandemic has certainly helped to accelerate and advance discussions on the issue around the world, as it understands that the digitization of analog and traditional services is a necessity.”

The end of 2020 was a milestone that closed out one of the most dramatic years in the history of contemporary societies, but it also revealed ways to combat global economic and health crises.

Blockchain technology has helped societies fight corruption, adopt more transparent processes and even contributed to the certification of medicines and vaccines during the most serious health crisis of the last 100 years, in addition to helping companies to improve procedures, products and services.

Meanwhile, Bitcoin has strengthened as an economic protection and investment product, has attracted institutional investment giants, and — together with other crypto technologies — has even laid the foundation for central banks around the world to start implementing their own digital currencies.

We still do not know the depth of the revolution we are experiencing with the digitalization of societies and the weakening of national currencies around the world, but by the end of 2021, we will certainly know many of the answers to the questions that still plague us at the beginning of this new year.

Crypto and blockchain: What the Brazilian market can expect for 2021



Artificial Intelligence

Generative Adversarial Transformers: Using GANsformers to Generate Scenes




Author profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!

Watch the Video:

0:00​ Hey! Tap the Thumbs Up button and Subscribe. You’ll learn a lot of cool stuff, I promise.
0:24​ Text-To-Image translation
0:51​ Examples
5:50​ Conclusion


Complete reference:
Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv., abstract:

“We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables longrange interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model’s strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-theart results in terms of image quality and diversity, while enjoying fast learning and better data efficiency. Further qualitative and quantitative experiments offer us an insight into the model’s inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at​.”

Video Transcript

Note: This transcript is auto-generated by Youtube and may not be entirely accurate.


