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Are QR Codes Failing ?

Posted on 13/05/202113/05/2021 by Anteelo Master

QR code, also known as flash code, is a two-dimensional barcode that records information related to an item. It can be read by a QR scanner or a mobile device with a camera. QR codes can have many usages. Scanning a barcode could lead to the downloading of an application, a link to more information about about a business, or even personal usage like the uploading of one’s resume.

QR Codes are everywhere, on articles, buses, websites, billboards… It is a true form of advertising. It can be playful, luxurious, or even more dangerous. But it has not yet won its credibility in the eyes of the public. Millions of people are spending their time tweeting, pinning, checking but why not flashing?

An American blogger, Sean x Cummins surveyed 300 people on the streets of San Francisco, showing them the photo of the small square, and asking them if they knew what it was? Only 11% answered “QR code”. The others suggested anything and everything, from the korean military code to the aerial view of San Francisco

The results were similar when surveying New Yorkers. The Brooklyn Museum put a code on the back of its entrance tags that served as an introduction to what visitors might find behind QR codes throughout the building. Every visitor coming in the door gets one of these tags, but only 1.77% of visitors responded by scanning the code.

Although this mobile technology seems great and useful in providing additional content in numerous media channels, its popularity is lacking.

Here is why: We may be in 2012, but a large large number of people do not still know what QR codes represent. The reason for this lack of knowledge is the little to no communication on the technology itself. Previous launches of barcode scanners proved unsuccessful. QR codes must regild to show their great potential.

The problem remains that QR codes do not really bring interest to get flashed. The proposed contents have to bring a real added value to the product or to the communication, send back towards a simple web page without interest and not optimized for mobile bring nothing. And why not directly integrate the reader of flash codes in the camera, as a native app? Now, you must download a third application on the market of your telephone, an application which will not be integrated into your camera.

QR codes are obviously a powerful tool of communication on which it will be necessary to count in the next years. For marketers, QR codes provide a relatively inexpensive way of reaching out to consumers while still maintaining complete control of the resulting interaction. With creativity, the possibilities are endless. It only needs a big advertising campaign to ground the movement. Place your bets now.

International Dating? Choose the ‘Flutter’ app

Posted on 13/05/2021 by Anteelo Master

By only working one day a week, Flutter aims to create a meaningful environment where users form genuine connections and feel empowered to move the relationship forward, and off of the app.

Tired of ghosting and drawn-out conversations? Dating in 2020 can feel like a minefield, especially with all the different dating apps out there. Yet, even when you find an app that you somewhat enjoy, the ability to create a real, authentic relationship can be extremely difficult. Flutter, a new dating app, is trying to change that. Instead of swiping left or right, having a mindless chat, then leaving the app, Flutter is facilitating relationships by only allowing app action on Sundays. This includes viewing, matching, and chatting with other users, with the exception of allowing you to edit your profile on other days of the week.

In late 2019, Clay Jones and Teddy Jungreis founded Flutter, on a mission to create an app that would foster genuine relationships and move away from the more common dating app trend of swiping and never actually connecting. According to Biz Journals, Jungreis explained that “Flutter forces people to exchange information quickly so they can continue to build the relationship off of the app.

While apps like Tinder and Hinge, leave people with “ghosting and mismatches and conversations that fizzle out,” Flutter is for users who want to create a serious connection.

In a unique approach to dating, Flutter only allows users to access the app features on Sundays. It all begins on Sunday morning when you are added to the waitlist. You then have until the afternoon to claim a spot and be added to the dating pool. The user then has access to view, like, and comment on profiles, and a few hours later the matches are released. This gives users a small window of time to make a move before all the information disappears forever. The app is currently offered to users in San Francisco, but they plan on expanding to New York, Los Angeles, and other cities soon.

Flutter is free and has a 4.3/5 star rating in the app store with 91 ratings in total. When Flutter launched to Product Hunt in early March, they received over 900 upvotes, an impressive number that they exhibit on their front page.

Agile Development: Build Your App

Posted on 13/05/2021 by Anteelo Master

AGILE METHODOLOGY

You finally have it. After months, maybe even years, of failing to envision an app that will resonate with users, you’ve come up with The Really Good Idea: An app that fills a niche no one else has thought of, an app that fills a need users didn’t even know they had.

So, you plan. You decide exactly what you’re going to build, you get funding, you set goals, you track every step, you work an utterly absurd amount of hours, you arrive at your finished product, you release it to the world, and… no one is impressed. Your idea was not the hit you expected; users barely notice it.

Predicting what customers will want is not easy. No matter how skilled your team is, if your Really Good Idea isn’t actually as useful or appealing as you imagined, at the end of it all you’ll have merely wasted time, money, and energy.

Agile development is essentially an umbrella term for various methodologies such as Scrum and extreme programming (XP), and represents a way of avoiding this fate. The idea came about in 2001 when 17 software developers met up at Utah’s Snowbird resort and shared their ideas with one another about making the software development process more efficient and simple. The result was the Manifesto for Agile Software Development, which outlined a set of principles used to build an app that actually gives users what they want.

WHAT AGILE DEVELOPMENT IS

The emergence of agile development followed a period in the 1990s when teams were striving to create lightweight software development methods. Prior to this, many software development teams followed the “waterfall” model. This process involved following a strict set of steps to arrive at a finished product: Once the product idea was in place, teams would first analyze the software and system requirements necessary to realize the concept. Following that, they would design the software architecture, code, test for bugs and defects, and eventually introduce the finished product.

It’s a process that requires a fair amount of planning and provides little room for flexibility. The problem for app developers, though, is that all that strict regulation, combined with the sequential nature of the waterfall method, means that there’s no way of knowing whether or not users will actually be interested in a product until it is complete.

Agile development, by focusing on incremental goals, rather than just the major end goal, gives developers the opportunity to respond quickly to user reactions and modify plans to build an app that will succeed. By embracing certain key principles, including frequent delivery of software and an openness to change at any step of the process, developers can avoid gambling all of their resources on the hope that the app they are working to build will appeal to users.

THE AGILE DEVELOPMENT PROCESS

Again, it’s important to keep in mind that agile development encompasses many different methodologies. No business needs to use all of them, nor could any one business possibly use all of them. As such, the process varies depending on the project.

That said, there are basic aspects of agile development that are generally applicable. One of these is the idea of “iterations.”

Instead of spending months working to create a complete product, which often involves a tremendous amount of planning and risk, agile app development encourages teams to spend one to four weeks developing basic iterations of an app. At the end of each cycle, the latest iteration is demonstrated. Although these individual iterations are rarely strong enough to bring to market, they allow a business to introduce new features, identify potential problems, and adapt to changing needs more quickly.

While working on an iteration, a common part of the agile development process is a daily meeting in which all team members explain what they did the previous day to reach the goal of that iteration, and bring up any roadblocks they anticipate. However, these daily meetings are usually not meant to be problem-solving conferences. If any problem-solving does occur, it only involves select members of the team, so that other team members can continue working on their piece of the project. In this way, time management and efficiency is preserved during the process.

Although milestones are a key part of agile development, they can be changed if necessary.

AGILE DEVELOPMENT ROLES

There is no one set of roles that all agile development teams adopt. Different methods encourage different roles. However, there are certain team members who are typically involved.

