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Digital commerce predictions for 2021

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Digital commerce predictions for 2021
By Mike Monty

Prior to 2020, many Canadian businesses were lagging in terms of digital transformation. The global pandemic has permanently altered the way businesses operate. This year, nearly six in 10 organizations have accelerated their digital transformations to meet health and safety guidelines and keep up with changing consumer shopping behaviours. While online shopping isn’t new, this year proved that it’s here to stay, and there is no turning back.

What does the future of digital commerce look like following a year of accelerated transformation? These are some of my predictions on some of the top trends of 2021 as the pandemic, and other factors continue to influence consumer buying behaviour.

Augmented reality redefines retail

Augmented Reality (AR) applications have been on the rise in recent years with virtual “try-before-you-buy” experiences. Due to pandemic related lockdowns, the trend is only increasing as retail brands no longer have the live interaction with their customers they previously depended on.

AR helps bridge the gap between online and offline retail experiences with use cases ranging from previewing furniture and products in your home – from brands like IKEA and Home Depot – to virtually trying on a pair of glasses in Warby Parker’s mobile app without ever having to leave the couch. In terms of more recent innovation, just last week, Google announced its move into the AR space by launching an AR-powered cosmetics try-on experience on Google Search. Once a nice-to-have feature, AR is quickly becoming an essential technology for retailers.

AI takes customer service to new heights

While the past few years have allowed many companies to dip their toe into artificial intelligence, 2020 proved to be the year to dive in headfirst. Newer developments have seen AI-driven technology moving from back-of-house to front-of-house, with customer-facing initiatives such as AI-powered chatbots that enable users to perform their everyday tasks more efficiently, automate customer conversations, predict customer behaviour and increase customer retention.

In 2021, AI will be leveraged to enhance the customer experience by delivering uber personalized guidance and recommendations. For businesses that leverage AI to collect data, the more data they continue to collect and optimize a customer and predict products and services, the more they can build a compelling shopping experience. Add more data, and AI can learn and infer user preference, delivering best-in-class personalization.

The online shopping experience gets personal

The pandemic is driving e-commerce competition to record heights as Canadian consumers spend $52 billion on retail e-commerce this year, increasing 20.7 percent compared to 2019. While sales are booming, it is increasingly challenging for retailers to cut through the noise. Consumers now seek richer experiences when they shop. With the absence of brick-and-mortar, the digital shopping experience requires immersive, informative, and personalized tools.

The future of personalized experiences will be one where retailers can leverage customer data to create one-to-one personalization. More and more, customers will start to receive offers that are highly targeted at them, as individuals, with products, offers, and communications that are uniquely relevant to them. International cosmetics retailer, Sephora, is a prime example of online personalization done right from its personalized emails, Beauty Insider loyalty program and in-store technology. Shoppers’ Beauty Insider profiles are unified across Sephora.com and its mobile app and can be accessed in store to personalize consumers’ shopping experiences, no matter their entry point.

We’re likely to see brands increase their investment in personalization tools to help them stand out in a crowd and prove they know their customers best.

The rise of voice commerce

Voice assistants are seeing their use cases expand beyond checking the weather, operating smart home devices, or searching fun facts on Google. They’ve been quietly taking over the e-commerce industry. Given that we spent more time at home this year, we’ve seen increased interactions with voice assistants as families adopted new technologies and habits around their newly disrupted routines.

The more opportunities consumers have to engage with new technology, such as voice assistants, the more it allows long-lasting habits to set in. This is likely why this year saw a significant growth of independent voice assistants. For example, Houndify is a voice AI platform that allows brands to add smart, conversational interfaces to an Internet connection.

In terms of where voice can take retailers, it has the potential to help brands improve the way they interact with customers, providing more seamless, conversational customer journeys that shepherd them through purchase funnels, customer service encounters, and other types of payments and transactions. 

Social commerce is trending

Social commerce made major strides this year as social platforms evolved to meet consumers where they spend most of their time, and I anticipate this trend will only continue to accelerate in 2021. In May, Facebook launched Facebook Shops, enabling businesses to set up a single online store for customers to access Facebook and Instagram. Businesses small and large now have a simplified way to build an e-commerce outlet on the world’s leading social network, and consumers are presented with a frictionless shopping experience without ever leaving the social app. More recently, Instagram has been testing its Shop tab as an evolving e-commerce tool, providing businesses with new revenue opportunities.

