Apache Kafka – A Scalable Messaging System

Kafka is a distributed messaging system that allows to publish-subscribe messages in a data pipeline. It is a fast and highly scalable messaging system and is most commonly used as a central messaging system and cnetralizes communication between different and large data systems.

Image reference: http://kafka.apache.org/documentation.html

Advantages of using Apache Kafka

1.) Highly Scalable:

As mentioned earlier, one of the major advantages of using Kafka is that it is highly scalable. In times of any node failure, Kafka allows for quick and automatic recovery. In a world that now deals with high volumes of real-time data, this feature makes Kafka a hands down choice for data communication and integration.

2.) Reliable and Fault – Tolerant:

Kafka helps to replicate data and also supports multiple subscribers. Thus, in case of any failure there is no fear of data crash. Kafka is a fault-tolerant messaging system,  thus making it a highly reliable pub-sub messaging system among the many others

3.) High Performance:

Kafka is super efficient at handling real-time and complex data feeds with high throughput and lesser delays. The data and stored messages can run into terabytes, yet Kafka delivers high performance and the best companion for any enterprise Hadoop infrastructure.

Popular use case scenarios for Apcahe Kafka

1.) Messaging

A message broker is used for many reasons such as separating data lines from data producers, buffer and load unprocessed images etc and Kafka works as the best messaging broker to support all these activities. Also, with the credibility of being fault-tolerant and highly scalable, Kafka is a good solution for processing large scale messages.

2.) Website Activity Tracking

The main use of Kafka was to help to track and analyze real-time feeds of complete website activity such as page views, search, publish and subscribe and any activity that user performs on the site. All these activities are stored as separate topics in the data pipeline.

Kafka is also used to track high volumes of data activities as each page view can generate multiple messages.

3.) Log Aggregation

Kafka helps to collect distributed data files and puts them all together in a central place for processing. It de-clutters the extra details and only gives log and event data that has been recorded. Kafka is better suited from other log-centric systems because of greater performance and durability due to data replication.

4.) Stream Processing

Kafka helps to process data in multiple stages where the raw input data procured from Kafka topics is aggregated, enriched and transformed into new topics for further data mining.

Right from crawling content, to publishing it and further categorizing it under relevant topic and then attempting to recommend the content to users, Kafka does it all! The processing pipeline is quick and has low latency. It also provides real-time data graphs and hence is considered to be the most reliable stream processing tool.

Thus, Kafka is an amazing big data processing tool that most MNCs such as LinkedIn, Twitter, Pinterest and many more use as their publish-subscribe messaging system and also to track data. Its durability and scalability give Kafka an edge over other big data solutions.

Understanding Smart Cities

Imagine a city that has a nearly zero carbon footprint. It uses analytics to determine the best routes for electric mass transit systems, reducing traffic and carbon dioxide emissions.

Crime is low due to gunshot detection systems and facial recognition systems.

Large data sets and advanced analytics are used to deter criminals and help law enforcement patrol areas statistically most likely to have problems particular times of day or after certain events.

Utilities such as water, waste removal and electricity are administered efficiently, maximizing the cities resources and reducing costs to end users. Wireless network access blankets the city and works seamlessly as you move about your day.

If you need a permit, request records or interact with the government, it happens electronically and instantly. No more waiting in line, no more dealing with flash, flash, hundred yard dash.

It sounds lovely I’m sure. The vision for “Smart Cities” is something like the above depending on who you talk to. But what is a smart city and is the future it promises achievable?

A smart city, in short, uses networked devices and data analytics to harness the power of the “IoT” and “Big Data” in order to improve the lives of people who live in heavily populated cities.

Smart cities are important to the growth of the human race. As the population of those who live in urban environments explodes, the need for good planning and infrastructure is going to become critical. The world’s urban population has grown from 746 million in 1950 to 3.9 billion by 2014.