Usually, the development team consists of 10 or fewer people equipped with the skills necessary to plan, develop, and test a new iteration of an app. These teams are usually self-motivated, and although there is rarely a specific team “leader,” there is often one member whose role involves addressing any issues that would distract a team from its task.

Another key player often involved in agile development is a product manager who represents the interests of the customers and stakeholders. Because the development team should be focused on reaching the goals of a particular iteration, someone needs to be available who can make sure that the work being done will actually result in something of value. One of the major principles of agile development is the idea that business owners and developers should be in constant communication, and this person facilitates that.

BENEFITS OF AGILE APP DEVELOPMENT

Building a successful app almost always involves being able to adapt. Your Really Good Idea might have only been the seed for something that customers actually want. As talented as you might be, predicting what will or will not resonate with users is no easy task. Yet, some approaches to software development rely on a team’s ability to make that kind of accurate prediction.

Agile development helps to limit risk by letting teams build apps in increments. Users can test new iterations and provide feedback; additional features can be launched to see if they add to the product; teams can spot potential problems early. They can also identify potentially successful features that they might not have thought of during the early planning stages.

As Fueled strategist Aaron Cohen puts it, “The agile process allows for unparalleled flexibility and customization during the development cycle of an application. While we may be certain of fundamental features and user experiences, the interface details and specific user flows can be tweaked during development should new use cases arise or insights be learned. Our designers often create discrete graphic elements moments before the engineers incorporate them into the codebase, freeing the engineers from reliance upon product level design files and less-than-ideal graphic elements. This is one of the primary reasons why our apps, even our v1 releases, exude such an evolved design sensibility.”

To put the agile development process to use, let’s look at a real-life example: The origin of Instagram is Burbn, a check-in app that you’ve probably never heard of. The team realized it was too similar to Foursquare and as a result, they changed direction, focusing on the photography element of the product. In the world of app development, teams have to be able to pivot when a better opportunity presents itself. But by sticking to a plan that’s too rigid, this can be difficult, if not impossible.
Agile development isn’t about doing away with planning. It isn’t about being unfocused. It’s about making sure your team works in a way that allows it to make those pivots when they need to.

Delve into the world of Machine Learning (ML)

Posted on 13/05/2021 by Anteelo Master

For years, many industries have explored ways to incorporate artificial intelligence (AI) into their services because it provides a competitive edge. Since it is an evolving technology, the exploration of AI has brought about sub-concepts. One of the more significant concepts of AI is machine learning (ML), coined by Arthur Samuel in 1959 as “the field of study that gives computers the ability to learn without being explicitly programmed.”

MACHINE LEARNING

So, let’s talk a little more about machine learning and what it is. Just as Samuel described, machine learning is a subset of AI where computer algorithms are used to automatically learn from data and information, without being explicitly programmed.

By learning from data and information, the system is able to change and improve its algorithms on its own. The learning algorithm enables the system to identify patterns in observed data and build predictive models based on the observations. Because ML is used in situations where perfection is not expected, the goal is not to achieve perfect predictions but to achieve predictions that are good enough to be useful.

Just like the rest of the tech world, machine learning is not a simple concept. It’s important to note that the concept of ML is broken down into smaller subsets based on how much data the system is provided: supervised learning, unsupervised learning, and reinforcement learning. The type of learning is determined on if the information it’s fed is labeled or not.

PREDICTIVE ANALYSIS
The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.

Predictive analysis is used across a multitude of apps, like e-commerce, social media, finance, and even transportation. E-commerce apps use predictive analysis to provide consumers recommendations for other products to consider purchasing. The system detects patterns in items that are commonly purchased together and generates suggestions based on these patterns. Social media does the same, but instead of suggesting products, it recommends people to follow. As mentioned earlier, the system collects demographic data from users and (using unsupervised learning) creates patterns to make these suggestions to users.

There are tons of budgeting apps that will use your banking information (after giving permissions to link) to help you manage income and spending. The system analyzes your transaction history and detects patterns to offer appropriate suggestions for spending. That maps app you use to find the best route to get from work to home uses ML, too. The system records past traffic patterns associated with the time of day to provide recommendations for your commute. ML is in use all around you, and you may not have even realized it until now.

You’re probably asking yourself how the system undergoes training. The type of ML that you’re trying to use determines how much training the system has to undergo. The amount of training for the system is determined by how much data is initially provided to the system. Data is the center of ML, without it, the system wouldn’t know how to do its job. Before we dive into why and how we use the different types of ML, let’s talk about what they are first.

SUPERVISED LEARNING

Supervised learning is the process of ML when the system is initially provided data where the algorithm’s inputs (x) and their respective outputs (y) are correctly labeled. Because the input and output data are labeled accordingly, the system is trained to recognize patterns in the data with the algorithm. In future scenarios, this allows the system to receive inputs and produce correctly labeled outputs based on the pattern. Supervised learning is beneficial because it can be used to predict outcomes based on future input data without human interference, like when social media automatically recognizes someone’s face after you’ve tagged them in a picture. When you tag your Aunt Sally in a picture (y), social media stores the facial features of Aunt Sally (x). When you upload pictures of Aunt Sally in the future, social media recognizes her facial features (x) and automatically tags Aunt Sally (y).

UNSUPERVISED LEARNING

In unsupervised learning, data is fed to the system, but the outputs are not labeled accordingly like they are in supervised learning. Unsupervised learning allows the system to observe the data and determine patterns with the information it is given, rather than being trained to recognize the pattern. Once the system has stored the patterns it created, future inputs are assigned to a pattern (created by the system) to produce an output. Unsupervised learning is beneficial because it can show patterns in data that may have been overlooked when observed by humans. Unsupervised learning is used when social networks make recommendations of friends to follow; these recommendations are based on patterns created by demographic data individuals share online. If you went to University X and studied calculus, the algorithm is trained to recognize and recommend other individuals that went to University X and studied calculus as people you may know.

REINFORCEMENT LEARNING

Though it is a separate classification, reinforcement learning is a type of unsupervised learning. Similar to unsupervised learning, the data provided to the system is not labeled, so the system is left to create its own patterns. Where reinforcement differs from unsupervised is that when a correct output is produced, the system is told that this output is correct. This type of learning allows the system to learn from its environment and its experiences to explore a full range of possibilities. It is quite literally, learning through reinforcement. When Spotify makes a recommendation for a song (based on a pattern it noticed in your music selection) you are given the option to “thumbs up” or “thumbs down” the recommendation. When you indicate “thumbs up” or “thumbs down,” Spotify utilizes reinforcement learning because it’s learning your music taste based on what you tell their system.

MACHINE LEARNING APPLICATIONS

Why all the hype about machine learning? Well, because it is the next step in achieving artificial intelligence, and is a big step for app developers. ML gives apps the ability to improve and adjust based on user data, without developers influencing it to do so. This technology is saving time for developers (which saves you money) by enhancing the user experience with accuracy on what users want. While there is a multitude of uses for machine learning, two significant ones are image processing and predictive analysis.

IMAGE PROCESSING

Image processing is a very common application of ML, one that you probably see every day. It is used through supervised learning where an algorithm is used to detect various objects in a given image. Much like a child is taught that shapes with five sides are pentagons, the machine is taught to recognize objects in images. The machine is trained by providing a set of labeled images containing different objects; when given future inputs, the machine will identify objects within those inputs and label them. Apple’s Face ID feature is an example of image processing; when you set up Face ID, there is a series of steps you undergo to train the system (iPhone) to recognize your face. These steps include taking photos of your face from multiple angles so the system can analyze your face store this data. After being trained, the system recognizes your facial features as a means to unlock the phone.

