April 2020

It’s a crazy time in the tech job market right now (believe me, I know), and we need all the help we can get! That’s why it’s good to see things like Career Compass Group’s “Job Beat”, a digital hiring magazine created by Steve Rosen to help people hiring managers and techies looking for jobs find each other.

There’s lots of useful information on their site, including their Dear Recruiter, Help! series of short (10 minutes or less) podcasts, which alternate between being aimed at hiring managers and job-seekers:

There’s also a blog, hiring FAQs, and information about their workshops. Check them out!

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Why just increment when you can increment ANIME STYLE?

by Joey deVilla on April 27, 2020

Unfortunately, you can do neither in Swift. The ++ operator has been gone since Swift 3, and the closest Swift will let you get to the cool anime way of incrementing is i -= -1. There has to be a space between the -= and -1; otherwise you get hit with this error message: Use of unresolved operator ‘-=-‘

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Yes, the title of that 2012 article in Harvard Business Review may have stretched things a bit. If you’re a regular reader of this blog, you know that any scientific, tech, or engineering endeavor has its long stretches of dullness and drudgery. You also know that if you can make it past those stretches, the work’s pretty rewarding.

If you’d been meaning to get into data science, today’s your day. For the entire day of Monday, April 27, 2020 and only until the stroke of midnight that marks the start of Tuesday, April 28, 2020, Manning’s solo liveProject courses are selling for $10 instead of the usual $50 or $60.

Manning liveProjects are learn-by-doing exercises. They start with a challenge that isn’t all that different from one you might encounter on the job, and the project is about addressing that challenge.The project is broken into several milestones where you can check your progress against a tested reference implementation. Along the way, you’ll have access to book and video resources selected for your project, as well as opportunities to collaborate with other participants. You do it at your own pace, and if you’d like extra help, there’s a (pricier) version with a mentor.

Here are the liveProjects on sale:

Discovering Disease Outbreaks from News Headlines
Imagine this: You are a data scientist at the WHO trying to get a handle on a virus outbreak. Your task? Use machine learning techniques to analyze news headlines gathered from around the globe for clues about its spread. What do you do?

Work and learn with over 1000 other participants in this liveProject. In Discovering Disease Outbreaks from News Headlines, you’ll analyze a database of headlines gathered clusters on a map to find patterns indicating an epidemic. As you work through this liveProject, you’ll develop techniques for text extraction, data manipulation, clustering, interpreting algorithm outputs, and producing an actionable report.

Decoding Data Science Job Postings to Improve Your Resume
Imagine this: You step into the life of a budding data scientist looking for their first job in the industry. There are thousands of potential roles being advertised online, but only a few that are a good match to your skill set. What do you do?

In Decoding Data Science Job Postings to Improve Your Resume, you’ll learn how to use libraries in the Python data ecosystem to analyze text-based data, such as resumes and job listings. As you build this project, you’ll clean data from HTML files, use text similarity analysis to find the perfect job, and visualize your results using word clouds and plots. When you finish, you’ll be ready to apply your new skills to any text analysis task.

Human Pose Estimation with Deep Neural Networks
Imagine this: You are a machine learning engineer working for a company developing augmented reality apps, including apps like fitness coaches that need to be able to reliably recognize the shape of a human body. Your challenge is to create an application for human pose estimation: detecting a human body in an image and estimating its key points such as knees and elbows. What do you do?

In Human Pose Estimation with Deep Neural Networks you’ll build a convolutional neural network from scratch, training your model using Google Colab and your GPU. At the end of this liveProject, you’ll have completed an interactive demo application that uses a webcam to detect and predict human keypoints!

Training Models on Imbalanced Text Data:
Imagine this: You are a data scientist working for an online movie streaming service. Your bosses want a machine learning model that can analyze written customer reviews of your movies, but you discover that the data is biased towards negative reviews. Training a model on this imbalanced data would hurt its accuracy, and so your challenge is to create a balanced dataset for your model to learn from. What do you do?

In Training Models on Imbalanced Text Data, your challenge is to create a balanced dataset for your model to learn from. You’ll start by simulating your company’s data by deliberately introducing imbalance to an IMDb (Internet Movie Database) review dataset, experimenting with two different methods for balancing this dataset. You’ll build and train a simple machine learning model on each dataset to compare the effectiveness of each approach.

