​If you are thinking about becoming a coder after you have finished school, you likely already know about LeetCode, a software application designed to help you study coding, practice, and improve your coding ability.  It contains a large database of coding problems and exercises that help users to improve their skills, along with sets of tools (editors, debuggers, forums, etc.).  However a portion of LeetCode’s content is also designed to help one prepare for a ‘technical interview’, such as one might encounter if applying for a coding job at a large corporation, with common interview questions, practice algorithms, and data structures that can be used to simulate what might be asked in an interview.  This makes LeetCode an ideal tool for interview preparation, and also provides interviewers with lots of potential coding questions that can be ‘asked’ during an interview.
As is human nature, even the smartest of us has pangs of inferiority (except Elon Musk), as did a sophomore in Columbia’s Department of Computer Science, as he used a self-developed ‘cheating tool’ that improved his interview performance enough to receive employment offers from Amazon (AMZN) , Meta (FB) TikTok (pvt) and Capital One (COF).  The cheating tool (named ‘Interview Coder’) acts as an invisible plug-in that cannot be detected by software used for remote test monitoring and is able to create perfect answers to questions with a few minor flaws.  That said, after it was discovered that the sophomore had cheated on his interviews, he was expelled. 
Interviewers look for those that are constantly glancing at the right or left during interviews, or those that are just a bit slower to answer a verbal question (waiting for the answer to come up on another computer), but the ‘Interview Coder’ is much smarter than that and was specifically designed to deal with LeetCode content.  Interviewers favor LeetCode content because it is easier to pull questions from LeetCode than it is to write hundreds of original ones. However ‘Interview Coder’s’ ability to be a ‘hidden’ window and to use system permissions  to bypass the browser’s recording processes, while it remains an open widow on the cheaters computer that can be queried without any cursor movement or keyboard tracking.  It can even break down complex or ambiguous questions into sections to make sure the cheater can answer them with a complete level of understanding and can copy them directly to the test, with a few spelling or grammatical errors thrown in.
Looking to capitalize on his new application after his fall from grace at Columbia, the former sophomore put the app on Github, where it immediately gained large scale recognition.  At a cost of $3,000/month for server support, a $60/month user price, and a 99% profit margin, the app took in $228,500 in its first month, and less than 2 months after its launch it had an annual recurring revenue of $2.2m.  He marketed the application through social media, posting pictures of himself cheating on exams..
LeetCode questions have been long criticized as ~90% having little or nothing to do with actual day-to-day coding work.  Despite that fact, Google (GOOG) uses it for interviews that can take up to 300 hours of answering coding questions.  As the pass rate for those exams is less than 2% it is not surprising that the application is so popular, especially when the typical competitive ratio for a single position garners over 200 interview candidates, making the $60/month fee against a $100,000+ job easy to justify.

Digital twins, no not Mario & Luigi or the Property Brothers, although both could be considered digital twins if they are viewed on any digital media, but ‘real’ digital twins, meaning digital constructs that are used in analytics and predictive analysis.  Since 2002, when Michael Grieves introduced the concept[1] at an engineering conference.  NASA began using the idea in 2010 when it created simulations of space capsules and vehicles for testing, and has since been cited as one of the top 10 technology trends of the last few years. 
So what is a digital twin?  Simply a digital twin is a mathematical model of an object.  Static mathematical models (aka Monte Carlo simulation) represent an object at a given point in time and have been around for centuries, but dynamic mathematical models are time dependent and therefore change as parameters change over time, and while external observational changes could be made to dynamic models, only recently has technology been available to make those changes without human intervention.  The data for such models can be as simple as the mathematical description of the physical characteristics of a golf ball or as complex as the entire workings of a manufacturing plant, but the most important characteristic of a digital twin is that it is dynamic and can be used to simulate or mimic the actual object or objects on which it is based, without affecting the object itself.
Digital twins have innumerable uses in a wide variety of applications, ranging from stress testing to workflow, and can be used both before a product is developed, during that development for product scenario simulation, and after a product is developed for testing and modifications to extend the product’s abilities, with data from sensors that monitor the product in actual use feeding back information to the twin.  This also includes real-world historical data that can be added to the twin, all of which can be used to predict how the product will work and in what ways it can be improved, without the myriad prototypes and static models that are usually needed during product development and improvement.
There are many technologies on which the digital twin concept relies.  AI, analytics, and machine learning, and advances in these areas can certainly go toward increasing the value of digital twins across a broad number of applications and industries, but while IoT and sensor technology is usually given short shrift relative to flashier technology, the ability to feed real-time information from working products to digital twins is a major step forward that will add considerably to their value, and that data will allow faster product development and more responsive products as the digital twins gather more information. 
Of course, we can’t have wires running from sensors on equipment running across the country to product development or testing labs, so how will all of that real-time information get to digital twins? 5G would be the ideal mechanism for moving the data as close to real-time as possible and the highest transport speeds in 5G are those using mmWave spectrum.  While the necessity for mmWave speed and low latency are less for the typical mobile user, the bandwidth of mmWave does allow for lots of data to be sent without congestion or bottlenecks, which bolsters the case for mmWave private networks in industrial or business settings, particularly where digital twin applications are more commonplace. 
By mimicking physical assets, process operations, and frameworks, digital twins paired with 5G IoT transport can allow employees to view equipment in remote locations in real-time to solve production or maintenance issues without travel, such as in oil refineries, and can help to improve automotive design for vehicles from trucks to Formula racecars, and can solve some of the more recent global supply chain issues by creating and defining more efficient logistic networks.  Building maintenance and space optimization can be simplified using a digital twin and in retail, sensor information can be fed to a digital twin to predict customer behavior and the financial impact of a wide variety of scenarios, so while technologies like AR/VR get much of the headlines, digital twin software is the more practical side of the digital world and with growth estimates between 25% and 35% over the next few years, the opportunities for the expanding use of digital twins seems obvious, especially as IoT sensor and data transport technologies improve.


