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The cryptocurrency market grew 600% year-to-date, with its value now standing at a staggering $2.8 trillion according to data from cryptocurrency market analytics company, Footprint Analytics. This has been fueled by a surge in development and investment into the space, triggering a need for stable assets to underpin evermore volatile asset pairs. Consequently, there has been a rise in the number and value of stablecoins in the cryptocurrency market. Currently, there are 74 stablecoins according to the latest data, with a combined value of just under $165 billion. 

The most popular stablecoin, Tether, operates several fiat-stablecoin pairs, including USDT, EURT and GBPT. It alone is worth $78.2 billion, representing a 47.3% share of the total value of the stablecoin market. However, as has been demonstrated by the number of audits of Tether’s actual fiat holdings, tethered stablecoins have inherent centralization and subsequently and existential risk. 

Charge DeFi is an algorithmic crypto token with an innovative new “rebase” mechanism implemented to maintain stability. The mechanism adjusts the circulating supply of $STATIC using price-elastic tokens to increase the price when it is below a peg. This article will examine Charge DeFi’s solution to algorithmic stability, explore how it works, and compare it to the competition in the stablecoin space. It will then analyze the impact of algorithmic stability on the algorithmic space and the wider cryptocurrency market. 

But first, let’s take a look at Charge DeFi itself. 

What is Charge DeFi?

Charge DeFi is a combination of an algorithmic crypto token and rebase mechanics. A stablecoin is a cryptocurrency whose value is pegged to a single unit of a fiat currency, usually 1 USD. Normally, this is by means of “tethering” in which a company acquires an equivalent amount of say, USD and promises to back each unit of stablecoin 1:1. However, inherent in this mechanism is the requirement to trust the guarantor, which requires constant and often expensive monitoring.

An algorithmic crypto token takes stability to the next level. Instead of a fixed peg, an algorithm is used to adjust the price of a token based on pre-set conditions which can be written into a smart contract and launched in a fully decentralized way. Consequently, there is no input by any 3rd party thereafter, with the algorithm executing according to demand, supply and market movements.

Allowing for full, inexpensive independent monitoring as well as a lack of need to trust the word of a guarantor.

How Does Charge DeFi Ensure Stability?

There are two main tokens that feature in this rebasing mechanism, $CHARGE, and $STATIC. $CHARGE functions as the share/seigniorage token in the Charge DeFi ecosystem, and $Static as an elastic supply coin. 

One of the core features of this new ecosystem is the rebase mechanic implemented in the contracts. Rebase mechanics implement price-elastic tokens that adjust the circulating supply to influence a token price. Where other tokens feature rebase mechanics above and below a certain peg (target price), Charge DeFi has opted to only implement a mechanism that rebases below a certain peg. 

The rebase mechanism works as follows:

When the TWAP of $STATIC is below its $1.0 peg for 6 epochs (1 epoch is 8 hours), or when the TWAP of $STATIC drops below $0.8 the protocol rebases. An easier explanation for this would be that the protocol “compresses it’s tokens until the value is back at its $1.0 peg

During such a “compression” all tokens in circulation are compressed, including those in a user’s wallet and inside liquidity pools. Only unclaimed tokens in the project’s boardroom are exempt.

But perhaps an example would make this clearer:

You have 1k $STATIC tokens ($STATIC = $1.0) in your wallet, worth $1000 

The price drops 1 $STATIC = $0.98 for more than 6 epochs

Your wallet value is $980.0

The rebase starts and $STATIC tokens are compressed 

You now have 980 $STATIC tokens worth $1.0 in your wallet, worth $980

Algorithmic cryptocurrency rewards holders when the ecosystem expands through a seigniorage system. Investors who stake tokens into a boardroom receive part of the expansion. 

For an example of this mechanism working, please visit ChargeDeFi’s FAQs here.

The ideal range for $STATIC is roughly between $1.20 and $1.70. This will result in a reliable reward system for everyone. A much higher price will give more rewards, but may also attract arbitrage hunters who are looking to capitalize in the short term.

Users can invest in the liquidity pools that help to maintain the rebasing mechanism for $STATIC. Investors receive a reward, through an entity called the Boardroom every epoch (~8 hours). Charge DeFi also maintains a decentralized exchange (DEX) called Smart Swap which acts as a medium of exchange between $STATIC and $CHARGE.  