While social commerce enables brands to focus on direct selling as the key priority, it also presents an opportunity to increase audience engagement and create awareness as over half (60 per cent) of Instagram’s users learn about new products on the app. Given three in four Canadians (77 per cent) use Facebook daily, and Instagram coming in a close second with 69 per cent daily users, 2021 will be the year to capitalize on this market and increase conversions via social commerce.

These are just two examples of social platforms with built-in checkout functionalities designed to streamline the online shopping experience and facilitate more immediate purchase behaviour in response to user actions. With the increased usage of TikTok and YouTube, the possibilities of social commerce are endless.

Optimize the shopping experience for mobile

As lockdown rules change on a week-by-week basis, many consumers are left with lingering fears over renewed outbreaks, making them wary of returning to stores. Discomfort with physical shopping forced consumers to try digital and mobile commerce in new areas. Grocery shopping is a prime example.

Research from earlier this year found that nearly half of Canadians surveyed said they had bought groceries online in the past six months. Among those who ordered groceries online, 53 per cent said this would be something they would continue to do so long after the pandemic is over. To reach the rising number of mobile customers, Walmart Canada rolled out mobile check-in across the country this fall, so customers can check-in for their grocery orders while on route, making the pickup speed quicker.

As customers continue to make more purchases using mobile devices, brands will need to provide consistent experiences across all devices, from desktop to tablet to mobile, for their online store or risk shopping cart abandonment and above-average bounce rates. The mobile shopping gains we’ve seen this year will likely stick post-pandemic, and I expect the effects of the pandemic will only accelerate long-term trends in mobile usage.

With roughly half (48 per cent) of Canadians using e-commerce platforms more often now than pre-pandemic, technology needs to be part of your go-to-market and business strategy. Consider if your brand is leveraging any of the above-mentioned trends, and if not, how could adoption help your company keep pace with changing consumer habits? 

If 2020 has taught us anything, it’s that digital transformation isn’t a concept but a reality.

Source: https://www.fintechnews.org/digital-commerce-predictions-for-2021/

AI

How Machine Learning is Being Applied to Software Development

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When Elon Musk proposed the idea of autonomous vehicles, everyone assumed it to be a hypothetical dream and never took it seriously. However, the same vehicles are now on the roads, being one of the top-selling cars in the United States.

The applications of artificial intelligence and machine learning are visible in all areas, from Google Photos in your smartphone to Amazon’s Alexa at your home, and software development is no exception. AI has already changed the way iOS and Android app developers work.

Machine learning can enhance the way a traditional software development cycle works. It allows a computer to learn and improve from the experiences without the need for programming. The sole purpose of AI and ML is to allow computers to learn automatically.

Moreover, being a software developer, you might need to specify minute details to let your computer know what it has to do. Developing software integrated with machine learning can help you make a significant difference in your developing experience.

Machine Intelligence is the last invention that humanity will ever need to make!

When it comes to how machine learning and AI help developers, only the sky’s the limit. Taking it even broader, AI has always transformed every industry it has ever entered. Here’s a quick rundown of stats that convey the same:

As the figures stated, artificial intelligence and machine learning are surely transforming the world, and the development industry is no exception. Let’s have a look at how it can help you write flawless code, deploy, and rectify bugs.

AI and ML in Development – How Does This Benefit Software Developers?

Whether you’re a person working as an android app developer or someone who writes codes for a living, you might have wondered what AI has in it for you. Here’s how developers can harness the capabilities of machine learning and AI:

1. Controlled Deployment of Code

AI and machine learning technologies help in enhancing the efficiency of code deployment activities required in development. In the development spectrum, the deployment mechanisms include a development phase where you need to upgrade your programs and applications to a newer version.

However, if you fail to execute the process properly, you need to face several risks including corruption of the software or application. With the help of AI, you can easily prevent such vulnerabilities and upgrade your code with ease.

2. Bugs and Error Identification

With the advancements in Artificial intelligence, the coding experience is getting even better and improved. It allows developers to easily spot bugs in their code and fix them instantly. They don’t have to read their code, again and again, to find potential flaws in their code anymore.

Several machine learning algorithms can automatically test your software and suggest changes.

AI-powered testing tools are certainly saving a plethora of time to developers and help them deliver their projects faster.

3. Secure Data Storage

With the ever-growing transfer of data from numerous networks, cybersecurity experts often find it complex and overwhelming to monitor every activity going on in the network. Due to this, there might be a threat or breach that may go away unnoticed, without producing any alerts.

However, with the capabilities of artificial intelligence, you can avoid issues such as delayed warnings and get notified about bugs in your code as soon as possible. These tools gradually lessen the time it takes a company to get notified about a breach.

