Pair Programming — a pair that’s greater than the sum of its parts. You may have heard about pair programming and wondered whether it was worth trying in your workplace. On the surface it sounds simple, but two developers sitting together are not all that it takes to achieve productive pairing.
Logistical and personal hurdles such as scheduling, tool choices, and distractions can stop you from getting the most out of pairing. But the potential advantages can make it worth the trouble of recognizing and surmounting these challenges.
How could it be more productive to take two programmers who were previously working on separate projects and have them work together on a single project? Won’t everything take twice as long? To an outsider the idea of pairing may sound counterproductive at first, but the advantages become apparent when you start to think about why we code and what we’re trying to accomplish.
Programming is not about churning out the most lines of code in the shortest amount of time, or even delivering the most features within increasingly tight deadlines. You can have engineers working around the clock pushing new features into production, but how productive are they really if those features are cranked out by individuals working in isolation according to their own unique understanding of the overall architecture? The resulting code is likely to be riddled with technical debt such as hidden bugs, performance issues, idiosyncratic syntax, and inefficient designs that may not use resources efficiently and may make it that much more difficult and time consuming to modify the code when one of those flaws surfaces.
You need your code to be meaningful and well written so that it works together seamlessly and can be modified easily. You need it to encapsulate the desired functionality so that your end product behaves properly and performs as expected. You need it to be resilient so it can withstand organizational changes that are a natural part of working together, as well as environmental changes and new customer expectations that may make today’s workable solution obsolete without much warning.
In order to make that possible, developers need to be able to agree about fundamental requirements clearly, get up to speed quickly with whatever new or established technologies may be required, and focus without interruption to test out creative solutions and develop a product that’s worth putting in front of the customer.
These are the real-world challenges that pair programming helps to address. When two developers work together in a pair, the quality of the code they produce improves along with their shared understanding of how it works. This makes it easier for the next person who reads the code to pick it up and modify it when necessary, and it reduces the danger that the only person on the team who knows how part of the code works may win the lottery and leave the team, taking that precious knowledge with them.
The time cost in mythical work hours is nowhere near the 50% that may seem intuitive if you tried to to equate the intricate art of coding with repetitive assembly line work. Some empirical studies have concluded that pair programming might result in about a 15% increase in the time it takes two programmers to accomplish the same tasks had they been working alone, but the resulting code will also be of much higher quality, with about 15% fewer observable defects to fix. Combine this with the shared ownership, deeper engagement, and faster problem solving that comes from having more than one mind engaged in solving a problem, and it’s clear why pair programming is a popular approach.
What Exactly is Pairing?
So what does it take for two developers working together to achieve the productivity and quality improvements that come from pairing? It’s mostly a matter of learning how to work collaboratively, which is not necessarily the way most of us learned to code.
By definition, pair programming doesn’t start until you have two people working together on one computer. But how does that work in practice?
Two People …
The fundamental element of pair programming is working together with your pair. When a task is accepted, it needs to be shared between both of the people working on it, and they both need to be fully engaged in the task while they’re pairing on it. That means that they both need to understand the requirements the same way, and work together to come to a shared understanding of how they want to go about meeting them.
Pairing helps people get better at verbalizing their ideas and expectations. The implicit understanding you have in your head when you’re working alone needs to be communicated so both you and your pair know you’re on the same page. Getting as explicit as possible about the work and the approach up front will help make the pairing experience much more agreeable. Pairing involves a lot of talking, as that’s the best way to keep two minds actively engaged in the problem at the same time.
For this reason, pairing is often associated with agile story writing, in which requirements for a feature are defined in consistent, plain language that can be understood equally well by Product and Engineering people with little room for ambiguity. Often pairs will ask for stories to be spelled out in Gherkin, which is a way of using common, non-technical phrases that are easy to translate into automated tests, so the pair can verify and demonstrate that each feature works as expected.
Writing in Gherkin means taking a feature and breaking it down into a simple story about a customer who wants something that this feature will deliver:
As <a customer of the product> I want <something desirable> So that <I can achieve a specific goal>
Then all the acceptance criteria are written out in a consistent syntax, defining the anticipated permutations and scenarios associated with that story:
Given <a customer in a particular state> When <something specific happens> Then <there is a specific outcome> Given <a customer in a particular state> When <something different happens> Then <there is a different specific outcome> etc.