Moving the Algorithmic Cryptocurrency Space Forward

As explained above, Charge DeFi relinquishes the need for a central guarantor to be trusted to guarantee a 1:1 peg between fiat and a stablecoin. This is a positive step in the direction of decentralization given the scandals that have occurred with both Tether and USDCoin, the largest stablecoins in the cryptocurrency market. 

Tether, for example, was fined $41 million for misleading authorities and investors about the level of resources it kept honoring its guarantee. This follows years of assurances from the Tether team (who are also owned by the same holding company that owns the centralized exchange, Bitfinex) that the 1:1 guarantee between USDT and USD was fully resourced. 

DAI, an algorithmic stablecoin created by MakerDAO, was one of the first attempted solutions to this problem. It creates stability by collateralizing a basket of crypto assets and using an algorithm to execute market orders of those assets to maintain a peg. Whilst this approach is fully decentralized, there is a risk when it comes to getting data for the assets in its basket. This became clear in November 2023, when a bad oracle transmission led to the price of DAI surging momentarily by 30%. As a result, $88 million worth of liquidations were registered in DAI-based pairs on the DeFi protocol compound. 

Charge DeFi’s algorithmic rebasing solution is the answer to this. It is decentralized, transparent and functions without human or oracle intervention, removing the requirement to trust, but also reducing the potential margin for error. 

A New Dawn for Stablecoins?

Charge DeFi is pioneering a new mechanism for maintaining stability in an increasingly volatile cryptocurrency market. Not only does its rebasing mechanism outdo rival algorithmic cryptocurrency, but its riddance of the inherent flaws of tethered stablecoins allows for confidence to be restored in a market that is demanding more of its arbiters of stability. 

These innovative changes to the stablecoin spaces are being materialized in their roadmap for 2023. The Charge DeFi team is working towards launching two user beta platforms in Q1 of 2023 as well as several improvements and new features to the current platform. These include a voting system, a guided re-investment feature, and the boardroom compound.

Additionally, the team intends to secure a number of resource-sharing partnerships, which will also introduce more of the crypto-world to their innovative new cryptocurrency approach. Indeed, Charge DeFi could be on the precipice of introducing new dawn for the algorithmic cryptocurrency space, pioneering a new approach to stability and, as such, leading the space into a decentralized, transparent and algorithmic future.

For more about Charge DeFi and its unique fractional-algorithmic approach to stability visit their website here or follow the Twitter community here.

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Creating A Visual Tooltip In Power Bi

I’m going to show you my most favorite visualization idea around tooltip in Power BI. I saw it demonstrated really well in a Power BI challenge submission and I just want to show you how far you can take it. It’s not that difficult at all and it can add so much value to your report. You may watch the full video of this tutorial at the bottom of this blog.

This visualization technique using the Power BI tooltip will keep your consumers engaged and it enables you to compact more information into a singular report page. You don’t need to build comprehensive reports with ten pages or more. I’m going to show you how you can do that with this great tooltip idea.

Historically, this is what a tooltip has looked like, right? But what if you could customize this tooltip and create a visual tooltip? Wouldn’t that be awesome?

To do this, I first go to the paint roller, to the page size, and then I select tooltip.

This gives me this unique page that is optimized for tooltips. I’m going to grab a table visualization and put some information in it. I want to be able to hover over a customer on my report page, which will then show me some information about the customer.

In my table visual, I put my Date table as well as my Revenue and Orders. I then adjust the table accordingly to fit the page.

Next, I put a simple pie chart below the table. And then I’m going to grab the Channel from my Sales table because I want to see the channel in which a lot of these sales are happening. And I’ll also bring in my Revenue. Then, I just simplify this table and change the data labels into Category, percentage total.

I’m just going to rename this page as Details. You can rename it whatever you’d like. And, because this is a tooltip, I’m going to just hide it because we won’t need it in the application itself.

Lastly, I turn on the Tooltip in the Page information to make this work in the report page.

And now, when I hover over the visualization, I have this dynamic visual tooltip. I just love this idea. It’s so cool!

You can do a lot of other interesting things in the tooltip page that you’ve created. You can play around with the colors of the background, text or fonts, etc. It doesn’t need to be white. Just be creative and think about what could make the visualization pop.