4. Strategic Decision Making and Prototyping 

It’s a habit for a developer to go through a hefty and endless list of what needs to be included in a project or code they’re making. However, technological solutions driven by machine learning and AI are capable of analyzing and evaluating the performance of existing applications.æ

With the help of this technology, both business leaders and engineers can work on a solution that cuts down the risk and maximizes the impact. By using natural language and visual interfaces, technical domain experts can develop technologies faster.

5. Skill Enhancement

To keep evolving with the upcoming technology, you need to evolve with the advancement in technology. For the freshers and young developers, AI-based tools help them to collaborate on various software programs and share insights with fellow team members and seniors to learn more about the programming language and software.

Parting Words

While machine learning and AI simplify numerous tasks and activities related to software development, it doesn’t mean that testers and developers are going to lose their jobs. A hired android app developer will still write codes in a faster, better, and more efficient environment, supported by AI and machine learning.

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Artificial Intelligence

Generative Adversarial Transformers: Using GANsformers to Generate Scenes

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@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:

Chapters:
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

References

Paper: https://arxiv.org/pdf/2103.01209.pdf
Code: https://github.com/dorarad/gansformer
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 https://github.com/dorarad/gansformer​.”

Video Transcript

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

00:00

the basically leveraged transformers

00:02

attention mechanism in the powerful stat

00:04

gun 2 architecture to make it even more

00:06

powerful

00:10

[Music]