Of course, it’s not mandatory to use this exact phrasing, but if the requirements of a feature can’t be expressed in this minimalist way, it’s possible that the expectations are ambiguous. That’s a potential red flag that’s easier for a pair of programmers to spot when they start to discuss what’s needed.
As soon as a pair accepts a story to work on, they should be able to define how they will know they are done and how they’re going to prove it. From there, they can start to figure out together how best to approach the job.
In fact, the pair working on a feature should know enough up front that they could start by writing an automated test based on the first acceptance criterion before writing any code, making sure the new test fails, and then writing just enough code to make that test pass before refactoring and then starting on the next acceptance criterion. This approach is known as behavior-driven development, and while it’s not part of the definition of pair programming, it harmonizes beautifully, along with test-driven development.
Working Together …
When you have two or more people trying to work together, the first thing they need to do is agree on a work schedule. It’s not really pairing if two developers aren’t working together at the same time. Because of this, it’s essential that the developers who plan to work together coordinate their schedules and make sure they both agree to a time and place where they will work.
One mistake I’ve seen pairs make is trying to maximize the time they work together as a pair by scheduling a full eight hours together, and sometimes trying to work together beyond that. Pairing is intensive work that requires a heightened level of focus and participation. It’s very taxing to try to pair for more than five or six hours in a day, and even that might be stretching it for even the most resilient of us. When setting up a pairing schedule, try to agree on a fixed and limited time that will fit within a typical eight-hour work day, leaving time for lunch, email, personal tasks, etc. Those personal tasks are essential to our work day, but they’re also distractions that shouldn’t be attempted during a pairing session.
It can also be socially awkward to remind your pair when the agreed pairing time has come to an end. For that reason, it can be a good idea to set an alarm that will break the news to both of you without putting the burden on one or the other.
Skill level is another area where pairs can have trouble. It’s a fair assumption that, no matter what you’re working on, the person you’re working with has a different background, experience, and comfort with the topic. Recognizing that up front is important, so neither of you will feel the need to try to hide that fact. One of the benefits of pairing is that working together naturally brings up the skills of anyone learning something new, whether that something is a programming language or a communication style.
Be gracious if you feel you’re more skilled than your pair. Treat them the way you’d want to be treated as you learned something new. And remember that the point is not only to get the work done, but to make sure that both of you have sufficient knowledge and ownership of the end result.
To that end, it’s vital that each programmer have the opportunity to sit at the keyboard and drive while the other observes and navigates through the code. The concept of taking turns may be new to anyone whose exclusive experience as a programmer has been solo work, so there will be a learning curve as you adapt to verbalizing your intentions when navigating, or carrying out someone else’s ideas when driving.
A programmer new to pairing but comfortable with the task at hand can easily get into a pattern of holding onto the driver role for as long as possible. Similarly, if you’re not driving at the keyboard and you’re not all that familiar with the code, it’s easy to find your mind wandering back to your phone, your email, and your other tasks. When that happens, you end up with one person coding alone and the other person sitting in the same room scrolling through social media.
One of the clues that a pair might be having trouble taking turns is silence. Pairing is a noisy process, involving a lot of questions, feedback, discussion, and collaboration. When a pair finds themselves going for more than a minute or two without saying a word, it’s likely the pairing has stopped.
One useful technique that can keep pairs from falling into this antipattern is to use a Pomodoro timer. These timers will keep a running countdown of the seconds as you work in 25-minute increments, and then tell you to take a break for five minutes. By switching roles between the driver and the navigator during these breaks, a pair can avoid lapsing into extended sessions with just one driver.
On One Computer …
Pairing only works when two people dedicate their full attention to a single computer. It’s better to avoid the distraction of having two (or more) active screens going during a pairing session. Even if one person just wants to look up some relevant code examples or check on the status of a background process, it’s better to do that on the one shared computer. If it’s a search, both developers can see how the search is constructed and what potential results come up. If it’s a status update, neither developer needs to be left behind.