This is just another amazing idea about how you can maximize the Power BI tooltip. I don’t think there’s any other analytical tool that would enable us to do this. It’s pretty epic how a consumer is able to see all this information so quickly and easily.

There’s so much variability in report development now inside Power BI compared to what it was even just a few years ago. Microsoft has done some amazing work!

This is a great visualization feature in Power BI. I recommend that you implement this technique in your own reports. You just need to think strategically about how your model is set up for this to work.

Cheers!

Sam

Creating A Culture Of Learning In Your Organisation

Creating A Culture Of Learning In Your Organization

“Building a culture of learning in your organization is a fundamental concept for raising the bar of achievement and skill levels across the whole firm. It just works!”.

In this course, I explore the ways that you can contribute to building a culture of learning in your organization. Not just helping people learn, or driving education and training, but how you can create a firm-wide, global culture that embeds a passion for self-learning and education into the structural fabric of your company. To make learning part of your DNA.

Part 1 – Introduction

What to expect from the course and why I’m qualified to teach this. I’ve been a people manager for 20 years at some of the biggest companies in the world. I’ve led teams large and small and am obsessed with making work a great place to be so that everyone can bring their best selves to work every day. Learning is a key pillar of a high-performing company and this guide will help you embed learning as a foundational pillar of your organization, your team, and yourself.

Part 2 – The Importance of Learning

What makes a culture of learning so important and why should you even bother learning? We’ll take a look at the benefits of being a true self-learner and what a systemic drive for education can do to an organization. We’ll also complain about how rubbish it used to be in the old days, and how lucky you guys are now to have all the cool resources available. Does anyone remember Encyclopedia Britannica? Thought not!

Part 3 – How Individuals Can Become True Self-Learners

In this section we explore what being a true self-learner actually involves, We’ll also dive into a number of resources and approaches that individuals can adopt to really supercharge their learning abilities. Techniques from speed listening, making the most of the dead time in your day, to developing the mental habits that can help you embed learning into your daily routine.

Part 4 – What Managers & Bosses Can Do To Facilitate Learning

In part 4 we take on the role of a manager or team leader. How can you help your teams maximize their learning capabilities and how can you drive a culture of learning to make your team the best around. We’ll look at how to structure work to give people the autonomy and freedom to learn new skills, as well as encourage internal mobility and personalized learning plans. We’ll even explore how you can make your team so good that they quit!

Part 5 – What Senior Leadership Must Do To Drive A Culture Of Learning

Senior leadership play a critical role in helping an entire firm embrace a learning mindset. As a senior leader we’ll use this section to explore how you can not only provide your initiatives with the resources and funding they require, but we’ll show how you can inspire and drive the uptake of your learning culture across the whole firm. Selling the message, demonstrating the return on investment and also driving vendor certification. It’s all here.

Part 6 – The Downsides Of A Learning Culture

As with any good thing, there are a couple of downsides. We’ll explore them here. Note, there aren’t many! Learning is overwhelmingly positive, but driving a change in culture does have some important caveats.

Part 7 – Conclusion & Recap

Wrap up time!

Who this course is for:

Individuals with a thirst for knowledge

Managers that want to embrace a learning mindset for their teams

Senior leaders that want to develop a culture of learning in their organizations

Goals

How to maximise your learning every day

What benefits learning gives to individuals, teams & organisations

How you can be a true self-learner

How managers can set up teams to embrace learning

The responsibilities of senior leadership for building a culture of continuous learning

How to create a culture of learning in your organisation

How to become known as a learning leader and broaden your learning network

Prerequisites

Have a desire to learn and want to develop more skills

Understanding Online Dating Apps And Playing With Their Algorithms

One of the highest ranged searches on the internet is ‘best dating apps for relationships’. To many people that can be sad but today, online dating has done away with the stigma around it.

Online dating apps & their algorithms

Studies show that 3 out of 5 people in the USA are not opposed to meeting someone online. But, how much do we know about this online dating matchmaking process?

Online dating websites all have their apps now, and even big sharks like OkCupid and Tinder all are powered by machine learning AI algorithms that match people around their geography.

So, the chances are if you have met your soul mate online, you have to thank algorithms rather than just Cupid, the God of love.

Even though Tinder, OkCupid, eHarmony have managed to keep the secret behind their matchmaking process a secret, researchers at Cornell University have cracked that can wide open.