00:14

this is what’s ai and i share artificial

00:16

intelligence news every week

00:18

if you are new to the channel and would

00:19

like to stay up to date please consider

00:21

subscribing to not miss any further news

00:24

last week we looked at dali openai’s

00:27

most recent paper

00:28

it uses a similar architecture as gpt3

00:31

involving transformers to generate an

00:33

image from text

00:35

this is a super interesting and complex

00:37

task called

00:38

text to image translation as you can see

00:41

again here the results were surprisingly

00:43

good compared to previous

00:45

state-of-the-art techniques this is

00:47

mainly due to the use of transformers

00:49

and a large amount of data this week we

00:52

will look at a very similar task

00:54

called visual generative modelling where

00:56

the goal is to generate a

00:58

complete scene in high resolution such

01:00

as a road or a room

01:02

rather than a single face or a specific

01:04

object this is different from delhi

01:06

since we are not generating the scene

01:08

from a text but from a trained model

01:10

on a specific style of scenes which is a

01:13

bedroom in this case

01:14

rather it is just like style gun that is

01:17

able to generate unique and non-existing

01:19

human faces

01:20

being trained on a data set of real

01:22

faces

01:24

the difference is that it uses this gan

01:26

architecture in a traditional generative

01:28

and discriminative way

01:29

with convolutional neural networks a

01:32

classic gun architecture will have a

01:34

generator

01:35

trained to generate the image and a

01:36

discriminator

01:38

used to measure the quality of the

01:40

generated images

01:41

by guessing if it’s a real image coming

01:43

from the data set

01:44

or a fake image generated by the

01:46

generator

01:48

both networks are typically composed of

01:50

convolutional neural networks where the

01:52

generator

01:53

looks like this mainly composed of down

01:56

sampling the image using convolutions to

01:58

encode it

01:59

and then it up samples the image again

02:02

using convolutions to generate a new

02:04

version

02:05

of the image with the same style based

02:07

on the encoding

02:08

which is why it is called style gun then

02:12

the discriminator takes the generated

02:14

image or

02:15

an image from your data set and tries to

02:17

figure out whether it is real or

02:18

generated

02:19

called fake instead they leverage

02:22

transformers attention mechanism

02:24

inside the powerful stargane 2

02:26

architecture to make it

02:27

even more powerful attention is an

02:30

essential feature of this network

02:32

allowing the network to draw global

02:34

dependencies between

02:36

input and output in this case it’s

02:39

between the input at the current step of

02:41

the architecture

02:42

and the latent code previously encoded

02:44

as we will see in a minute

02:46

before diving into it if you are not

02:48

familiar with transformers or attention

02:50

i suggest you watch the video i made

02:52

about transformers

02:54

for more details and a better

02:55

understanding of attention

02:57

you should definitely have a look at the

02:58

video attention is all you need

03:01

from a fellow youtuber and inspiration

03:03

of mine janik

03:04

kilter covering this amazing paper

03:07

alright

03:07

so we know that they use transformers

03:09

and guns together to generate better and

03:12

more realistic scenes

03:13

explaining the name of this paper

03:15

transformers

03:17

but why and how did they do that exactly

03:20

as for the y they did that to generate

03:22

complex and realistic scenes

03:24

like this one automatically this could

03:26

be a powerful application for many

03:28

industries like movies or video games

03:30

requiring a lot less time and effort

03:33

than having an

03:34

artist create them on a computer or even

03:36

make them

03:37

in real life to take a picture of it

03:40

also

03:40

imagine how useful it could be for

03:42

designers when coupled with text to

03:44

image translation generating many

03:46

different scenes from a single text

03:48

input

03:48

and pressing a random button they use a

03:51

state-of-the-art style gun architecture

03:53

because guns are powerful generators

03:55

when we talk about the general image

03:58

because guns work using convolutional

04:00

neural networks

04:01

they are by nature using local

04:03

information of the pixels

04:05

merging them to end up with the general

04:07

information regarding the image

04:09

missing out on the long range

04:11

interaction of the faraway pixel

04:13

for the same reason this causes guns to

04:15

be powerful generators for the overall

04:18

style of the image

04:19

still they are a lot less powerful

04:21

regarding the quality of the small

04:23

details in the generated image

04:25

for the same reason being unable to

04:27

control the style of localized regions

04:30

within the generated image itself this

04:33

is why they had the idea to combine

04:34

transformers and gans in one

04:36

architecture they called

04:38

bipartite transformer as gpt3 and many

04:41

other papers already proved transformers

04:44

are powerful for long-range interactions

04:46

drawing dependencies between them and

04:48

understanding the context of text

04:50

or images we can see that this simply

04:53

added attention layers

04:54

which is the base of the transformer’s

04:56

network in between the convolutional

04:58

layers of both the generator and

05:00

discriminator

05:01

thus rather than focusing on using

05:03

global information and controlling

05:05

all features globally as convolutions do

05:07

by nature

05:08

they use this attention to propagate

05:10

information from the local pixels to the

05:12

global high level representation

05:14

and vice versa like other transformers

05:17

applied to images

05:18

this attention layer takes the pixel’s

05:20

position and the style gun to latent

05:23

spaces w

05:24

and z the latent space w is an encoding

05:27

of the input into an intermediate latent

05:30

space

05:30

done at the beginning of the network

05:32

denoted here

05:34

as a while the encoding z is just the

05:37

resulting features of the input at the

05:39

current step of the network

05:40

this makes the generation much more

05:42

expressive over the whole image

05:44

especially in generating images

05:46

depicting multi-object

05:48

scenes which is the goal of this paper

05:51

of course this was just an overview of

05:53

this new paper by facebook ai research

05:55

and stanford university

05:57

i strongly recommend reading the paper

05:59

to have a better understanding of this

06:00

approach it’s the first link in the

06:02

description below

06:03

the code is also available and linked in

06:05

the description as well

06:07

if you went this far in the video please

06:08

consider leaving a like

06:10

and commenting your thoughts i will

06:12

definitely read them and answer you

06:14

and since there’s still over 80 percent

06:16

of you guys that are not subscribed yet

06:18

please consider clicking this free

06:20

subscribe button

06:21

to not miss any further news clearly

06:23

explained

06:24

thank you for watching

06:33

[Music]

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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 datasciencetrivia.com

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.

Link: https://www.kaggle.com/learn/python

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.

Link: https://www.kaggle.com/learn/pandas

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.

Link: https://www.kaggle.com/learn/data-visualization

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.

Link: https://www.kaggle.com/learn/intro-to-machine-learning

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.

Link: https://www.kaggle.com/learn/intermediate-machine-learning

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.

Link: https://www.kaggle.com/c/titanic

Kaggle Playground Competition: Housing Prices

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

Link: https://www.kaggle.com/c/home-data-for-ml-course

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.

Link: https://www.coursera.org/learn/machine-learning

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.

Link: https://www.deeplearning.ai/deep-learning-specialization/

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.

Also published at https://towardsdatascience.com/if-i-had-to-start-learning-data-science-again-how-would-i-do-it-78a72b80fd93

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AI

Fetch.ai (FET) hits a 2-year high after DeFi integration and Bosch partnership

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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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Source: https://cointelegraph.com/news/defi-summer-2-0-gen-2-tokens-on-a-tear-amid-wider-market-slump

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Source: https://coingenius.news/fetch-ai-fet-hits-a-2-year-high-after-defi-integration-and-bosch-partnership/?utm_source=rss&utm_medium=rss&utm_campaign=fetch-ai-fet-hits-a-2-year-high-after-defi-integration-and-bosch-partnership

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