the basically leveraged transformers


attention mechanism in the powerful stat


gun 2 architecture to make it even more






this is what’s ai and i share artificial


intelligence news every week


if you are new to the channel and would


like to stay up to date please consider


subscribing to not miss any further news


last week we looked at dali openai’s


most recent paper


it uses a similar architecture as gpt3


involving transformers to generate an


image from text


this is a super interesting and complex


task called


text to image translation as you can see


again here the results were surprisingly


good compared to previous


state-of-the-art techniques this is


mainly due to the use of transformers


and a large amount of data this week we


will look at a very similar task


called visual generative modelling where


the goal is to generate a


complete scene in high resolution such


as a road or a room


rather than a single face or a specific


object this is different from delhi


since we are not generating the scene


from a text but from a trained model


on a specific style of scenes which is a


bedroom in this case


rather it is just like style gun that is


able to generate unique and non-existing


human faces


being trained on a data set of real




the difference is that it uses this gan


architecture in a traditional generative


and discriminative way


with convolutional neural networks a


classic gun architecture will have a




trained to generate the image and a




used to measure the quality of the


generated images


by guessing if it’s a real image coming


from the data set


or a fake image generated by the




both networks are typically composed of


convolutional neural networks where the




looks like this mainly composed of down


sampling the image using convolutions to


encode it


and then it up samples the image again


using convolutions to generate a new




of the image with the same style based


on the encoding


which is why it is called style gun then


the discriminator takes the generated


image or


an image from your data set and tries to


figure out whether it is real or




called fake instead they leverage


transformers attention mechanism


inside the powerful stargane 2


architecture to make it


even more powerful attention is an


essential feature of this network


allowing the network to draw global


dependencies between


input and output in this case it’s


between the input at the current step of


the architecture


and the latent code previously encoded


as we will see in a minute


before diving into it if you are not


familiar with transformers or attention


i suggest you watch the video i made


about transformers


for more details and a better


understanding of attention


you should definitely have a look at the


video attention is all you need


from a fellow youtuber and inspiration


of mine janik


kilter covering this amazing paper




so we know that they use transformers


and guns together to generate better and


more realistic scenes


explaining the name of this paper




but why and how did they do that exactly


as for the y they did that to generate


complex and realistic scenes


like this one automatically this could


be a powerful application for many


industries like movies or video games


requiring a lot less time and effort


than having an


artist create them on a computer or even


make them


in real life to take a picture of it




imagine how useful it could be for


designers when coupled with text to


image translation generating many


different scenes from a single text




and pressing a random button they use a


state-of-the-art style gun architecture


because guns are powerful generators


when we talk about the general image


because guns work using convolutional


neural networks


they are by nature using local


information of the pixels


merging them to end up with the general


information regarding the image


missing out on the long range


interaction of the faraway pixel


for the same reason this causes guns to


be powerful generators for the overall


style of the image


still they are a lot less powerful


regarding the quality of the small


details in the generated image


for the same reason being unable to


control the style of localized regions


within the generated image itself this


is why they had the idea to combine


transformers and gans in one


architecture they called


bipartite transformer as gpt3 and many


other papers already proved transformers


are powerful for long-range interactions


drawing dependencies between them and


understanding the context of text


or images we can see that this simply


added attention layers


which is the base of the transformer’s


network in between the convolutional


layers of both the generator and




thus rather than focusing on using


global information and controlling


all features globally as convolutions do


by nature


they use this attention to propagate


information from the local pixels to the


global high level representation


and vice versa like other transformers


applied to images


this attention layer takes the pixel’s


position and the style gun to latent


spaces w


and z the latent space w is an encoding


of the input into an intermediate latent




done at the beginning of the network


denoted here


as a while the encoding z is just the


resulting features of the input at the


current step of the network


this makes the generation much more


expressive over the whole image


especially in generating images


depicting multi-object


scenes which is the goal of this paper


of course this was just an overview of


this new paper by facebook ai research


and stanford university


i strongly recommend reading the paper


to have a better understanding of this


approach it’s the first link in the


description below


the code is also available and linked in


the description as well


if you went this far in the video please


consider leaving a like


and commenting your thoughts i will


definitely read them and answer you


and since there’s still over 80 percent


of you guys that are not subscribed yet


please consider clicking this free


subscribe button


to not miss any further news clearly




thank you for watching



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Artificial Intelligence

How I’d Learn Data Science If I Were To Start All Over Again




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@santiviquezSantiago Víquez

Physicist turned data scientist. Creator of

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.

I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.

So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.

As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.

Kaggle micro-courses

I know it may be weird to start here, many would prefer to start with the heaviest foundations and math videos to fully understand what is happening behind each ML model. But from my perspective starting with something practical and concrete helps to have a better view of the whole picture.

In addition, these micro-courses take around 4 hours/each to complete so meeting those little goals upfront adds an extra motivational boost.

Kaggle micro-course: Python

If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.


Price: Free

Kaggle micro-course: Pandas

Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.


Price: Free

Kaggle micro-course: Data Visualization

Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.


Price: Free

Kaggle micro-course: Intro to Machine Learning

This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.


Precio: Free

Kaggle micro-course: Intermediate Machine Learning

This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.


Price: Free

Let’s stop here for a moment. It should be clear that these 5 micro-courses are not going to be a linear process, you are probably going to have to come and go between them to refresh concepts. When you are working in the Pandas one you may have to go back to the Python course to remember some of the things you learned or go to the pandas documentation to understand new functions that you saw in the Introduction to Machine Learning course. And all of this is fine, right here is where the real learning is going to happen.

Now, if you realize these first 5 courses will give you the necessary skills to do exploratory data analysis (EDA) and create baseline models that later you will be able to improve. So now is the right time to start with simple Kaggle competitions and put in practice what you’ve learned.

Kaggle Playground Competition: Titanic

Here you’ll put into practice what you learned in the introductory courses. Maybe it will be a little intimidating at first, but it doesn’t matter it’s not about being first on the leaderboard, it’s about learning. In this competition, you will learn about classification and relevant metrics for these types of problems such as precision, recall, and accuracy.


Kaggle Playground Competition: Housing Prices

In this competition, you are going to apply regression models and learn about relevant metrics such as RMSE.


By this point, you already have a lot of practical experience and you’ll feel that you can solve a lot of problems, buuut chances are that you don’t fully understand what is happening behind each classification and regression algorithms that you have used. So this is where we have to study the foundations of what we are learning.

Many courses start here, but at least I absorb this information better once I have worked on something practical before.

Book: Data Science from Scratch

At this point, we will momentarily separate ourselves from pandas, scikit-learn ,and other Python libraries to learn in a practical way what is happening “behind” these algorithms.

This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.

Link: Amazon

Price: $26 aprox

If you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums, and explore new methods that you find in other participants’ solutions.

Online Course: Machine Learning by Andrew Ng

Here we are going to see many of the things that we have already learned but we are going to watch it explained by one of the leaders in the field and his approach is going to be more mathematical so it will be an excellent way to understand our models even more.