PREDICTIVE ANALYSIS

The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.

HBase vs Cassandra: A Comparative Look

Posted on 12/05/202102/06/2021 by Anteelo Master

Selecting the best database management system is the key to ensure effective, streamlined app development process and a successful end outcome. However, choosing an ideal system for a project is not very easy for there are always a number of details to be considered at every turn. Especially when it impacts the performance of your project and the development process.

In this article, we will be taking an in-depth look into two such popular systems and look into how they stack up against each other: HBase vs Cassandra.

We will be exploring the essentials, architecture, performance, amongst other things.

What is HBase?
What is Apache Cassandra?
The Similarities Between HBase and Cassandra
HBase vs Cassandra: The Differentiating Factors
When to Use Which Database

Let us start with the overviews first.

What is HBase?

HBase is a distributed, scalable, column-based database with dynamic diagram for structured data. It enables efficient and reliable management of large data sets which are distributed among multiple servers.

HBase Architecture & Structure

It is a one of a kind database which works on multiple physical servers simultaneously, which ensures a smooth operation even though the servers are not operating together. HBase data model uses two primary processes for ensuring ongoing operations:

A. Region Server – It can support various regions. The region here stands for record array that corresponds to a specific range of consecutive RowKey. Every RowKey contains these elements –

Persistent Storage – It is a permanent storage data location in HBase. The files are used in the HDFS storage in HFile format. The RowKey sorts this data type and divides them in pairs, where each pair aligns with one HFile.
MemStore – It is a write buffer in which anything written to the HBase gets stored. When the MemStore reaches a specific size, the data gets written in a new HFile.
BlockCache – It is a read cache which enables you to save time on the data which is frequently read.
WAL – When the data is written into memstore, there is always a risk of losing it. WAL (Write Ahead Log) saves all the operations prior to its implementation. This way, the data can be recovered if something happens.

B. Master Server – It is the primary server of Apahe HBase. It manages regions distribution across Region Server, monitors regions, manages running of the ongoing tasks and performs a series of other necessary tasks.

To coordinate the action between services, it uses Apache ZooKeeper – a service for configuration and service sync management.

What is Apache Cassandra?

Cassandra belongs to the NoSQL-system class and is designed for creating reliable, scalable repositories of data arrays which are represented as hash. It works with key space, which aligns with the concept of database scheme in relational model. There can also be multiple column families that relate to the concept of relational table.

Apache Cassandra Architecture

The idea behind the Cassandra architecture is to have a P2P distributed system which is made of nodes cluster in which a node can accept the read or write requests. Every node in the cluster communicates the state information about itself and the other nodes through P2P gossip communication protocol. This together forms the basis of Cassandra data modeling and analysis.

At the center of the Apache Cassandra data model lies a Log Structured Merge storage engine. It comes with key elements like:

Memtable
Commit log
SSTables
Compaction

The overview of both HBase database management system and Cassandra must have given you an idea of how similar the features of HBase and cassandra can be.

The Similarities Between HBase and Cassandra

1. Database

Both HBase and Cassandra are NoSQL open-source databases (like Aerospike database). Both of them can handle large data sets and non-relational data, which includes images, audio, videos, etc.

2. Scalability

Both HBase and Cassandra have a high linear scalability feature. Under the feature, users who want to handle more data only need to increase the nodes number in cluster. This makes them both equally good choices for handling huge data.

3. Replication

In case of both HBase and Cassandra, there is a safeguard which prevents the loss of data even after it fails. This is done through the mode of replication. The data which is written on one node gets replicated on multiple nodes in a cluster. Because of this, if a node fails, a redundant node is always present for accessing data.

4. Coding

Both the databases are column-oriented which implement similar write paths. Columns are mainly the center storage unit in a database. Users can add columns according to their requirements. Additionally, the right path starts with logging a write operation to log file. It is basically done for ensuring durability.

Now that we have looked into what makes them similar, let us shift our attention to the difference between HBase and Cassandra.

HBase vs Cassandra: The Differentiating Factors

1. Data Models

While the terms of both the databases are more or less, there are some fundamental difference between HBase and Cassandra.

The column in Cassandra is like HBase’s cell. Its column family is also more like HBase table. On the other hand, HBase column qualifier is a lot like Cassandra’s super column.

One of the Cassandra key characteristics is that it only allows for a primary key to have multiple columns and HBase only comes with 1 column row keys and puts the responsibility of the row key design on the developers. Also, Cassandra’s primary key contains partition key and the clustering columns in which the partition key might contain different columns.

2. Architecture

HBase has a master-based architecture while Cassandra has a masterless one. It means that HBase comes with a single failure point, while Cassandra does not. The HBase client communicates directly with slave-server without contacting master, this gives a working time once the master is down.

Moreover, in the Cassandra and HBase comparison, the former supports both data storage and management, while in case of the latter, the architecture is only designed for data management while it relies on other systems/technologies for storage, server status management, and metadata.

3. Performance – Read & Write Capability

When the comparison is drawn between Apache Cassandra performance and Apache HBase performance, it is done on the front of read and write capability.

Write: Both HBase and Cassandra’s on-server write paths are fairly alike. There are some differences though which makes Cassandra better, like the difference in names for the data structure and the fact that HBase does not write to log and then cache simultaneously.

Read: If you are looking for consistent and fast reads, you should go with HBase. Since it writes on only one server, there is never the need of comparison between the various nodes’ data versions.

Even though Cassandra can handle over 129,000 reads in one second, the reads are targeted and there are high probability of them being inconsistent.

4. Security

Both HBase and Cassandra offer not only database-wide access control but also granualty of a certain level. Cassandra allows row-level access while HBase goes a step ahead and offers cell-level access. Cassandra sets the users roles and their condition, while HBase comes with an inverse move in which the administrators assign visibility label to the data sets and then informs user groups which labels they can view.

5. Infrastructure

HBase makes use of the Hadoop infrastructure which consists of moving parts such as HBase master, Zookeeper, Name and Data nodes.

Cassandra comes with several different operations and infrastructure. It also uses different DBMS in addition to the infrastructure. A number of Cassandra apps also use Storm or Hadoop. Additionally, its infrastructure is based on a single node type structure.

6. Support

The support specific Cassandra and HBase comparison looks like this – HBase doesn’t support the ordered partitioning, while Cassandra does. Ordered partitioning leads to making the row size in Cassandra to 10s of megabytes.

7. Nodes

In the case of Cassandra, the users have to identify nodes as seed nodes. These serve as the points for inter-cluster communications. In the case of HBase there are several master nodes. They monitor and coordinate actions of region servers.

8. Internode Communication

Both HBase and Cassandra have internode communication. While Cassandra uses the Gossip Protocol, HBase uses Zookeeper Protocol where a single node acts as boss through with the other nodes gets the necessary data.

9. Transactions

When it comes to HBase vs Cassandra comparison in terms of transactions, Cassandra comes with the feature of lightweight transactions. The mechanisms used here are Row-Level Write Isolation and Compare and Set. While, on the other hand, HBase works with two different mechanisms known as Check and Put and Read Check Delete.