The term ‘smart cities’ has been increasingly used over the last few years and recently more-so than ever before. With a multitude of next generation wireless technologies such as Li-Fi, 5G, LoRa and network slicing being developed alongside the emergence of IoT (Internet of Things) our experience of large towns and cities is set to be enhanced considerably.

It’s very important that these wireless technologies are developed quickly to provide the infrastructure for so many of the proposed aspects of smart cities, such as connected cars, water-systems and transport systems.

The issue that developers currently face is the existing mobile and wireless infrastructure. 3G and 4G came before the real inception of IoT and smart devices so they lag behind considerably with the technologies required to support such a complex network.

In order to cater for the devices that make up for the anticipated $1.7 trillion spend that consumers will have made by 2020, network operators are going to need to improve their infrastructure.

This poses the question, what technologies are being proposed to cater for requirements of smart cities?

Network slicing

Network slicing is the process whereby network operators will be able to divide or ‘slice’ their networks depending on the requirements of its users.

To meet the needs of smart cities, smart services will rely on low latency to offer essential real-time and ultra-reliable connectivity and interactivity. For example, autonomous drones are going to need incredibly fast response times, but won’t necessarily require fast data rates.

On the other hand, things like cloud based services will be reliant on fast data rates, but not necessarily need low latency. This is where network slicing comes in. It will allow providers to “slice up” their 5G networks to meet the different requirements of different services.

Li-Fi

Li-Fi gets its name from its similarity to Wi-Fi, only it uses visible light rather than radio waves to connect to devices. Due to its nature, it can reach staggering speeds of up to 224 gigbits per second. Something that will be very useful within a high-speed, high-demand network.

One of the most obvious benefits of Li-Fi will be its ability to connect places where Wi-Fi can’t. It uses LED (Light-emitting diode) bulbs to bring very fast and reliable connectivity to users within its proximity.

LoRa

As we move towards smart cities, millions of low power sensors will replace wide area networks and connect to provide connectivity and interconnectivity for users moving within that space.

This is where LoRa comes in.

LoRa is a low power, low range wireless network platform which is secure, highly available and highly reliable. It has been developed specifically with IoT and smart cities in mind. Importantly, it is configured to work on multiple frequencies within the free radio spectrum, which allows for applications across very wide geographical areas and connecting both indoors and outdoors.

LoRa’s most compelling opportunity lies in its ability to connect and manage cities own sub-systems such as waste-management, agriculture, parking, lighting etc. By connecting simultaneously, it will provide the platform for easy, comprehensive management.

There is no denying smart cities will be a reality very soon and the 5G technologies behind them are sure to include an array of sophisticated technologies that will enhance and add to our existing infrastructure. When this will be fully operational across the world’s major towns and cities is unclear, but Li-Fi, LoRa and network slicing are sure to provide the foundations of the fast-approaching 5G future.

IBM Watson – Technology Or Magic ?

Isaac Asimov, a science fiction author wrote a trilogy series called “Foundation” in 1950s. The foundation is all about a scientist named Harry Seldon who picks up a group of high IQ people in different fields at a very early age of 8 to 10 years and creates a civilization on an uninhabited planet. A super computer governs this civilization. Since all the people are of known behavioural trend, this computer not only analyses  characters and their offsprings, but also governs them silently. At any given time it can predict who is going to be their leader, how long he is going to rule and who will be the successor. It can predict the entire civilization for next 150 years. When an issue arises, the computer can predict and provides the solution for the same. It learns from the current civilization to prepare prediction for next 150 years.

Now the entire story is far fetched, but seemingly plausible, thanks to Watson. That is the power of Watson. Its artificial intelligence, though not as accurate as that depicted in the fiction, it is a starting point.

IBM Watson can analyse all the data fed into it and come up with an accurate prediction. This is not an easy task for any computer or logic. It really pains us when somebody thinks Watson just answers queries. It is not a product or a piece of code, it is an IBM (marketing) brand used for a whole bunch of stuff.