So in any pair, both developers must be able to see the screen they are working on together clearly. One of the essential tools for pairing is a monitor big enough that both developers can see what’s being written clearly. Depending on the circumstances, this can be accomplished with a shared laptop if you don’t mind huddling together and you use a large enough font with adequate contrast. A better solution is a larger desktop or wall-mounted monitor where the code can be displayed and viewed together more comfortably.
Remote pairing is a very viable option these days. There are a number of great solutions ranging from Slack or Zoom tools to full-blown integrated development environments and text processors that support screen sharing, conference calling, or both, allowing two people on opposite sides of the desk or opposite sides of the world to work together. Even if you’re at the next seat in an open office, screen sharing can make it easier for both of you to see and interact with the code on the screen more comfortably. (I just encourage you to stay focused and resist the urge to bring up your email in a separate window while you’re pairing remotely. It’s disrespectful, it will be obvious to the person you’re pairing with, and your distraction will make you miss out on opportunities to learn together and produce quality results.)
If a colocated pair is sharing a single machine, they’ll need to come to some agreements about the configuration of their shared computers, keyboards, mouses, etc. That ergonomic keyboard with all the custom hardware macros might not be the best choice for sharing. But if you don’t mind using the same laptop or keyboard and mouse, swapping roles from navigator to driver in a pair might be as simple as switching places.
A popular and very robust solution is to make active use of a version control system like Git. Commit your code frequently to a shared repository so each developer can pull the latest version and work on their own device when they switch roles. Using version control to handle swapping in pairs has the added advantage of creating a more detailed history of code changes for future logging and potential rollbacks. If the Git logs get too cluttered, it’s always possible to go back and squash those extra commits into a single, more meaningful one before doing a pull request.
At a higher level, once you start pairing with another developer, you’re going to notice some differences in the ways you each approach your tasks. One of you might know some fancy keyboard shortcuts, have some special aliases for common shell commands, or prefer to use a specific IDE because of its unique features. In terms of the code itself, you may also each have a different implicit understanding about how variables are named, how to structure a commit message, when to use comments, etc.
Pairing is an opportunity to make these unconscious variations in technique visible so everyone can benefit from the hidden wealth of experience and knowledge about how we code more effectively.
How to Start Pairing
There’s often a period of adjustment while building the muscle memory and learning to express ideas out loud that were once just thoughts in the back of your head. It’s also necessary to establish workable logistics to allow two people to work together, which might mean making adjustments in terms of schedules, locations, and equipment. And when you’ve got that working, you might try expanding the process to the whole team with enhancements such as mob programing for groups, or promiscuous pairing to give everyone on the team a chance to pair on all of the team’s stories.
Putting in the effort to get through the learning stages usually pays off in significant improvements, both for the quality of the code and for the happiness and sustainability of the people who create it. And if your entire team or organization adopts pairing, the learning curve will become even easier for new people, improving the onboarding process and helping everyone be more productive.
How To Manage A Technical Debt Properly
Co-founder & CEO at stepsize.com, SaaS to measure & manage technical debt
We’re used to thinking that you cannot deliver fast and maintain a healthy codebase. But does it really has to be a trade-off?
One of my greatest privileges building Stepsize has been hearing from hundreds of the best engineering teams in the world about how they ship software at pace while maintaining a healthy codebase.
That’s right, these teams go faster because they manage technical debt properly. We’re so used to the quality vs. cost trade-off that this statement sounds like a lie—you can’t both be fast and maintain a healthy codebase.
Martin Fowler does a great job at debunking this idea in his piece ‘Is high quality software worth the cost?‘. Spoiler:
High quality software is actually cheaper to produce.
The lessons I’ll relay in this article are drawn from the combined centuries of experience of the these 300+ software engineers I’ve interviewed.
As Adam Tornhill and I recently discussed in our webinar, software has well and truly eaten the world. And look, if you’re here, this will probably sound like a cliché to you. In this case, it’s because it’s true. Look around you, can you name one object that didn’t need some form of software intervention to be manufactured, purchased, or delivered to you?
Software companies live and die by the quality of their software, and the speed at which they deliver it.