Price: Free without the certificate — $79 with the certificate

Book: The Elements of Statistical Learning

Now the heavy math part starts. Imagine if we had started from here, it would have been an uphill road all along and we probably would have given up easier.

Link: Amazon

Price: $60, there is an official free version on the Stanford page.

Online Course: Deep Learning by Andrew Ng

By then you have probably already read about deep learning and play with some models. But here we are going to learn the foundations of what neural networks are, how they work, and learn to implement and apply the different architectures that exist.


Price: $49/month

At this point it depends a lot on your own interests, you can focus on regression and time series problems or maybe go more deep into deep learning.

I wanted to tell you that I launched a Data Science Trivia game with questions and answers that usually come out on interviews. To know more about this Follow me on Twitter.

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AI (FET) hits a 2-year high after DeFi integration and Bosch partnership




As some brand-name decentralized finance (DeFi) tokens sputter, a crop of new projects have emerged that are catching strong bids on the back of aggressive yield farming programs, generous airdrops, and significant technical advances. 

It’s a set of outlier projects pushing forward on both price and fundamentals that has led one crypto analyst, eGirl Capital’s mewny, to brand them as DeFi’s “Gen 2.”

Mewny, who in an interview with Cointelegraph pitched eGirl Capital as “an org that takes itself as a very serious joke,” says that Gen 2 tokens have garnered attention due to their well-cultivated communities and clever token distribution models — both of which lead to a “recursive” price-and-sentiment loop. 

“I think in terms of market interest it’s more about seeking novelty and narrative at this stage in the cycle. Fundamental analysis will be more important when the market cools off and utility is the only backstop to valuations. Hot narratives tend to trend around grassroots projects that have carved out a category for themselves in the market,” they said.

While investors might be eager to ape into these fast-rising new tokens, it’s worth asking what the projects are doing, whether they’re sustainable, and if not how much farther they have to run.

Pumpamentals or fundamentals?

The Gen 2 phenomena echoes the “DeFi summer” of last year, filled with “DeFi stimulus check” airdrops, fat farming APYs, and soaring token prices — as well as a harrowing spate of hacks, heists, and rugpulls

However, mewny says that there’s a population of investors that emerged from that period continuously looking for technical progress as opposed to shooting stars. 

“There are less quick “me too” projects in defi. An investor may think that those projects never attracted much liquidity in the first place but they overestimate the wisdom of the market if that’s the case. They did and do pull liquidity, especially from participants who felt priced out or late to the first movers.This has given the floor to legitimate projects that have not stopped building despite the market’s shift in focus. ”

One such Gen 2 riser pulling liquidity is Inverse Finance. After the launch of a yield farming program for a forthcoming synthetic stablecoin protocol, the Inverse Finance DAO narrowly voted to make the INV governance token tradable. As a result, the formerly valueless token airdrop of 80 INV is now priced at over $100,000, likely the most lucrative airdrop in Defi history. 

Another Gen 2 star is Alchemix — one of eGirl Capital’s first announced investments. Alchemix’s protocol also centers on a synthetic stablecoin, alUSD, but generates the stablecoin via collateral deposited into Yearn.Finance’s yield-bearing vaults. The result is a token-based stablecoin loan that pays for itself — a new model that eGirl thinks could become a standard.

“eGirl thinks trading yield-bearing interest will be an important primitive in DeFi. Quantifying and valuing future yield unlocks a lot of usable value that can be reinvested in the market,” they said.

The wider markets appears to agree with eGirl’s thesis, as Alchemix recently announced that the protocol has eclipsed half a billion in total value locked:

Staying power?

By contrast, governance tokens for many of the top names in DeFi, such as Aave and Yearn.Finance, are in the red on a 30-day basis. But even with flagship names stalling out, DeFi’s closely-watched aggregate TVL figure is up on the month, rising over $8.4 billion to $56.8 billion per DeFi Llama — progress carried in part on the back of Gen 2 projects. 

The comparatively wrinkled, desiccated dinosaurs of DeFi may have some signs of life left in them, however. Multiple major projects have significant updates in the works, including Uniswap’s version 3, Sushiswap’s Bentobox lending platform, a liquidity mining proposal working through Aave’s governance process, and Balancer’s version 2.