Please don’t confuse a framework with an algorithm. Tensorflow is a software library that can be used to implement a number of machine learning algorithms. It’s the algorithm itself that matters, not the framework. Tensorflow is just a library that helps with parallelism, which is only useful in a hand full of cases.

IBM developers – as far as I know – are a bit indifferent when it comes to libraries. They rely heavily on (and contribute to) open source and will use whatever works best. A lot of the components/algorithms they use are much older than TensorFlow and most machine learning libraries. If you ask me, they probably have built most of this stuff from scratch without using any particular framework.

IBM Watson is a cognitive computing based Artificial intelligence super computer which uses unstructured big data as a source. Watson is a question answering computer system capable of answering questions posed in natural language.

Watson is a question answering computer system capable of answering questions posed in natural language, developed in IBM’s DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM’s first CEO, industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy!

In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.

Watson received the first place prize of $1 million.

Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage including the full text of Wikipedia, but was not connected to the Internet during the game. For each clue, Watson’s three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game’s signaling device, but had trouble in a few categories, notably those having short clues containing only a few words.

In February 2013, IBM announced that Watson software system’s first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with health insurance company WellPoint.  90% of nurses in the field who use Watson now follow its guidance wholeheartedly.

At the core, Watson is a complex NLP system. Numerous processes are involved that are rule-based, such as Lucene building a variety of indices, based on rules, as one of 20+ pre-processing steps for corpus content i.e documents that contain the domain knowledge.

There is a second phase where humans provide examples of implicit rules. A textual query is related to a portion of the corpus, Q&A, essentially telling Watson that when it sees the same query after training it should respond with the area of the corpus indicated.

The challenge is that Watson, and NLP in general, is a non-deterministic system based on probabilities. The training process above is repeated thousands of times and the algorithms  build up probabilities of the relationship of a text query to an area of the corpus.

Some experts will suggest that IBM Watson is a failure and some will tell you that it is the biggest technological marvel ever. The debate will be forever, the lesson is to take the positives from the Watson and build on it.

Harnessing its powers is the way forward.

PHP 7 Traits Revealed

PHP 7 comes with latest features and fast performance as compared to its previous versions. PHP 7 has been introduced with a goal to free up space which leads to improvement. It was crucial to get rid of many deprecated functionalities , old and unsupported Server APIs and extensions to increase the speed and free up space.

This clean up provides more security by removing items that have deprecated for a while in PHP 5 and not in use for a long time. PHP 7 can break the code if your app is running on older version of PHP.

PHP 7 is a platform that can deliver powerful app, all from cloud to enterprise applications and from mobile to the web Applications. Almost everything comes under the umbrella of PHP 7. This version has the most powerful impact as it decreases the memory consumption with extreme improvements in performance.

Let us have a look at some of the new features PHP 7 is equipped with.

Speed Improvement

PHP 7 is benchmarks for consistently showing speeds twice as fast as PHP 5.6 . It provides unmatched computation speeds and flexibility to adapt to an ever changing environment. Thanks to the new Zend Engine 3.0, apps see up to 2x faster performance and 50% better memory consumption than PHP 5.6, allowing you to serve more concurrent users without adding any hardware. Designed and refactored for today’s workloads, PHP 7 swiftness paves a new path for all developers. 

Type Declarations

Type declarations simply means specifying which type of variable is being set instead of allowing PHP to set this automatically. PHP is considered to be a weak typed language. In essence, this means that PHP website development does not require you to declare data types. Variables still have data types associated with them but you can do radical things like adding a string to an integer without resulting in an error. Type declarations can help you define what should occur so that you get the expected results. This can also make your code easier to read.

Since PHP 5, you can use type hinting to specify the expected data type of an argument in a function declaration, but only in the declaration. When you call the function, PHP website development will check whether or not the arguments are of the specified type. If not, the run-time will raise an error and execution will be halted.