10. Documentation

Cassandra’s documentation is a lot better than HBase’s documentation. Because of this, working on and learning Cassandra also becomes easier.

11. Query Language

Both HBase and Cassandra shell are based on the JRuby shell. Cassandra query language, is very specific. It is CQL (which is modeled in the line of SQL). Compared to HBase query language, the functions and features of CQL are far more rich.

The differences between HBase and Cassandra shows that there is no concrete answer to which database is better of the two. It all boils down to when to use which.

When to Use Which Database

The Cassandra and HBase use cases can be differentiated on the grounds of application type they are used in and the outcome expectation that an app development company has.

Use HBase if you need consistency in the large scale reads and if you work with a lot of batch processing and MapReduce for it has a direct relation with the HDFS.

HBase’s use cases consist of online log analytics, write-heavy applications, and apps that need a large volume, such as Facebook posts, Tweets, etc. Additionally, there is a large set of use cases related to Cassandra Hadoop integration.

Use Cassandra if high availability of large scale reads are needed. Also, since it requires a very minimum setup with less administration overhead it is a lot easier to get the process started in. It also offers greater flexibility in CAP theorem tradeoffs.

Some examples of what is Cassandra used for can be seen in the development of messaging systems, e-commerce websites, and real-time sensor data.

In short, use HBase data model and implementations when you have to analyze for big data or have to perform aggregations. Use Cassandra if you have to emphasize on interactive data and real-time transaction processing.

Python-The Ultimate Choice For Startups, Why?

Posted on 12/05/202102/06/2021 by Anteelo Master

Being in the market for over thirty years now, it is indubitable that Python has become the epitome of simplicity with effectiveness. The gradual increase in its popularity is something that raises the question of “why and how Python is becoming the most popular programming language?”.

Upon confronting any developer on this matter, one would come across responses such as – “It is highly readable”, “Building applications with Python is very easy because of the understandable code”, “It makes complex processes easy”, and so on and on.

However, today we will discuss the validity, feasibility, and scope of Python app development from the business front, i.e., from the vantage point of startups and establish why Python is the top programming language for your startup.

What Does a Startup Need from a Language?

To effectively deduce why Python poses as an absolute option for startups to go with, we should first discuss what are the factors that are prominent for a startup business.

High scope of scalability and the ability to add new features
Faster development of Minimum Viable Product
Quick yet efficacious iterations
Integration with other technologies and software
Time and cost-monitored development process

Now, when that is out of the way, we can positively move on to discussing whether Python actually fulfills all these needs of a startup to emerge as an exemplary choice.

Features of Python

1. Smooth integration

Unlike many programming languages out there, Python is pretty mellow when it comes to integration. You can easily integrate with other languages such as C, C++, Java, and so on.

2. Support TDD

Python is very popular for its test-driven development. It is quite easy for developers to create prototypes with it, and they can later convert them into fully-functioning applications by simply refactoring the code and testing it instantly.

3. Wide range of standard libraries

Though the list of why Python is quickly ascending the ladder of fame is tediously long, this is one of the primary reasons why. The easy availability of standard and robust libraries is what attracts developers and startups towards Python.

4. Ideal for ML and Big Data

Machine Learning and Big Data are two of the emerging technologies that have acquired eyeballs of many establishments globally. They both come blessed with tons of locked potential, something we are tapping into slowly. Among other kinds of applications written in Python, you can also develop apps integrated with these technologies, including a wide range of AI technologies as well, as they have many robust libraries to support the development.

Reasons Making Python An Ideal Choice For Startups

Let’s have a look at the reasons for why to choose Python for business and startups.

1. Remarkable choice for MVP

Being a startup, time is of the essence for you. You need to unleash your app in the market, targeting a certain niche before anyone else does it. Moreover, as a startup, you need to convince your investors that your app idea is something that is going to be a huge hit, and for that, you need to show them a working prototype of your soon-to-be-developed Python mobile app, hence an MVP.

Now, one of the best uses of Python is MVP development because of its expeditious nature. Python encourages robust and rapid development by allowing the software developers for startups to do quick iterations after getting the feedback. It is the pre-built modules and packages of Python that render it the ability to expedite the product completion process.

In fact, it has been observed that Python in comparison to languages like Java can provide a fully functioning MVP in weeks as compared to months; it is time-efficient, something that every startup requires.

2. Scalable

Scalability is one inevitable component that any startup cannot afford to overlook. As a business grows, its users grow too. Under such instances, it would become a priority for any business to have an application that has the scope of growth as well. This is what Python is good for. With the help of the Django framework, a series of wired-up and ready-to-go components, Python is able to provide highly scalable apps.

The components in the Django framework are decoupled, i.e., independent from each other so they can be replaced and unplugged as per the demands of the business. Moreover, with a wide range of libraries available for Python, it is a piece of cake to add new features to a product.

3. Versatile

Versatility is another great reason that makes Python an ideal choice for startups. This means if you need a code that works phenomenally across all platforms such as Windows, macOS, or even Linux, then Python should be the language of your choice.

Additionally, if you need to integrate technologies like Machine Learning and Data Science, then you need nothing but Python as the programming language to help you achieve the app you envisioned but better.

4. User-friendly

“Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.”

As you can see in the extract of Zen of Python philosophy, Python language works for of certain principles and guidelines that render it to be the most user-friendly language. This is one of the many reasons why developers love Python and have made it the second most popular language on Github beating even Java.

5. Community support

Community support becomes prominent when the team of developers faces problems during the product development process. Hiccups are unavoidable, but what matters is how active and capable is the community to provide possible solutions to certain issues that Python may face. Thankfully, the Python community is among the best ones out there as it strives towards curing all the issues the language may possess and improve its features and functions from time to time.

6. No need for extra developers

It is always good news for the startup mobile app company whenever they can save a few bucks. In comparison to other languages, Python offers a concise and rather easy code to app developers for startups, thereby eliminating the need for big teams for the same task while delivering the product of impeccable quality. This also helps developers to provide software development services for startups without any hassle. Let’s look at the example of the code in Java and Python which cater to the same function and purpose –

7. Security

Speaking of security, Python is evidently more secure than some other prominent languages, like PHP. It is because of frameworks such as Django that come loaded with built-in security features protecting the application from many security threats lurking on the internet.

This framework is capable of hiding the site’s source code from direct viewing by dynamically producing web pages and employing templates sending data to web browsers.

8. Helps combat complexity

As Python is very simple in nature and associates simplicity with effectiveness, it is a great medium to handle complexities. It is ideal for web solutions as it can overcome complexities related to the integration of different systems, among others. Qualities such as these are what together make Python ideal for a startup app development company as it is time-efficient, fast, and easy to work with.

9. Ubiquitous in nature

Do you know what makes sense? – Developing a solution that is based on ubiquitous technology. From Youtube to Dropbox to Reddit- all are avid users of Python language. It is something that is unprecedentedly proliferating as of now and will become a standard to which other languages will be assessed. So, if you want to develop an application that is going to perform just as remarkable as it would do in the present, then there is nothing better than Python for you.

Which Startups Use Python?

Look at the list of Python startups that opted for or are using Python as their choice and are now thriving in their particular niche. These use cases of Python are an ideal example of why Python is perfect for startups software development companies.