These developments could mean that DeFi’s “Gen 2” phenomena is simply a temporary, intra-sector rotation, and that the “majors” are soon to roar back. It would be a predictable move in mewny’s view, who says “every defi protocol needs at least 1 bear market to prove technical soundness.”

What’s more, according to mewny some of the signs of market irrationality around both Gen 2 tokens as well as the wider DeFi space — such as triple and even quadruple-digit farming yields — may be gone sooner rather than later.

“I don’t think it’s sustainable for any project in regular market conditions. We are not in regular conditions at the moment. Speculators have propped up potentially unsustainable DeFi protocols for a while now.”

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Artificial Intelligence

iMe Messenger and Crypto Wallet Review: Designed for Secure Chats and Low-Fee DeFi




The iMe Messenger and Crypto Wallet, developed by iMe Lab, is a five-star multi-functional client powered by Telegram API. Telegram is a privacy-focused messenger and one of the world’s largest applications.

Leveraging Telegram APIs

This means the platform incorporates some of the critical features of Telegram and aspects of decentralized finance (DeFi), essentially becoming a Telegram client with crypto wallet support. 

Aiming to revolutionize messaging, the iMe Messenger also has integrated tools and leverages artificial intelligence and blockchain. 

Combining all these features and integration makes the iMe Messenger and Crypto Wallet multi-functional, fast, powerful, and private. 

All user data are secured in a multi-cloud, ensuring privacy is protected–an impeachable human right.

iMe Messenger Features

The iMe Messenger is designed to work for chats. 

Accordingly, the client has specific topics and chats folders complete with advanced settings for tabs and folders. 

This, in turn, introduces unparalleled levels of flexibility, allowing the end-user to sort and archive chats. At the same time, users can listen to music from the app’s music player while chatting from the same device.

For easy client onboarding, the iMe Messenger has specifically designed the front end and the user interface to be easy to use, regardless of a user’s tech-savviness. 

For instance, the messenger boasts a side menu setting, and users can filter contacts. There is also a multi-panel in chats. 

At the same time, they can disable the bottom bar in their respective channels. All iMe Messenger settings can also be backed up in the cloud. It is convenient, especially for users who may want to change devices but keen not to lose previous settings.

The messenger goes a notch higher for differentiation, especially in the array of tools it avails to users. Once installed and chatting, a user can multi-forward chats. Concurrently, they can also multi-reply in groups. 

Users chatting in multilingual groups/channels can also auto-translate incoming and outgoing messages. Additionally, users can convert voice messages into text, extract text from images, and make descriptions of images. 

Those in the move or busy not to reply can activate neurobots-assistants that can step in and offer quick replies in phrases and GIFs. 

The iMe Messenger Crypto Wallet is also multi-currency and forms a critical part of the iMe Smart Platform. Their merger of exciting features of Telegram and support of cryptocurrency—and by extension DeFi, makes iMe one of the most advanced messenger applications. 

iMe Messenger has been downloaded over 634k times. The application has over 93k active users, of whom most use Android devices.

Cryptocurrency Support and DeFi

The application is also a cryptocurrency wallet supporting some of the leading coins and DeFi tokens. Top of the list is ETH and USDT. Ether (ETH) is the native currency of the Ethereum network that’s now the home of DeFi. 

According to trackers, there are over $40 billion of digital assets managed by Ethereum-based DeFi protocols. The USDT token is an enabler, allowing users to interact with the various DeFi dApps. 

Already, iMe Messenger has integrated Uniswap. The DeFi protocol is the largest decentralized exchange globally, allowing the trustless exchange of tokens through incentivized pools where liquidity providers (LPs) earn rewards. 

Beyond DeFi tokens, iMe Messenger enables secure storage of LIME and OLCF. Through the platform, users can safely store, trade, and transfer these coins within chats. The platform also has its native currency, AiCoin. It enables in-app monetization and acts as a means of calculation.

For faster and seamless entry into crypto, iMe Messenger has partnered with Simplex. Users can purchase supported cryptocurrencies using their credit cards. This helps in driving adoption and improving awareness of cryptocurrencies. 

Simplex is a regulated financial institution that allows users to deploy a range of channels as payment options. 

To further enhance the user experience, especially those actively involved in DeFi, the iMe Messenger has integrated with the Binance Smart Chain (BSC). 

The smart contracting platform fronted by Binance is compatible with the Ethereum Virtual Machine (EVM), and transacting via the network is relatively cheap and faster since the platform is scalable. 


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