These days most online dating apps use their AI algorithm to match new users on the following factors initially –

The agreeableness level

Closeness preference

Romantic passion range

Extroversion or Introversion level

Importance of spirituality

The level of optimism or happiness

In addition to these criteria, the algorithm then adds on the new user’s location, height, religion information to draw matches for users.

So, you can see that the algorithm polls in all this information and draws in matches that are closest to the new user’s preference. Hence, you can thank math for that lovely date you had last Saturday.

How do Dating Apps eliminate guesswork

Simply put, the dating app algorithm learns from user data. The AI is designed to not just gather user data but learn from your preferences and actions. That helps the AI to push the matched profiles closest to your preferences.

For example, if you don’t like people with piercings, then the algorithm will quit showing your profiles who have piercings.

This means that all the guesswork in the dating game is out of the window for you. You would not have met all these people had you tried dating offline. Dating app algorithms bring you closer to likeminded individuals and also learning your actions all the way.

Can you play with the Dating App Algorithm

For this, I have to go back to an episode from Parks and Recreation. Tom Haverford, a character in that comedy sit-com opened 26 different Tinder profiles. He did that to ensure his chances of meeting a woman of almost all preferences was higher.

So, yes tacking such preference alterations through multiple profiles can be a big issue for online dating apps like Tinder, Bumble, eHarmony, etc. There are some smart apps that detect an unused profile long enough then they also call for the deletion of it.

To add to it, most dating app companies use computer-human intuitions to detect misbehavior. For example, they will read through your activity and see if you have been opening multiple profiles. It can also detect whether a user is pretending to be in a foreign country. You can also get banned for explicit language and behavior.

Do not swipe right on every profile. Rather, keep it at a healthy 30% to 50%.

Work on your profile. Use a catchy caption — Link Instagram, etc.

Be genuine — no fake pictures or Photoshop.

Try replying to every message. This matters. So if you believe you would be inactive for a while, better deactivate the app temporarily.

Be yourself. Don’t try to spoof anything.

Remember, just like Facebook and Google, the algorithms of dating apps expect their users to use their applications genuinely. Don’t try shortcuts.

Are Online Dating Apps worth it?

Yes! Online dating boils down to strict math but, as true as that statement is; so is the fact that you get to meet different people. These dating app algorithms are designed to bring you closer to someone who shares your beliefs and your ideas.

Making the user in the centerfold of the whole math, you can ensure that you would at the very least end up meeting someone interesting.

EndNote

There is no doubt that the future of online dating is mobile-based for the industry. With more and more people getting on the online dating wagon, users are changing in how they use the internet to find their mate.

So definitely, knowing the algorithm and its preferential math system is going to help you refine your activities in a way, so you are matched with someone closer to your tastes.

Read next: Mistakes to avoid in Online Dating.

Data Structures & Algorithms

Data Structures & Algorithms – Overview

Data Structure is a systematic way to organize data in order to use it efficiently. Following terms are the foundation terms of a data structure.

Interface − Each data structure has an interface. Interface represents the set of operations that a data structure supports. An interface only provides the list of supported operations, type of parameters they can accept and return type of these operations.

Implementation − Implementation provides the internal representation of a data structure. Implementation also provides the definition of the algorithms used in the operations of the data structure.

Characteristics of a Data Structure

Correctness − Data structure implementation should implement its interface correctly.

Time Complexity − Running time or the execution time of operations of data structure must be as small as possible.

Space Complexity − Memory usage of a data structure operation should be as little as possible.

Need for Data Structure

As applications are getting complex and data rich, there are three common problems that applications face now-a-days.

Data Search − Consider an inventory of 1 million(106) items of a store. If the application is to search an item, it has to search an item in 1 million(106) items every time slowing down the search. As data grows, search will become slower.

Processor speed − Processor speed although being very high, falls limited if the data grows to billion records.

Multiple requests − As thousands of users can search data simultaneously on a web server, even the fast server fails while searching the data.

To solve the above-mentioned problems, data structures come to rescue. Data can be organized in a data structure in such a way that all items may not be required to be searched, and the required data can be searched almost instantly.

Execution Time Cases

There are three cases which are usually used to compare various data structure’s execution time in a relative manner.