Ometria

Ometria is one of the perfect examples of startups using Python. This startup gives marketers the ability to create personalized experiences throughout the customer journey. They offer services such as cross-channel marketing, AI-enabled customer intelligence, and others.

Weglot

Weglot seamlessly integrates with your website, adapting to your technology, and delivering it in any language. It has solutions for e-commerce, marketing websites, and web agencies.

Paddle

It is a SaaS commerce platform for payment processing, billing, sales tax management, merchant of record. With Paddle, companies are finally able to transform their revenue delivery infrastructure into a strategic growth lever to respond faster and more precisely to every opportunity.

Stripe

It is a well-known American technology company that offers a platform for startups and big companies to accept payments, send payouts, and manage their businesses online. They also help companies to beat fraud, send invoices and manage business spend.

Virail

It is an online platform that provides the fastest way to choose the cheapest ticket or hotel. They also help to search for all routes and choose the best one for you. The platform works with 200 transportation companies and offers you the best travel solutions across the world.

Final Thoughts

According to many startup app development companies in USA, it is no contest that Python may become the programming language of the future. Every quality, feature, and function that we have discussed above is a clear indication that Python is the best language for startups.

In fact, besides startups, many fully grown organizations such as NASA, IBM, to name a few, are bending towards mobile app development using Python to fulfill their needs. Moreover, because of its ubiquity, it gains a little more edge over the other languages.

Zero-Knowledge Proof & its Role in the Blockchain World

Posted on 08/05/202102/06/2021 by Anteelo Master

Over the past few years, we have become accustomed to the way large banks and other firms access and employ our personal information to deliver us an enhanced experience. We have over time given them a ‘green signal’ to the mechanisms that use our sensitive details to help us sustain in a certain way. But then entered the Blockchain technology and it totally changed everything.

The Blockchain technology brought various characteristics like transparency, immutability, decentralization, and distributed ledger into existence. It enabled users to act anonymously and perform transactions with high-end security.

[Before we look further, we strongly recommend taking the time to understand the basics of Blockchain.]

Blockchain, in simple terms, gave users control to their privacy and future back.

But, has Blockchain really succeeded in doing so?

In one word, the answer is NO.

Many blockchain networks use public databases. So, anyone having an internet connection can view the list of the network’s transaction history. They can see all the details associated with the transaction and your wallet details, but the name of the user will still be unknown to them. Instead, they will come across as a public key – the unique code representing the user on the blockchain network.

This way, the public key created via the cryptography technique safeguard your privacy to some extent. But, it is still possible for one to expose you via other techniques.

This put your anonymous cover blown; debunking the myth of Blockchain’s anonymity and privacy, and make you realize that –

The user’s sensitive information stored on a Blockchain network is only confidential, not anonymous.

Likewise, there are various blockchain networks governed by consensus algorithms to deliver high-end privacy and stability, but decentralization is considered as a second priority in such cases. In many such cases, the two parties do not trust each other. [To read about these consensus models in detail, check out this blockchain consensus algorithm guide.]

This, as a whole, gives a clear indication that Blockchain is not that anonymous and decentralized as many of its enthusiasts believe it to be. And even gives birth to various questions, such as –

Do Blockchain networks really need to be anonymous? How can Blockchain offer more anonymity and better privacy protection to their users?

In the present Blockchain networks, the transactions are recorded in the public ledger and are transparent in nature. Because of this, various reputed brands and markets like Wall Street are hesitating to adopt this technology as the confidentiality of client and transaction is a must for them. This, as a whole, is questioning if at all Blockchain web 3.0 will be able to impact businesses.

Coming to the second question, there are various concepts and methods like Coin mixing, Ringct, and Coin Join that are making transactions anonymous in Blockchain, but the one that is highly appreciated is Zero-Knowledge Proof.

The one will cover in detail in this article.

So, let’s begin with a simple definition of zero-knowledge proof.

What is Zero Knowledge Proof?

Zero-knowledge Proof is an encryption scheme proposed by MIT researchers Silvio Micali, Shafi Goldwasser, and Charles Rackoff in the 1980s. In this method, one party (Prover) can prove that a specific statement is true to the other party (Verifier) without disclosing any additional information.

With definition being cleared, let’s take an example to understand how zero knowledge proof works.

Example: Kids and Candy Bars

Suppose, two children – Bob and Alice, have received some candy bars from a party. Bob wants to know if Alice has got the same number of candy bars or not. But, at the same time, none of them is ready to reveal the exact number.

So, what they do is that Bob brings four lockable boxes in a room, assuming that the number of candy bars received will be 10, 20, 30, and 40. He labels each box with a value corresponding to the number of candy bars.

Then, Bob keeps the key to the box that defines the number of candy bars he received in his pocket (let’s say he got 30 candy bars) and throws away the keys of all other boxes. And he leaves the room.

Now, Alice enters the room with 4 small pieces of paper and writes ‘+’ on one of them while ‘-’ on every other. Here, ‘+’ denotes the number of candy bars she got, while ‘-’ represents every other value.

She slips the paper piece with ‘+’ sign in one box (let’s say in the one representing 20 candy bars) and ‘-’ in the rest of the boxes. And she leaves.

Now, Bob enters the room again and opens the box whose key is in his pocket. Then, he checks if the box has a piece of paper with ‘+’ sign or ‘-’ sign. If it’s a ‘+’ sign, he realizes that Alice has an equal number of candy bars. While, in the other case, she doesn’t.

As we know that Alice has 20 candy bars and Bob has 30 candy bars, it is clear that Bob will find a ‘-’ sign in the lockable box whose key he has. This will make him clear that they both do not have the same number of candy bars.

At the same moment, Alice will re-enter the room and find a ‘-’ sign in Bob’s hand and she will also come to know that they have a different number of candy bars.

Note: By this method, Bob will learn that they do not have an equal number of candy bars. But, he will still have no clue if Alice has more or less candy bars than him, and vice versa.

Thus, the zero-knowledge proof maintains the privacy of users’ sensitive information, while making a transaction (in this case, the transaction is finding if they have the same number of candy bars or not).

Although this example would have helped you in understanding what exactly is Zero Knowledge Proofs (ZKPs), let’s refresh the concept with an image-

Now, as the concept of zero-knowledge proofs (ZKPs) is explained, it is the best time to look into what makes everyone prefer it over other available options.

Benefits of Zero Knowledge Proofs (ZKPs)

Simple – One of the prime advantages of zero-knowledge proof is that it does not involve any complex encryption method.
Secure – It does not require anyone to reveal any sort of information.

While these are the pros of Zero-knowledge proof, the concept has some disadvantages as well. A few of which are:-

Lengthy – In the zero-knowledge method, there around 2k computations, with each requiring a certain amount of time to process. This is the foremost con of going with zero-knowledge proof.
Imperfect – The messages delivered to verifier/prover might be destroyed or modified.
Limited – The zero-knowledge protocol demands the secret to be a numerical value. In other cases, a translation is required.

With this covered, let’s dig deeper into the technicalities before we evaluate when and how Zero-knowledge protocols can be introduced into the Blockchain ecosystem.

Starting with what are the core characteristics of a zero-knowledge proof.

Properties of Zero-Knowledge Proofs

1. Completeness

If the statement is true and both users follow the rules religiously, then the verifier would be convinced without any external help.