Use Machine Learning to Detect Phishing Websites:
Imagine this: You’re a data scientist employed by the cybersecurity manager of a large organization. Recently, your colleagues have received multiple fake emails containing phishing attacks, one of the most common—and most effective—online security threats. Your manager is worried that passwords or other information will be given to an attacker. What do you do?

In Use Machine Learning to Detect Phishing Websites, you’ll build a machine learning model that can detect whether a linked website is a phishing site. As you go, you’ll sort out what’s safe and what’s a security risk, use common Python libraries, clean and query datasets, learn performing hyperparameter tuning, and summarize the performance of your models.

Building Domain Specific Language Models
Imagine this: You’re a NLP data scientist working for Stack Exchange. Your boss wants you to create language models that are tuned to the particular vocabulary of different Stack Exchange sites. Language is domain specific, so an insurance company’s documents will use very different terminology than a post on a social media site. Because of this, off-the-shelf NLP models trained on generic text can be inaccurate for specialized domains. What do you do?

In Building Domain Specific Language Models you’ll build a language model capable of query completion, text generation, and sentence selection for the domain-specific language of the Cross Validated statistics and machine learning site. Challenges you’ll face include preparing your datasets, building and evaluating n-gram word-based language models, and building a character-based language model with AllenNLP. At the end, you’ll have built a foundation for any domain specific NLP system by creating specialized, robust and efficient language models!

Training and Deploying an ML Model as a Microservice
Imagine this: You’re a developer for an ecommerce company. Customers provide reviews of your company’s products, which are used to give a product rating. Until now, assigning a rating has been manual. Your boss has decided that this is too expensive and time consuming. Your mission is to automate this process. What do you do?

In Training and Deploying an ML Model as a Microservice you will have to train a machine learning model to recognize and rank positive and negative reviews, expose this model to an API so your website and partner sites can benefit from automatic ratings, and build a small webpage using FaaS, containers, and microservices that can run your model for demonstration. You’ll learn how all parts of machine learning tie together, and how to effectively deploy a model to production.

Monitoring Changes in Surface Water Using Satellite Image Data
Imagine this: You’re a data scientist at UNESCO. Your job involves assessing long-term changes to freshwater deposits. Recently, two satellites have given you a massive amount of new data in the form of satellite imagery. Your task is to build a deep learning algorithm that can process this data and automatically detect water pixels in the imagery of a region. What do you do?

With Monitoring Changes in Surface Water Using Satellite Image Data, you will design, implement, and evaluate a convolutional neural network model for image pixel classification, or image segmentation. Your challenges will include compiling your data, training your model, evaluating its performance, and providing a summary of your findings to your superiors. Throughout, you’ll use the Google Collaboratory coding environment to access free GPU computer resources and speed up your training times!

3D Medical Image Analysis with PyTorch
Imagine this: You’re a machine learning engineer at a healthcare imaging company, processing and analyzing MR brain images. Your current medical image analysis pipelines are set up to use two types, but a new set of customer data has only one of those types! What do you do?

In 3D Medical Image Analysis with PyTorch your challenge is to build a convolutional neural network that can perform an image translation to provide you with your missing data. Utilizing the powerful PyTorch deep learning framework, you’ll learn techniques for computer vision that are easily transferable outside of medical imaging, such as depth estimation in natural images for self-driving cars, removing rain from natural images, and working with 3D data.

I ordered them all, paying $90 in the process. I’ll write about my experiences as I do each of these courses.

If you’re interested, go visit the promo page for these discounted liveProjects and place your order before midnight!

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Even more online events this week!

by Joey deVilla on April 27, 2020

In addition to my weekly listing of events in Tampa Bay, here are even more online events aimed at techies taking place this week from all over the world, and most of them are free! Thanks to Diversify Tech for the finds.