Worst Case − This is the scenario where a particular data structure operation takes maximum time it can take. If an operation’s worst case time is ƒ(n) then this operation will not take more than ƒ(n) time where ƒ(n) represents function of n.

Average Case − This is the scenario depicting the average execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then m operations will take mƒ(n) time.

Best Case − This is the scenario depicting the least possible execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then the actual operation may take time as the random number which would be maximum as ƒ(n).

Basic Terminology

Data − Data are values or set of values.

Data Item − Data item refers to single unit of values.

Group Items − Data items that are divided into sub items are called as Group Items.

Elementary Items − Data items that cannot be divided are called as Elementary Items.

Attribute and Entity − An entity is that which contains certain attributes or properties, which may be assigned values.

Entity Set − Entities of similar attributes form an entity set.

Field − Field is a single elementary unit of information representing an attribute of an entity.

Record − Record is a collection of field values of a given entity.

File − File is a collection of records of the entities in a given entity set.

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Creating A Budget For Marketing A Mobile App

How much does it cost to market a mobile app?

As a starting point for creating a budget for an app you need to be clear on the benefits for business and consumer of investing in the app. Why does your business need a mobile app distinct from a mobile responsive website? These reasons include:

The need to offer a mobile-optimized experience for your users, whether: shoppers, readers, and social network communities. For example, Zappos offers its smartphone users to shop via a mobile app.

A desire to create a stand-alone mobile app business with a free-to-play, ad-based or subscription-based business model. Games, useful apps and news services fall into this category.

The necessity to establish the brand’s presence in a mobile domain. Games and useful tools help users continue to interact with the brand on mobile devices. For example, Lego and McDonalds offers free smartphone games for kids.

Gone are the days, when the millions of users came to your app through a sheer luck or by word of mouth. By now there are over a million apps in the App Store alone and the battle for the top positions is heating up. Most of the users discover their new apps by searching the app stores or browsing the top 10, top 25 or top 50 rating. So if your app appeals to the mass audience, you should be prepared to fight for the most lucrative top positions in the app stores. In special cases however, for example, when your app users are the existing clients of a regional bank or heart surgeons, a more targeted approach to marketing the app through digital even offline channels is more preferable.

How do you calculate mobile app marketing budget?

To calculate the budget you should first establish how many users you should have, or if your funds are limited, how many you can afford to acquire. Then you will need the cost per install to arrive to a total daily ad budget.

It is cheaper to get to the top of an app category in a smaller country. The most expensive is the top free apps in the App Store in large countries such as US, Germany or China.

How many installs should I plan for?

For example, to get your app to the top 10 free apps in the US App Store in the category Music, your app should generate 111,551 installations per day. You find this by looking at the number of daily installs of the top 10 apps in this category.

What is the cost per install or CPI?

This is probably the most difficult question, but the answer to it is $1,2-$1,6. The actual problem with evaluating a CPI is because the effectiveness of ad platforms differs greatly. Some platforms will sell you so-called incentivized traffic whereby the users download the app in exchange for free in-app currency or other virtual goodies. Those users are not interested in your app and are likely to delete it soon thereafter. Still, if you are not after the revenue but the number of installs to push your rating, even those installs will help.

How to get the most installs for your money?

So how do you get to the CPI of $1,2 or even $1,6? Here is what you need to do:

Manual work

Hire a small army of marketing analysts.  The objective of your analysts is to design an ad campaign for each of the ad platforms: be it Facebook, AdMob, Twitter or various traffic exchanges. They should then run campaigns and track what platforms users come from, how long they use the app for and how quickly they delete it from their device. Having ranked the sources they will provide you with a useful list of those platforms that send you real, engaged, interested users that are more likely to download your app than others.

Automated tools

Use an automated mobile app marketing service that will automatically analyze user actions and filter out the platforms that bring poor-quality results. Those are installs that do not result in further actions, low percentage of installs, high number of app removals. An automated service will experiment with a large number of traffic sources and select those that bring the best users at the lowest cost of installs. If you are a game developer with a large budget, go for AppLift. If your daily ad budget is below $20,000, test AppInTop.

This is your ad budget

Assume that you have optimized your cost per install to $1,2.

Then your daily ad budget will be: number of required daily installs multiplied by CPI equals 111’551 * $1,2 = $133’861.

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