Also, with PHP 7 we now have added Scalar types. Specifically: int, float, string, and bool.

Error Handling

Handling fatal errors in the past has been next to impossible in PHP website development. A fatal error would not invoke the error handler and would simply stop your script. On a production server, this usually means showing a blank white screen, which confuses the user and causes your credibility to drop. It can also cause issues with resources that were never closed properly and are still in use or even PHP 7, an exception will be thrown when a fatal and recoverable error occurs, rather than just stopping the script. Fatal errors still exist for certain conditions, such as running out of memory, and still behave as before by immediately stopping the script. An uncaught exception will also continue to be a fatal error in PHP website development in 7.

New Operators

-Spaceship Operator: The spaceship operator, or Combined Comparison Operator, is a nice addition to the language, complementing the greater-than and less-than operators. The most common usage for this operator is in sorting.

-Null Coalesce Operator: The Null Coalesce Operator, is effectively the fabled if-set-or. It will return the left operand if it is not NULL, otherwise it will return the right. The important thing is that it will not raise a notice if the left operand is a non-existent variable.

Easy User-land CSPRNG

What is Easy User-land CSPRNG? User-land refers to an application space that is external to the kernel and is protected by privilege separation, API for an easy to use and reliable Cryptographically Secure Pseudo Random Number Generator in PHP website development. Essentially secure way of generating random data. There are random number generators in PHP, rand() for instance, but none of the options in version 5 are very secure. In PHP 7, they put together a system interface to the operating system’s random number generator. Because we can now use the operating system’s random number generator. Secure random numbers are especially useful when generating random passwords or password salt.

There are quite a few other features added in PHP 7, like unicode support for emoji and international characters. Another big area that could cause trouble, are features that have been removed. This should really only be an issue if you’re working with an older code base, because the features that have been removed are primarily ones that have been deprecated for a long time. If you’ve been putting off making these necessary changes, the huge advantage in speed with PHP 7 should help convince you, or management, to take the time needed to update your code.

That Thing About Internet

We are in the midst of one of the greatest shifts in manufacturing since the Industrial Revolution. At the heart of this disruption is the IoT. Smart connected products provide manufacturers incredible insight into how customers use products and services. This new level of understanding, combined with manufacturers’ deep product knowledge, is rapidly evolving business models and creating a competitive advantage. Manufacturers can use the data collected from IoT to lower costs, reduce downtime, improve future designs, and even push new features to equipment directly via the Internet. Forward-thinking manufacturers are using IoT to transform their business models by moving from one-time product transactions to ongoing product-as-a-service offerings. 

But what is IoT?

And how did it emerge as a game changer?

The Internet of Things allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention.

When IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, virtual power plants, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing Internet infrastructure. Experts estimate that the IoT will consist of about 30 billion objects by 2020.

The concept of a network of smart devices was discussed as early as 1982, with a modified Coke machine at Carnegie Mellon University becoming the first Internet-connected appliance, able to report its inventory and whether newly loaded drinks were cold. Mark Weiser’s seminal 1991 paper on ubiquitous computing, “The Computer of the 21st Century”, as well as academic venues such as UbiComp and PerCom produced the contemporary vision of IoT. In 1994 Reza Raji described the concept in IEEE Spectrum as ” small packets of data to a large set of nodes, so as to integrate and automate everything from home appliances to entire factories”.

Between 1993 and 1996 several companies proposed solutions like Microsoft’s at Work or Novell’s NEST. However, only in 1999 did the field start gathering momentum. Bill Joy envisioned Device to Device (D2D) communication as part of his “Six Webs” framework, presented at the World Economic Forum at Davos in 1999.

The term “The Internet of Things” was coined by Kevin Ashton in a presentation to Proctor & Gamble in 1999.  Ashton is a co-founder of MIT’s Auto-ID Lab.  He pioneered RFID use in supply-chain management.  He started Zensi, a company that makes energy sensing and monitoring technology.  He later sold the company to Belkin.  He has been involved in other startups, such as ThingMagic.  He is also the author of the book How to Fly a Horse: The Secret History of Creation, Invention, and Discovery.