Stripe found that ‘engineers spend 33% of their time dealing with technical debt’. Gartner found that companies who manage technical debt ship 50% than those who don’t. These data points may seem a little dry, but we intuitively know they’re true. How many times have we estimated a feature will be delivered in a sprint, only for it to take two? Now take a moment to extrapolate and think about the impact this will have on your company over a year, two, or its entire lifespan.
Is it not clear that companies who manage technical debt properly simply win?
A simple framework to achieve these results
Google around for ‘types of technical debt’, and you’ll find hordes of articles by authors geeking out about code debt, design debt, architecture debt, process debt, infrastructure debt—this debt that debt.
These articles are helpful in that they can train you to recognise technical debt when you come across it, but they won’t help you decide how to deal with each piece of debt, let alone how to manage tech debt as a company.
The only thing that matters is whether you’re dealing with a small, medium, or large piece of debt.
The process for small pieces of debt
This is the type of tech debt that can be handled as soon as the engineer spots it in the code—a quick refactoring or variable rename. Engineers don’t need anyone’s approval to do this, or to create a ticket for it to be prioritised. It is simply part of their jobs to apply the boyscout rule coined by Uncle Bob:
Always leave the code better than you found it.
This is table stakes at every software company who have their tech debt under control that I’ve interviewed. It’s mostly driven by Engineering culture, gets enforced in PRs or with linters, and it is understood that it is every individual contributor’s responsibility to handle small pieces of debt when they come across them.
The process for medium-sized debt
The top performers I’ve interviewed stress the importance of addressing technical debt continuously as opposed to tackling it in big projects.
Paying off technical debt is a process, not a project.
You do not want to end up in a situation where you need to stop all feature development to rewrite your entire application every three to five years.
This is why these teams dedicate 10-30% of every sprint to maintenance work that tackles technical debt. I call the tech debt that is surfaced and addressed as part of this process medium-sized debt.
To determine what proportion of your sprint to allocate to tech debt, simply find the overlap between the parts of your codebase you’ll modify with your feature work, and the parts of your codebase where your worse tech debt lives. You can then scope out the tech debt work and allocate resources accordingly. Some teams even increase the scope of their feature work to include the relevant tech debt clean up. More in this article ‘How to stop wasting time on tech debt‘.
For this to work, individual contributors need to track medium-sized debt whenever they come across it. It is then the Team Lead’s responsibility to prioritise this list of tech debt, and to discuss it with the Product Manager prior to sprint planning so that engineering resources can be allocate effectively.
The process for large pieces of debt
Every once in a while, your team will realise that some of the medium-sized debt they came across is actually due to a much larger piece of debt. For example, they may realise that the reason the frontend code is under-performing is because they should be using a different framework for the job.
Left unattended, these large pieces of debt can cause huge problems, and—like all tech debt—get much worse as time goes by.
The best companies I’ve interviewed have monthly or quarterly technical planning sessions in which all engineering and product leaders participate. Depending on the size of the company, Staff Engineer, Principal Engineers, and/or Engineering Managers are responsible for putting together technical proposals outlining the problem, solution, and business case for each of these large pieces of debt. These then get reviewed by engineering and product leadership and the ones that get prioritised are added to the roadmap.
How to achieve this easier
In order to be able to run this process, you need to have visibility into your tech debt. A lot of companies I’ve spoken to try to achieve this by creating a tech debt backlog in their project management tool or in a spreadsheet.
It’s a great way to start, but here’s the problem: these issues will not contain the context necessary for you to prioritise them effectively. Not only do you need to rank each tech debt issue against all others, you also need to convincingly argue that fixing this tech debt is more important than using these same engineering resources towards shipping a new feature instead.
Here’s the vicious cycle that ensues: the team tracks debt, you can’t prioritise it, so you can’t fix it, the backlog grows, it’s even harder to prioritise and make sense of it, you’re still not effectively tackling your debt, so the team stops tracking it. You no longer have visibility into your debt, still can’t prioritise it, and it was all for nothing.
We built Stepsize to solve this exact problem. With our product, engineers can track debt directly from their workflow (code editor, pull request, Slack, and more) so that you can have visibility into your debt. Stepsize automatically picks up important context like the code the debt relates to, and engineers get to quantify the impact the debt is having on the business and the risks it presents (e.g. time lost, customer risk, and more) so that you can prioritise it easily.