2. Soundness

If the statement is false, the verifier won’t be convinced in any scenario (even if the prover says that the statement is true for some small probability).

3. Zero-Knowledge

In both cases, verifier won’t be able to know any information beyond that the statement is true or false.

While the principles of Zero Knowledge Proof are covered, let’s talk about the different types of ZKPs a business enthusiast can invest in.

Types of Zero Knowledge Proofs

1. Interactive Zero-Knowledge Proof

In an interactive zero-knowledge proof, a prover performs a series of actions under the mechanism of mathematical probability to convince the verifier of a particular fact.

2. Non-Interactive Zero-Knowledge Proof (NIZKP)

As depicted from the name, Non-interactive zero-knowledge proof does not require an interactive process. Meaning, the prover can generate all the challenges at once and the verifier(s) can later respond. This restricts the possibility of collusion. However, it requires additional machines and software to find out the sequence of experiments.

Note: It is possible to make a transition from non-interactive to interactive ZKP.

Where to Implement Zero-Knowledge Proof in Blockchain System?

1. Messaging

In messaging, end-to-end encryption is imperative so that no one can read your private message besides the one you are communicating with. To ensure security, messaging platforms ask users to verify their identity to the server and vice-versa.

But, with the advent of ZKP, they will be able to build end-to-end trust in the messaging world without leaking any extra information. This is one of the prime applications of zero-knowledge proof in the blockchain world.

2. Authentication

Zero-knowledge proof can also facilitate transmitting sensitive information like authentication information with better security. It can build a secure channel for the users to employ their information without revealing it. And this way, avoid data leakage in the worst scenarios.

3. Storage Protection

Another possible use case of zero-knowledge proofs (ZKPs) is in the field of storage utility.

Zero-knowledge proof comes with a protocol that not only safeguards the storage unit, but also the information within it. Needless to say, the access channels are also protected to give a seamless and secure experience.

4. Sending Private Blockchain Transactions

When talking about sending private blockchain transactions, it is utterly important to keep it out of the reach of the third parties. Now, while the traditional methods are somewhat protective, they have some loopholes.

This is yet another area where ZKP comes into play. The concept, when integrated wisely, helps in making it nearly impossible to hack or intercept the private blockchain transactions.

5. Complex Documentation

Since zero-knowledge proof has the potential to encrypt data in chunks, it enables one to control certain blocks to provide access to a particular user, while restricting access for others. This way, the concept protects the complex documentation from those not authorized to see them.

6. File System Control

Another place where you can see an effective zero-knowledge proof implementation is the file system.

The concept adds different layers of security to the files, users, and even logins that makes it quite difficult for one to hack or manipulate the stored data.

7. Security for Sensitive Information

Last but not least, Zero-knowledge proof also refines the way blockchain technology is revamping transactions.

ZKP adds a high-end security level to every block containing sensitive banking information like your credit card details and history, such that banks need to manipulate only required blocks when a user requests for information. Other blocks remain untouched and thus, protected.

So, these were some of the use cases of zero-knowledge proof in the blockchain environment. To make your brand presence in the market by building one of these, hire a blockchain app development company.

And in case you are confused about its real-world implementation, check for the following existing projects operating with the convergence of two.

Real-Life Examples of Convergence of Zero Knowledge Proofs and Blockchain

1. ZCash

ZCash is an open-source and permissionless blockchain platform that offers the functionality to keep transactions ‘transparent’ and ‘shielded’ as per the requirement.

In the former case, the transactions are governed by a t-addr, just like bitcoin transactions. While in the latter case, a zero-knowledge proof called zk-SNARKs is used and the transactions are controlled by a z-addr.

2. ING

ING is a Netherlands based bank that has introduced its own zero-knowledge blockchain. However, they have modified their zero-knowledge system to make it a zero-knowledge knowledge range proof to lower down the need for computational power.

This way, they have prepared their zero-knowledge system to elevate the impact of blockchain in fintech.

3. ZCoin

The company uses Zerocoin protocol, which is based on zero-knowledge proof, to enhance security and anonymity in the transaction process. However, what makes it distinct from other projects working on this concept is that it offers scalability too.

Though various international actors have started showing an interest in implementing the concept of zero-knowledge proof into the blockchain, the adoption pace is too slow. And the prime reason behind is the following set of challenges associated with the addition of ZKP into the blockchain environment.

Challenges You Might Face While Integrating ZKP into Your Blockchain Project

1. Absence of Standards

Since blockchain technology itself is at its early adoption stage, there are no standards, system and homogeneous languages that enables app developers and business prospects to interact with the concept of ZKP and harness its potential in an efficient way.

2. Scalability

Another challenge that restricts the adoption of zero-knowledge proof in the blockchain environment is scalability, provided such algorithms require high computing capacity to operate on a high level.

Wrapping Up

Now that the concept of Zero Knowledge Proof and its scope in the Blockchain domain (along with real-life examples) is clear to you, we expect to find you investing in the broader application of the concept while stepping into the decentralized world. But, in case you still have any queries, connect with our Blockchain consultants.

Cross-Functional Teams Building Better Digital Product

Posted on 07/05/202102/06/2021 by Anteelo Master

Did you ever feel like your organization is working in silos? Designers want to deliver meaningful experiences, the Marketing team is keen on entering a new vertical while sales are focussed on bringing in more and more leads, but all of this happens in isolation. Even though the roles of each of these departments are varying in nature, their priorities and end goals must be clear and consistent. Otherwise, it’s a sheer wastage of the business’s resources spent across different directions. Cross-functional teams foster cohesion within the company, reduce wastage of valuable resources and enable you to maximize your efforts in order to accomplish goals faster.

More often than not, the underlying reason why companies struggle with building better digital products is how they prioritize milestones and the linear approach, where work is passed from one team to another without fully channeling the skill and expertise of each discipline.

Building a digital product requires a team effort. Each discipline has a role to play and sometimes, these roles are overlapping. A great idea can come from any person and any team. Organizations looking to grow with agility and speed need to build cross-functional teams and bring them to the center stage. When the skill of each discipline is valued, their perspective is heard and every discipline is represented at the table, it results in quality solutions.

So, What is a Cross-functional Team, anyway?

A Cross-functional team brings people together from different functional expertise. By mutually exploring the prospects of a project and through shared expertise the team accomplishes a common goal.

There are so many crucial roles in digital product design and development– Designers, Product Managers, Business Analysts, Engineers, Quality Analysts, Marketing Specialists, and the list goes on.

The approach behind a cross-functional team is to gather a good mix of people from various disciplines to work on a project together. It is about brainstorming ideas, uncovering different and unique perspectives, and channeling an umbrella of expertise towards achieving one common goal – how to build a unique digital solution.

The idea behind cross-functional teams stems from agile methodology. Every discipline deserves a seat at the table regardless that their department’s primary phase is underway.

What’s the need for a Cross-functional Team?

When teams collaborate together in every phase and stage of creating a digital product, the result is impactful. In a cross-functional team, each person understands the role of other team members and views the product not only from the perspective of his/ her field but as an entire product and as a team effort.

Having a Cross-functional team is extremely crucial because every person brings so much value to the table. When a diverse set of people come together, a problem is approached in all possible ways and we are able to uncover unique perspectives.

For instance, when an Engineer has a seat at the table right when the project kicks off, identifying the best technology to build a product becomes easy. While others in the team may not be technically adept, an Engineer brings a different level of technical understanding to the front, especially when they have worked with cutting-edge technologies in the past.