In a 2009 article he wrote for RFID Journal, Ashton explained the term:

“The fact that I was probably the first person to say “Internet of Things” doesn’t give me any right to control how others use the phrase. But what I meant, and still mean, is this: Today computers—and, therefore, the Internet—are almost wholly dependent on human beings for information. Nearly all of the roughly 50 petabytes (a petabyte is 1,024 terabytes) of data available on the Internet were first captured and created by human beings—by typing, pressing a record button, taking a digital picture or scanning a bar code. Conventional diagrams of the Internet include servers and routers and so on, but they leave out the most numerous and important routers of all: people. The problem is, people have limited time, attention and accuracy—all of which means they are not very good at capturing data about things in the real world.

And that’s a big deal. We’re physical, and so is our environment. Our economy, society and survival aren’t based on ideas or information—they’re based on things. You can’t eat bits, burn them to stay warm or put them in your gas tank. Ideas and information are important, but things matter much more. Yet today’s information technology is so dependent on data originated by people that our computers know more about ideas than things.

If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.

We need to empower computers with their own means of gathering information, so they can see, hear and smell the world for themselves, in all its random glory. RFID and sensor technology enable computers to observe, identify and understand the world—without the limitations of human-entered data.”

From a remote machine of coke which could report its inventory to a city which operate on itself,  IOT has indeed come a long way.

How to create a MarketPlace for your business

Probably you have a novel business idea that is keeping awake until late night, or you are just excited at the sudden success and trend of collaborative economies such as Air Bnb, Uber and want to try your hand at building a similar business model, then we can safely assume that you are interested in setting up your own marketplace.

Starting up today has become more easier than before and as the trend sees it, Startups are soon the way of the future. Sam Altman, the president of Y Combinator emphasizes that for any Startup to excel, there are 4 areas that need a concrete vision:

1. Idea
2. Product
3. Team
4. Execution

(Read more about this in Sam Altman’s lecture here..)

Image source: Art of Education

Whatever you are building, be it a multi-national business or a non-profit organisation, it all begins with the idea.

So, before you build your marketplace, we want to ensure that you have these five things sorted out to help you sail easily through your business.

Five things to keep in mind while building a MarketPlace

1. Solve a real-life problem

Most entrepreneurs are always very excited about their Startup idea. But do you have a vision that can translate this idea into a real-life product?

When creating a marketplace, you need to be 100% convinced of solving a real problem – for both the user and the service provider. That is how collaborative economies work – it is all about bring buyers and sellers on a unified platform and transacting business (something similar to e-commerce)

For example: Air BnB has disrupted the hospitality and travel industry because it solved one simple but important problem – finding cheap and nice places to stay. Hotels are not always an economic option especially for those who travel on budget trips. At the same time, Air BnB has shown that even the real estate market can be open to the hospitality industry with the right strategy. 

The only way to test your idea is by developing a minimum viable product and launch it. In this way you can be sure of the market and demand for your product / service.

2. Think as a Collaborator

If you are looking to tap into the collaborative economy, then you need to start looking at unused or idle assets that you can unlock. Whether it is creating a marketplace to make aviation parts easily available or creating an online metal trading portal, you need to think of your marketplace as a key that unlocks resources that would have otherwise not been easily available to consumers.

The benefit of creating a marketplace is that you can start one for almost anything – renting bikes, teaching classes, food or even gardening. As per the collaborative economy Honeycomb study by Jeremiah Owyang, the hottest startups are in transportation, goods and services and money.

Innovation is not always about creating something new, it is about doing the same thing in different ways. It is time that you put your thinking hat on and look at the resources around you in a different way. Think of opportunities of how you can acquire the maximum potential and that is when you will have a great marketplace idea.