You can join the best software companies by adopting this process, start here.
Previously published at https://www.stepsize.com/blog/how-to-maintain-a-healthy-codebase-while-shipping-fast
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What is key to stirring a Litecoin comeback on the charts?
[PRESS RELEASE – Please Read Disclaimer]
Zug, Switzerland, 9th March, 2021, // ChainWire // Privacy-centric blockchain Concordium has finalized its MVP testnet and concluded a private sale of tokens to fund further development. The company secured $15M in additional funding for the Public and permissionless compliance-ready privacy-centric blockchain.
Late February Concordium announced joint venture cooperation between Concordium and Geely Group, a Fortune 500 company and automotive technology firm. The partnership will focus on building blockchain-based services on Concordium’s enterprise-focused chain.
Concordium recently completed Testnet 4, which saw over 2,300 self-sovereign identities issued and over 7,000 accounts created, with more than 1,000 active nodes, 800 bakers, and over 3,600 wallet downloads. The successful testnet led to the release of Concordium smart contracts functionality based on RustLang, with a select group of community members participating in stress-testing the network. Test deployments for smart contracts included gaming, crowdfunding, time-stamping, and voting.
Concordium CEO Lone Fonss Schroder said: “The interest of the community, from RustLang developers, VCs, system integrators, family offices, crypto service providers, and private persons, has been amazing. Concordium has fielded strong demand from DeFi projects looking to build on a blockchain with ID at the protocol level.”
Concordium will bring its blockchain technology for broad use, which also appeals to enterprises with protocol-level ID protected by zero-knowledge proofs and stable transaction costs to support predictable, fast, and secure transactions. Its core scientific team is made up of renowned researchers Dr. Torben Pedersen, creator of the Pedersen commitment, and Prof. Ivan Damgård, father of the Merkel-Damgård Construct.
Concordium, which is on course for a mainnet launch in Q2, aims to solve the long-standing blockchain-for-enterprise problem by addressing it in a novel way with a unique software stack based on peer-reviewed and demonstrated advanced identity and privacy technologies providing speed, security and counterpart transparency.
The Concordium team intends to announce its post-mainnet roadmap in the coming days.
Concordium is a next-generation, broad-focused, decentralized blockchain and the first to introduce built-in ID at the protocol level. Concordium’s core features solve the shortcomings of classic blockchains by allowing identity management at the protocol level and zero-knowledge proofs, which are used to replace anonymity with perfect privacy. The technology supports encrypted payments with software that upholds future regulatory compliance demands for transactions made on the blockchain. Concordium employs a team of dedicated cryptographers and business experts to further its vision. Protocols are science-proofed by peer reviews and developed in cooperation with Concordium Blockchain Research Center Aarhus, Aarhus University, and other global leading universities, such as ETH Zürich, a world-leading computer science university, and the Indian Institute of Science.
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Bowling in VR!
The bowling ball: By pressing the trigger on the controller, the user can pick up, hold, and release the ball. The weight and speed of the ball mimics the movement that a regular bowling ball would have. After it is thrown, the ball will respawn in the starting position by hitting the backstop, or through a manual reset from the user pressing the “reset ball” button.
The pins: Once hit by bowling ball, the pins will fall down after the collision. Once all of the pins are hit, they will respawn to reset the game. The pins can also be manually be reset through the button as seen in the image above on the right hand side. The design of the both the ball and pins were already pre-created in the asset store which are available for free.
The asset store is your friend!
The lane: With a wood flooring, the lane has two walls on either side with a backstop, simulating bumpers that would be regularly seen bowling.
Here’s a look into how the game actually works!
These are the components added on the bowling ball for functionality in collision and interactivity. The rigid body and mesh collider are also applied to the bowling pins, with the only change in mass (the pins are 1, while the ball is 3), and the OVR Grabbable script.
Rigid body: This makes sure the laws of physics and gravity are applied upon the game objects, and lets you apply forces and control it in a realistic way.
Mesh Collider: The checkmark on “Convex” indicates that this mesh collider object will collide with other mesh collider objects so that they don’t fall through the floor!