As a full-cycle app development company, we believe in agile development with cross-functional teams. By building cross-functional teams, we were not only able to reduce the cycle time in new product development, but our teams also demonstrated flexibility in adapting to changing market needs and were able to develop innovative solutions at a faster pace.

While the number of Designers involved in a project will be more in the Design phase and the number of Engineers will rapidly increase in the Development phase, having representatives from each discipline throughout the app development process regardless of the phase adds a lot of value to the product.

The approach behind a cross-functional team resonates quite well with the practices of DevOps.

DevOps can be seen as a cultural perspective on how teams should be engaged in working the right way.

In today’s fast-paced world, with more advanced projects coming up, the need to move faster and be agile is ever-growing. DevOps addresses this concern – it makes development, operations, and other groups within an organization come together, collaborate closely on shared goals, and deliver software faster and more reliably.

Just like DevOps emphasizes close collaboration between software development and operations, having a cross-functional team for the entire product-development process improves collaboration across disciplines like Strategy, Design, Development, Testing and results in quality outcomes.

Benefits of Cross-functional Teams in Digital Product Development

The advantages of a cross-functional team in digital product development are manifold.

When all disciplines collaborate closely and everyone is invested in maximizing their contributions towards the project, it results in the best product possible.

1. Knowledge Sharing

The key idea behind accomplishing goals through interdisciplinary work is the exceptional variety of talent figuring out how to create something that is meaningful and brings value to end-users.

Confronting new perspectives, learning from others, and looking at things holistically impact the quality of solution and results in a robust product.

2. Alignment on Goals

In a cross-functional team, people look beyond their roles and responsibilities and are focused on accomplishing the common goal. There is total alignment on set milestones and people collaborate together to achieve them in the best way possible.

3. Less Handovers

When you collaborate across disciplines and everyone is present throughout the product development lifecycle, it minimizes handovers and speeds up the processes.

4. Teamwork

Working together facilitates teamwork. It brings people closer and improves communication. It is easy to talk through the issues and brainstorm great ideas. Collaborating closely builds respect and trust in teammates.

5. Identifying issues

When a project is handled by a cross-functional team, everyone is up to date with the project’s progress. When people work at the same time and don’t focus solely on their disciplines, it is easy to identify bugs and flag issues that may trouble the user.

6. Iteration

As mentioned above, cross-functional teams are up to date with all the issues within a project, which implies, making changes to the design or the code becomes easy. Testing and iteration can be done at the same time and it saves a lot of time, effort, and money.

7. Positive customer experience

While there are many different roles in an organization, each of these functions exists to serve the customer. By encouraging effective communication across teams, it becomes easy to provide a meaningful tailored customer experience.

8. Increased Innovation

When a group of people from different functional expertise comes together to solve a common problem, it is relatively easy to generate creative and overall solutions.

Companies that use cross-functional teams include marketing perceptions, sales conversations, product usage data, and other relevant sources of information into keen consideration when making product decisions which result in smarter decision making. The power of cross-functional teams is to drive better decision-making through business-wide learning.

Challenges faced by Cross-functional Teams

Effective cross-functional team collaboration is the key to tackle an organization’s biggest problems. However, there are certain problems with cross-functional teams and sometimes they can be difficult to manage.

Conflicting Goals

Each discipline comes with a set of roles and responsibilities. Oftentimes, when functional experts from different teams come together, they tend to give less priority to the project and are more focused on their own goals.

In such cases, it is essential for leaders to identify strategic goals to make sure there is clarity of purpose and the whole team is alined, on the goals of the team or the company.

Too much or too little Communication

With a cross-functional team, it is crucial to decide the right measure of communication. Too many unnecessary meetings lead to loss of time and can harm productivity while little communication regarding the project status and dulication can negatively impact a project.

Choosing Ideas

In a cross-functional team, you may give up on good ideas when others don’t like them.

While sometimes, it becomes easy to let go of complicated ideas and work more efficiently. This is where Discovery Workshop and operating in an agile manner comes into the picture. Scrum Methodology helps cross-functional teams encounter the biggest challenges and achieve great outcomes.

Leadership

In a self-organizing team with no clear leader, there is a possibility that the team may get lost and feel there is no clarity of purpose. It is essential to have a leader that can provide sufficient oversight so that small problems do not snowball into a bigger crisis and the team is able to collaborate efficiently.

Conclusion

Cross-functional teams often struggle because the organization lacks a structured approach. Teams are affected by insufficient oversight, conflicting goals, lack of accountability, and the organization’s failure to measure the impact of cross-functional collaboration.

Corporate structures are reorganizing themselves into cross-functional teams since the advantages are many. It may take time to adopt the cross-functional team approach to building products but it has nearly three times the success rates and is worth the rewards.

Making WFH Effective

Posted on 07/05/202118/06/2021 by Anteelo Master

Last year, around this time, we were contemplating how to shift our office to remote work. It’s going to be a year soon and it’s unbelievable how comfortable we are now with this new setup. I remember some of my colleagues worrying about the new rules of remote working. They had never worked from home before, even for a day. They believed that with family around, home is a place of distractions. Most of them worried about how they would participate in distraction-free meetings, concentrate on their work, and be productive.

A few days back, I asked how are you dealing with the ‘new normal’. And I was surprised to hear that they have started liking this new way. They even expressed a little hesitation in coming back to the workplace.

I totally get it. As Robin Sharma says-

“All change is hard at first, messy in the middle, and so gorgeous at the end.”

Last year, the work-from-home culture was barbaric for everyone. And now that all of us have adjusted to the humdrum of the WFH routine, going to the office seems like a hard task.

I wouldn’t be surprised if, in the coming days, office spaces become obsolete. Buffer and AngelList even released a State of Remote Work report where they shared insights on how the workplace of the future would look like.

Now that we know what the future of work looks like, shouldn’t we prepare ourselves for this new change? Absolutely. So in this blog, I’ll share what individuals and leaders can do to make WFH more effective and productive-

What can individuals do?

In the remote setup, one of the most important things is setting up a work desk for yourself. You don’t need an elaborate arrangement to make way for that. Just a table and a comfortable chair in a corner where you can work without any disturbance.

This and a robust internet connection. Now that remote work is going to stay, investing in this setup should be your first priority.

Next on your priority list should be– create a routine.

Why, you ask? Because the only thing we miss about the office is a routine. A routine of commuting to work, reaching our desk, participating in team rituals, working on individual tasks, taking breaks when we need them, and then wrapping up the day to come back home to family.

In the remote world, building a routine will help you wade through endless distractions (from home or mindless internet browsing) and the guilt of under-achieving your planned tasks. So create a To-Do list and plan your day in advance. Dress up as you would do for work and have a start and end time, just as before.

The next thing that you can do is make meetings interesting. Now that you can’t see your team in person, switch on video calls to maintain that human connection. In my team, we have this unsaid convention to be on video everytime we call each other. And we always start our team meetings with casual chit chat, asking questions about how everyone is doing. Even if we have nothing new to share, we just keep it casual for a few minutes before jumping on the agenda of the meeting.

Ask yourself– what did you do in the office that made it easy for you to go there everyday? When you have your answers, recreate the same atmosphere for yourself at home.

What can leaders do?