Image source: Juggernaut

3. Look for fragmented markets

One of the key ingredients to the success of a marketplace is when you reduce the gap between customers and service providers. Think of the small players and the inaccessible products that are of great value but lack an organised business structure.

For example: A big challenge in the freelance market has been to find qualified and dependable freelancers who can work your projects. With marketplaces like UpWork, it has just become so easy to filter out the right freelance workers for your business – be it video, content writing, design etc. Thus, both buyers and service providers can easily communicate and transact business at ease.

Thus, think if your marketplace as a one-stop shop to satisfy all your customer requirements – right from finding the product / service, evaluating it, reading reviews and buying it. As a service provider, your platform will be his door to a larger target audience. Hence it is a win-win for both parties, solving both problems – thus re-iterating point number one – solving a real problem.

4. Enable a layer of trust

Trust forms the basis for any business and the same rule applies to online marketplaces as well. One of the biggest reasons for users to shy away from transacting online has been the quality of products and trust in sellers.

Transacting second-hand goods (such as OLX) has become more acceptable these days only because the portal allows buyers to directly interact with sellers, check product reviews, thus enabling a layer of trust between the two parties.

Your marketplace is not a directory to just list products and services. It is about creating an environment where buyers and sellers collaborate and communicate before transacting business.

For example: Skill Share is one such trusted platform for education where users who want to learn new and diverse skills can reach out to industry experts and sign up for their classes. Skill Share has a policy which checks and establishes the credentials of the teacher.

Marketing gurus such as Seth Gordan have their courses listed on Skill Share and this creates a brand trust at a subconscious level, thus enabling easy on-boarding of users.

Thus, it is very essential that your marketplace is able to create this initial level of trust with users.

5. Narrow your market focus

While it is nice to spread your wings wide and fly, when it comes to creating a marketplace, it is best when you cater to a particular segment of products / services and users.

Don’t think of yourself as Amazon or eBay with a hundred million products. Just focus on solving just that ONE single problem that your marketplace has been designed for.

The only reason Air BnB and Uber have been successful in disrupting the market is because the former focused on providing temporary accommodation while the latter focused on creating an on-demand transport service for users. Both of them has a clear and narrow focus on the problem they wanted to address and solve for users.

Also, when you narrow down your focus you will be able to identify your true competition. There could be a particular niche that has not been tapped into and you could create a marketplace for that particular product.

Trying to be the next Air BnB or Uber with your market place is not the best strategy to move forward. Instead, trying to create a seasonal renting space or creating a taxi service specially for women / kids could be the kind of marketplace innovation that you must think of.

To sum it all up….

All we can say is, you can create a marketplace for practically any product under the sun, but these five points will help you stay focused on what matters.

Also, at GoodWorkLabs we have a fool-proof solution that can help you build a marketplace solution that can help you promote your business to millions of users across the world.

Read about our marketplace solution here.

Microsoft Azure

The tech giant, Microsoft has launched major new machine learning tools. The company has made exciting announcements for developers who want to build new AI models.

Microsoft has launched three new tools at company’s Ignite conference. The new set of tools are designed to help developers working new AI models as well as those who want simply want to use pre-existing models. The company has launched Azure Machine Learning Experimentation service, Azure Machine Learning Workbench, and the Azure Machine Learning Model Management service.

Azure Machine Learning Experimentation Service

The service is designed to help developers train and deploy ML experiments. Microsoft has added the support for popular open source frameworks like PyTorch, Caffe2, CNTK, TensorFlow, and Cahiner. The service is designed to scale from local machines to hundreds of GPUs in the cloud. It also supports Apache Spark on Azure HDInsight clusters.

Azure Machine Learning Model Management Service

This is a desktop client for Windows and Mac. The tool can act as a control panel for your development lifecycle. Microsoft is projecting the tool as the great way to get started with machine learning. The new model management service uses Docker containers. The developers and data scientists can manage and deploy their models to any Docker container. Microsoft has included its own Kubernetes-based Azure Container Service.