OVR Grabbable: From the free oculus integration in the asset store, the ovr grabbable script was already pre-made, allowing user interactiveness.
Pin and Ball Reset:
To reset both the pins and the ball, the user can click on these floating buttons in order to do so. I followed this tutorial (step 4 & 5) to add the buttons, but the most important step is integrating the Tilia package along with all the prefabs (fully configured game objects that have already been created for your use).
Installing the Tilia package into Unity: navigate to the manifest.json file in finder (go to the actual folder in your computer). After opening it up, there will be a section that says “dependencies”. At the bottom of this section, add in “io.extendreality.tilia.input.unityinputmanager”: “1.3.16”. The code below is a shortened version of what the dependencies would look like, and all the tilia extensions I added to complete this project.
//Above are a few of the dependencies already installed, while the tilia extensions below were manually added"io.extendreality.tilia.input.unityinputmanager": "1.3.16",
For the code behind the reset, here is a look into the C# script for the pins:
The Awake() call is used to load certain set up necessary prior to the game scene.
The SavePositions() method is called, where all the starting positions of the pins are logged in an array.
The ResetPositions() method contains a for loop, and goes through each of the pins to set the position and rotation to the original value saved previously. The velocity is also flattened on the pins, in the case that it spun out after being knocked over.
Once again, the asset store comes in really handy! The free oculus integration is compiled of pre-made scripts and functions, such as the OVR Player Controller that includes the camera rig for the oculus and the controller visibility. To properly set it up with controller integration, I followed this tutorial that is also mentioned underneath the resources below. I had to turn on developer mode on the oculus app and connect my computer to the headset with the USB-C charging cable.
Some awesome resources that helped me out:
India’s Crypto Ban Uncertain as Finance Minister Touts a Window for Experiments
Aave is a decentralized, open-source, non-custodial liquidity protocol that enables users to earn interest on cryptocurrency deposits, as well as borrow assets through smart contracts.
Aave is interesting (pardon the pun) because interest compounds immediately, rather than monthly or yearly. Returns are reflected by an increase in the number of AAVE tokens held by the lending party.
Apart from helping to generate earnings, the protocol also offers flash loans. These are trustless, uncollateralized loans where borrowing and repayment occur in the same transaction.
The following article explores Aave’s history, services, tokenomics, security, how the protocol works, and what users should be wary of when using the Aave platform.
How Does Aave Work?
The Aave protocol mints ERC-20 compliant tokens in a 1:1 ratio to the assets supplied by lenders. These tokens are known as aTokens and are interest-bearing in nature. These tokens are minted upon deposit and burned when redeemed.
These aTokens, such as aDai, are pegged at a ratio of 1:1 to the value of the underlying asset – that is Dai in the case of aDai.
The lending-borrowing mechanism of the Aave lending pool dictates that lenders will send their tokens to an Ethereum blockchain smart contract in exchange for these aTokens — assets that can be redeemed for the deposited token plus interest.
Borrowers withdraw funds from the Aave liquidity pool by depositing the required collateral and, also, receive interest-bearing aTokens to represent the equivalent amount of the underlying asset.
Each liquidity pool, the liquidity market in the protocol where lenders deposit and borrowers withdraw from, has a predetermined loan-to-value ratio that determines how much the borrower can withdraw relative to their collateral. If the borrower’s position goes below the threshold LTV level, they face the risk of liquidation of their assets.
Humble Beginnings as ETHLend
Aave was founded in May 2017 by Stani Kulechov as a decentralized peer-to-peer lending platform under the name ETHLend to create a transparent and open infrastructure for decentralized finance. ETHLend raised 16.5 million US dollars in its Initial Coin Offering (ICO) on November 25, 2017.
Kulechov, currently serving also as the CEO of Aave, has successfully led the company into the list of top 50 blockchain projects published by PWC. Aave is headquartered in London and backed by credible investors, such as Three Arrows Capital, Framework Ventures, ParaFi Capital, and DTC Capital.
ETHLend widened its bouquet of offerings and rebranded to Aave by September 2018. The Aave protocol was formally launched in January 2020, switching to the liquidity pool model from a Microstaking model.