Get things in order: The remote work culture puts additional responsibility on the leaders. Now, you have to figure out the what, how and where of everything– what will be the new way of doing things? How will the communication happen? Where will the team collaborate? How to arrange for access to client networks on personal laptops? What would happen if someone’s laptop breaks down? How would your IT-support team interact with those facing software issues?

It would have been much better if we all had got the time to prepare for these infrastructure changes. But, as they say, problems don’t matter, solutions do. So, as a leader, your first responsibility is to get things in place for the remote setup.

Check on the mental health of your team members: The next thing that leaders have to make sure is that people are focused, committed, and happy in their work. Remote work can be harsh for people who live alone or who only have workplace colleagues as friends to keep them company. Help them wade through this time by guiding them on how they can take care of themselves. All they need is a caring and empathetic leader who understands their point of view.

Don’t let productivity take a dip: The biggest advantage of remote work is the time you cut down on commuting. With more time on hand, there is no reason for productivity to take a dip unless there are some serious issues. Of course, there will be some changes in the work-schedule. Afterall, your home is a makeshift workplace. But that shouldn’t affect productivity for the long-term.

So, constantly keep a check on the quality of deliverables and raise flags whenever anomalies appear. When you see someone doing extraordinary work, or someone slacking off, share your feedback immediately.

In case you have to do some intense conversations– be it work-related or behavioral– come prepared with incidents to back up your observations. Ask them what they need from you in order to succeed. There is a chance that your team members wouldn’t feel comfortable speaking up on video call. In that case, give them the freedom to share their feelings through email or voice call.

Communicate transparently: Share everything with the team even before they begin to realize something is amiss. It’s natural for your team members to feel intrigued about what’s going on in the organization– when would the office open up? How long will they be working remotely? When the office opens up would they all be required to join or can they continue to work remotely if they wish? How is the sales and hiring pipeline shaping up? Has pandemic affected revenue streams? If yes, how bad is it?

All of these questions and many more are bound to crop up in their mind. As their leader, you need to answer to everything so that they feel connected to the organization.

At times, all of this can be a little overwhelming for the leaders, especially the new ones. In that case, just hold on to your values and lend an empathetic ear to your team members. Give them the benefit of doubt. It’s a challenging time for everyone. The best you can do right now is trust them (not blindly) and ask them to trust you.

There is a lot that we can do as individuals, leaders and organizations to better deal with this pandemic. But if you want to continue with remote work even after the pandemic is over (is it ever going to be over, I wonder?), this is not the time to do everything in one go. It’s the time to take things slow, experiment and see how we can expand our capabilities.

Opening gates to remote work gives opportunities to organizations to tap into the global talent pool. And if that’s what organizations are looking for, this is the right time to invest in creating practices and guidelines for the team members and set them up for success.

Colors in Health

Posted on 07/05/202117/06/2021 by Anteelo Master

Do you know why doctors wear a green/blue surgical scrub while operating? Have you ever wondered why walls are muted in color at adult patient rooms and brightly colored at children recovery rooms? The reason is colors influence human behavior. They affect our mood and emotions. They can even alter our perceptions or modify our actions.

For instance, blue and green are cool and calming colors. They bring a sense of tranquility and put patients at ease while they are on the operation table. Similarly, white is a color associated with warmth and peace. Colors like white, beige, have a calming effect on patients under intensive care. Likewise, blue, green, yellow and pink are suited for pediatric units where children are recovering.

The choice of colors make an impact in physical spaces as well as digital products.

Colors evoke different emotions in people according to their age, environment and condition. Therefore, it’s important to consider color theory while designing healthcare products.

So, let’s see how we can use the right colors to heal and comfort our end users.

Prescription delivery apps

Amazon has changed the entire delivery gamut. Most people have a mindset of “why should I go out if I can order it from my mobile phone”. The basic feature of prescription delivery apps is to ensure easy access to medications on need-basis. Users may also need to consult with the staff remotely, or ask for a refill of their prescription.

For such kinds of apps, green and blue are the best choice of colors. They are refreshing, not too harsh on the senses and encourage concentration. These colors also work well for the pharmacists who are processing a lot of prescriptions by the hour. So, these colors help them concentrate on small details and deliver the right medication to intended recipients.

Informational and health communities apps

Mobile apps that share informative healthcare articles have just one aim- to make people more aware of diseases, medications, treatment methodology, and so on. They want to make users in charge of their own health by disseminating information on their portal.

In such applications, we need to give users a control over what content they want to consume. We also have to ensure that the app functions well for all age groups. Including too many colors might hinder readability for elderly users whose vision declines with age.

The primary color of such apps should be white, followed by blue and yellow. White is a color that denotes cleanliness, freshness, and simplicity. You can use shades of blue and yellow to convey depth and power. Yellow is associated with positivity and warmth. These colors add liveliness and encourage people to keep reading.

Patient-engagement apps

Patient engagement apps work at various levels- they connect patients with doctors, through online consultation. They remind patients when it’s time to take medications. They encourage patients to exercise regularly. They keep a track of insurance renewal, and so on.

These apps also store sensitive information and personal health data. Therefore, the app colors must invoke a feeling of security as well as encouragement. These patient engagement apps aim to make users accountable for their own health. Therefore, colors that are soft and neutral must be used.

Orange is a color that signifies health, happiness, and encouragement. Green is the color of nature and it denotes freshness, and progress. One can even use shades of pink as they represent kindness and affection. Darker shades of pink or orange can be used in designing medication reminders and pills tracker.

Personal wellness apps

A major shift has happened in the way we manage our health. With apps that can monitor sleep, calories, heartbeats, and even steps, personal wellness apps have an increasing consumer demand.

Personal wellness apps help in motivating people to track their vitals, self-monitor critical signs of illness, and stay fit. Therefore the colors in these apps should give a playful, cheerful and cool vibe. Shades of purple, blue, and pink are the right colors for these kinds of wellness apps.

Purple denotes ambition and devotion which fits well with the mindset to track and improve health. Blue and pink add the coolness and kindness quotient. Pink exudes the message of playfulness as well as tenderness.

Remote consultation and monitoring apps

Remote consultation and health monitoring apps help in keeping the patient connected with their doctors. They are especially useful for the elderly or disabled who can’t visit the doctor’s clinic regularly.

In such apps, one important thing to consider is the choice of colors for all age groups. Color perception changes with age and demographics. What works for middle-aged people wouldn’t necessarily work with elderly. So, during the user research phase, identify your user persona and decide who your target audience is. People in their 30’s or 40’s might prefer bright and vibrant colors that are playful and cheery. On the other hand, older people are likely to enjoy softer, soothing colors like beiges and whites.

EHR apps

Patients are more informed now than ever. Technology has changed the way healthcare data is accessed and inferred. The end users want to analyze their personal health data and take control of their health. They also want to keep their privacy intact.

This means that the color palette of the application should give a confident, trustworthy and cool vibe. So use neutral colors and a lot of white space so that the users can access important information in a hassle-free way. You can also use warm tones to show graphical information.

In conclusion, I would just say start by reading about the psychology of colors. Understand the color wheel. Deep dive into various factors that affect people’s perception. All this will help you in deciding the best color combinations for your app. Colors that are well thought out after doing research on the demographics of the app, are timeless. They always attract new users and keep them engaged, no matter which trend appears or disappears.