Microsoft will continue to expand its toolbox for developers that want to build machine learning applications. The company is promoting its tools among startups that are working on innovative technologies. Microsoft’s latest offerings in Machine Learning can fuel the innovation across the globe.

Learning The Right Way

Technology, as a practitioner, is counter-intuitive.  This is perhaps the hardest concept for people learning to become technologists to understand.  It seems simple, but it affects so many things that will occur as you learn and grow your skill-set and build things.  The purpose in most cases of course is to build useful abstractions that make using technology feel intuitive to the end user, but as the practitioner you have to deal with the burden of this inherent lack of intuitiveness so that your users don’t have to.  

Bad technologies do exist, and in many cases, they’re bad because they attempt to make things easy which are not in any way easy or generally considered solved, and therefore encourage bad practices or hide critical problems.  PHP is a perfect example of this, MongoDB is another example of this.  There are hundreds of examples though that meet this criteria.  

Many of these bad technologies are very popular with beginners simply because they are easy to learn and work with.  The problem is, you will end up learning bad habits and putting trust in things which are not trustworthy.  This may not matter much when you’re just playing around on your own time to see how something works or try something new.  But the things you learn always end up coming back to you.  Your time is perhaps your most valuable asset, and how you invest it will determine many of the future choices you will end up making when you’re professionally using those skills to build something.

Because, ultimately, technologies are tools, just because a technology is bad in the general case doesn’t necessarily mean it should never be used.  But, it takes a significant amount of experience and expertise to adequately understand all the possible pitfalls of using a bad technology and whether or not those trade-offs are acceptable.  When you’re in the stage in your career where you’re still learning, this is not the time to throw away your very valuable time by investing in learning bad technologies.  You are much better off investing in learning technologies which are generally good, and ensuring you understand where all their skeletons are buried and what pointy edges exist, so that you can accurately and intelligently decide what trade-offs you’re willing to make in your designs.

The Definitive Answer

This is kind of the philosophical answer as to why we recommend not learning PHP as their first language.  While PHP is very easy to learn at a superficial level, it has a lot of faults and glitches that are not immediately apparent and are a direct result of it having a fundamentally bad language design.  These glitches can be near invisible, because they may not even prevent your application from working, but they could lead to opening up serious remotely exploitable security vulnerabilities or create a ticking time bomb waiting for the right conditions to cause the application to crash.  This type of unpredictable behavior is extremely frustrating even when you understand what is causing it, but as someone who is learning it wastes your time and distracts you from learning a clear understanding of the abstract concepts involved.

Python is an extremely sane, structured language, which is designed specifically to enforce good practices and help guide new programmers into understanding the proper way to build things.  Some of this is enforced in the interpreter, some of this is enforced in the community and the standards of companies and projects that use Python.  Either way, Python is an excellent first language to learn.  

The absolute best way to become a competent programmer, somebody who your peers look up to and rely on to be the person who can really solve problems, is to put a lot of effort in early on learning things the hard way.  Rather than picking heavily abstracted frameworks and technical tools that do a lot of heavy lifting for you, it’s important to understand why those pieces of heavy lifting are being done and what is going on in the background.  Python is a great introduction to this, because it’s abstracted enough to keep it from making you get dejected while learning, it provides immediate feedback because it’s interpreted, and it also provides an easy sliding scale to get you into deeper systems understanding.  

As technology continues to change and improve we have no doubt that there will be a need for you to continuously learn new things.  Don’t start from a easy to learn foundation, learn the fundamentals in a proper way so you have the right knowledge to act as building blocks for a long time to come.  

The choice is ultimately yours.

GoodWorkLabs rated as Top Mobile App Developer in Bangalore by Clutch

We are pleased to announce that GoodWorkLabs has been ranked under the top iOS and Android app developers in Bengaluru!