To add context to this evolution from a Microstaking model to a Liquidity Pool model, Microstaking was where everyone using the ETHLend platform. Whether one is applying for a loan, funding a loan, or creating a loan offer, they had to purchase a ticket to obtain the rights to use the application, and that ticket had to be paid in the platform’s native token LEND. The ticket was previously a small amount pegged to USD, and the total number of LEND needed varied based on the token’s value.
In the liquidity pool model, Lenders deposit funds to liquidity pools. Thus creating what’s known as a liquidity market, and borrowers can withdraw funds from the liquidity pools by providing collateral. In case the borrowers become undercollateralized, they face liquidation.
Aave is typically pronounced “ah-veh.”
Aave’s Products and Services
The Aave protocol is designed to help people lend and borrow cryptocurrency assets. Operating under a liquidity pool model, Aave allows lenders to deposit their digital assets into liquidity pools to a smart contract on the Ethereum blockchain. In exchange, they receive aTokens — assets that can be redeemed for the deposited token plus interest.
Borrowers can take out a loan by putting their cryptocurrency as collateral. The liquidity protocol of Aave, as per the latest available numbers, is more than 4.73 billion US dollars strong.
Aave’s Flash loans are a type of uncollateralized loan option, which is a unique feature even for the DeFi space. The Flash Loan product is primarily utilized by speculators seeking to take advantage of quick arbitrage opportunities.
Borrowers can instantly borrow cryptocurrency for a matter of seconds; they must return the borrowed amount to the pool within one transaction block. If they fail to return the borrowed amount within the same transaction block, the entire transaction reverses and undo all actions executed until that point.
Flash loans encourage a wide range of investment strategies that typically aren’t possible in such a short window of time. If used properly, a user could profit through arbitrage, collateral swapping, or self-liquidation.
Aave allows borrowers to switch between fixed and floating rates, which is a fairly unique feature in DeFi. Interest rates in any DeFi lending and borrowing protocol are usually volatile, and this feature offers an alternative by providing an avenue of fixed stability.
For example, if you’re borrowing money on Aave and expect interest rates to rise, you can switch your loan to a fixed rate to lock in your borrowing costs for the future. In contrast, if you expect rates to decrease, you can go back to floating to reduce your borrowing costs.
Aave Bug Bounty Campaign
Aave offers a bug bounty for cryptocurrency-savvy users. By submitting a bug to the Aave protocol, you can earn a reward of up to $250,000.
The maximum supply of the AAVE token is 16 million, and the current circulating supply is a little above 12.4 million AAVE tokens.
Initially, AAVE had 1.3 billion tokens in circulation. But in a July 2020 token swap, the protocol swapped the existing tokens for newly minted AAVE coins at a 1:100 ratio, resulting in the current 16 million supply. Three million of these tokens were kept in reserve allocated to the development fund for the core team.
Aave’s price has been fairly volatile, with an all-time high of $559.12 on February 10, 2021. The lowest price was $25.97 on November 5th, 2020.
Aave stores funds on a non-custodial smart contract on the Ethereum blockchain. As a non-custodial project, users maintain full control of their wallets.
Aave governance token holders can stake their tokens in the safety module, which acts as a sort of decentralized insurance fund designed to ensure the protocol against any shortfall events such as contract exploits. In the module, the stakers can risk up to 30% of the funds they lock in the module and earn a fixed yield of 4.66%.
The safety module has garnered $375 million in deposits, which is arguably the largest decentralized insurance fund of its kind.
Final Thoughts: Why is Aave Important?
Aave is a DeFi protocol built on strong fundamentals and has forced other competitors in the DeFi space to bolster their value propositions to stay competitive. Features such as Flash loans and Rate switching offer a distinct utility to many of its users.
Aave emerged as one of the fastest-growing projects in the Summer 2020 DeFi craze. At the beginning of July 2020, the total value locked in the protocol was just above $115 million US dollars. In less than a year, on February 13, 2021, the protocol crossed the mark of 6 billion US dollars. The project currently allows borrowing and lending in 20 cryptocurrencies.
Aave is important because it shows how ripe the DeFi space is for disruption with new innovative features and how much room there is to grow.
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