[1] The actual digital twin concept came from “Mirror Worlds” by David Gelerner in 1991.

The global population has faced some pretty serious epidemics over the last few hundred years, with biggies like Bubonic Plague in the mid 1300’s that took out between 30% and 60% of the European population, or the Spanish Flu of the early 1900’s that took out close to 100m.  HIV, still active since the 1980’s has taken over 36m lives, and our own generation’s COVID-19 has killed more than 4.5m and continues to do so across the globe, but we, as a society continue to survive despite these epidemics, yet there are some more subtle and perhaps even more insidious epidemics that afflict the global population at almost every level, and are supported by companies that generate trillions of dollars promoting such plagues, despite a level of addiction that continues to increase each year.
No, we are not talking about opioids, as that is a problem far beyond our area of expertise and scope, but one that afflicts consumers almost every hour of their waking lives and eats into the fabric of our society and lives a bit more each day.  Recent statistics on the use of mobile apps reveals that in 12 of the 16 markets shown below consumers spend more than 4 hours/day using mobile applications, and that number has increased from 8 of the 16 above 4 hours/day in just one quarter.  Assuming the average individual sleeps 7 hours a day, that comes to a staggering 24.7% of a US citizen’s time spent staring at a smartphone, while in Indonesia that number grows to 32.4%.
Rather than rant about how companies like Facebook (FB), Google (GOOG), and YouTube (GOOG) continue to work toward increasing that time so their user data becomes more valuable to those that want to sell you just about anything, we will let members of Congress embarrass themselves with their public display of their lack of knowledge concerning social media, so the question then becomes, what are we doing while we are staring at our smartphones?  We take the data one step further and break down the regions shown in Fig. 1 by the 5 most downloaded apps during the 3rd quarter. 

The obvious winner on a global scale is TikTok (pvt), which showed up in the top 5 in many of the major regions shown below.  Instagram (FB) was ranked second, followed by Facebook on a worldwide basis based on downloads, however in terms of spending, while TikTok still maintained the top position in 3Q, YouTube and Tinder (MTCH) were 2nd and 3rd.  Facebook continued to hold the number one position in active users, followed by WhatsApp Messenger(FB) and Instagram.

We break out the top 10 worldwide as to spending in the table below:

​It is hard to imagine that we spend so much time on our phones given that for those that are gainfully employed, the available time left, excluding sleep and work averages out to about 9 hours of ‘free time’.  For those in such a mode, 4.2 hours (US) would represent 46.7% of that free time spent on one’s phone, in theory.  There are times when our phones are used for work and other important communication, but given TikTok’s popularity, it would seem that much of that time is being idled away watching videos of anonymous folk doing odd things.  If that hasn’t changed the fabric of society, nothing will, but underlying it all is the need for content providers to hook you in to an ever increasing time on whatever application becomes tomorrow’s number one.  Sure, you can quit anytime, right?