Clutch is an independent research firm based in Washington, DC, which conducts independent reviews for tech companies like ours. Analysts rank companies that provide similar services using a unique algorithm which allows potential clients find their best possible partner.

While Clutch provides rankings on IT categories ranging from cloud storage providers to digital marketing agencies, their most competitive category is app developers.  

Among the ranking matrix, GoodWorkLabs has been ranked as the third best mobile app developer in Bangalore with raving client reviews from clients globally.

In a recent review, the founder of a startup gave us a glowing five-star review for our prototype of his iOS ride sharing app:

We are happy to add Clutch’s award to our growing list of recognition american companies such as Deloitte and Google.  

Please check out our profile on Clutch if you would like to read more reviews.

Clutch identified the best agencies and developers in India based on a long-standing proprietary research methodology that evaluates each company on its market presence, demonstrated by the services offered, clients served, and quality projects.

We are super happy that GoodWorkLabs is part of this prestigious ranking and has been trusted by users globally.

You can read the entire press release here.

Elon Musk – The Tech Savior

Elon Musk is doing everything in his power to achieve a perfect future. And he is the only businessman doing it. He has several innovative companies that work to better the future of the world. And the tech media has gone crazy over him.

Musk has become the “cool” face of technology. He is regarded as one of the most innovative businessmen in the world with SpaceX, Tesla, Hyperloop, OpenAI, and many more.

However, Musk does one thing no businessman does. He allows other companies to use his patents. He wants to promote growth of technology instead of promoting his company.

With his expertise after founding and running multiple Internet companies such as Zip2 and PayPal, it was fairly easy for Musk to create the next Internet company with a higher chance of success, but instead he chose in some of the most difficult industries: Space and Electrical cars because he believed in these causes.

Musk felt the need to get people inspired in space exploration, the need to prove that electric cars can be successful and those are the things that drove him to do what he did even when the chance of success was very low.

It takes a genius person to code a simple space survival game (called Blastar) at the age of twelve and scale that vision to founding a multi billion dollar giant (SpaceX) with the goal of making human life interplanetary and ensuring our existence, dealing with many obstacles and challenges along the way, something no one’s ever done before.

It takes a genius person to visualise an online payment system and go from there to starting an electric car company in Silicon Valley and remain more profitable than General Motors and Ford, something that’s never happened since 1954). From outsourcing the Hyperloop transportation idea to investing in SolarCity, OpenAI and NeuraLink, nobody is more conspicuous in looking like they’re saving the world, but Elon is actually doing a tremendous job. Sure, he loves the limelight and is flawed. But he’s achieved more than what most can only dream of in their lives and that’s what makes the hype deserved. There’s no stopping him.

“I don’t ever give up. Never. I mean, I’d have to be dead or completely incapacitated.”

In an interview when he was questioned about Eugene Cernan and Neil Armstrong’s criticism and testification that privatising space industry and allowing SpaceX government funding is a bad idea. He could do so much more from where he stands now.

His Achievements

– He could change the face of space industry by himself and a small dedicated team of spacex by making reusable rockets ultimately decreasing the cost to travel to space.

-His small team of AI has been able to beat the world champion of dota2 recently

-He started building underground tunnels from his Boring company which will solve the traffic problems in US.

-His Tesla has already got the order of more than 400000 units.

As with his many companies his ultimate goal is solving a problem and bringing a major positive change in human’s life, not just creating another product or offering another service that many other companies already do.

There are many car companies out there, but what makes Tesla unique is the fact that its cars aren’t built with the mere idea and purpose of taking people from place A to place B, they are made with the purpose of bringing a change, to prove a point and to set an example for others that an environmental friendly electric car is not only possible but better in so many ways.

The hype around Elon Musk and his companies is completely justified. He is trying to save humanity by foreseeing the future and the risks involved.

Honestly, he is the real life Tony Stark!