Monday, April 27

  • ThoughtWorks — Infrastructure Webconf @ 1:00 PM (FREE!)
    Our Infrastructure WebConf allows technologists interested in DevOps to listen and ask questions to Infrastructure specialists Marion Bruns and Max Griffiths. Our ThoughtWorks’ subject matter experts will explore best practices and thought-provoking learnings on the state of Infrastructure, testing and workflows for Infrastructure teams.
  • General Assembly — Inside the Design Studio @ 6:00 PM to 7:30 PM EDT (FREE!)
    This inspiring panel event series invites key players in New York City’s design community to offer a rare insider’s look at how they work and create. From branding to user experience to city planning, panelists will discuss how they approach projects from a design point of view, how design thinking methods help with problem-solving, and much more. Plus, they’ll cover opportunities within the field and their vision for the future of the industry.

Tuesday, April 28

Wednesday, April 29

  • Red Hat Summit, Day 2 (FREE!)
    Red Hat Summit is the premier open source technology event for thousands of IT professionals to innovate and focus on high-performing Linux, cloud, automation and management, container, and Kubernetes technologies.
  • Candor — Free resume workshop — honest advice on getting your resume in front of recruiters @ 1:00 PM EDT (FREE!)
    In this session, Candor cofounder Niya Dragova will share insider tips on nailing your resume.
  • IBM Developer — Hands on Introduction to NLP @ 12:30 – 2:00 PM EDT (FREE!)This workshop introduces Natural Language Processing in Python. Attendees learn how to process text with NLTK and Gensim to derive useful insights. Foundational concepts like tokenization and part of speech tagging and complex topics like Word2Vec, sentiment analysis and topic modeling are covered.How can computers interpret something so human like language? Can they actually understand what we are saying or are they hiding behind a façade of rules and algorithms? How do these systems of zeroes and ones make sense of words? This workshop introduces Natural Language Processing in Python and sheds light on how computers interpret our language. Attendees are introduced to NLTK and Gensim that help them tokenise, process and represent textual data. We will see how data is distilled into different linguistic features that power Machine Learning applications like text classifier, sentiment analyzer and topic modeler.

Thursday, April 30

  • Deserted Island DevOps @ 10:00 AM EDT (FREE!)
    Join us April 30th for a free one-day event celebrating DevOps and Animal Crossing, streaming live on twitch.tv!
  • QuarantineCon Virtual Career Fair @ 6:00 PM to 9:30 PM EDT (FREE!)
    Spring has come into full swing, and so has fresh conversation surrounding career and professional development. As a continued response to remain safe through social distancing, shelter in place, quarantine, etc., and still build community, Jopwell and QuarantineCon are partnering to bring together industry experts with a virtual career fair! We are creating space to discuss several career-centred topics, including “Navigating Today’s Job Market” and “Cold Outreach 101.” Featured speakers include Carla Harris, Vice Chairman at Morgan Stanley, and Nadia Abouzaid, Head of Recruitment at Jopwell.
  • Activate Conference — Accessibility talk: How to be an A11y @ 7:00 PM EDT (FREE!)
    Featuring Aisha Blake, software engineer at Gatsby.js

Friday, May 1

  • Byteconf React 2020 @ Friday through Sunday (FREE!)
    Byteconf React is a 100% free single-day conference with the best React speakers and teachers in the world. Conferences are great, but flights, hotels, and tickets are expensive, so not everyone can go. Byteconf is streamed on YouTube, for free, so anyone and everyone can attend. RSVP to receive your free ticket, and we’ll enter you in our free monthly giveaway, with a chance to receive a free 45+ hour React course from Udemy. See you on May 1st!

 

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Greetings, Tampa Bay techies, entrepreneurs, and nerds! Welcome to week 5 of the Florida general stay-at-home order! I hope you’re managing and even thriving. While it appears that event organizers are adjusting to our new, temporary version of “normal” with online events, this coming weekend’s looking a little quiet. Keep an eye on this post; I update it when I hear about new events, it’s always changing. Stay safe, stay connected, and #MakeItTampaBay!

To stay on top of the latest Tampa Bay events as well as all sorts of interesting tech articles, be sure to check out Global Nerdy (globalnerdy.com) regularly!

Monday, April 27

Tuesday, April 28

Wednesday, April 29

Thursday, April 30

Friday, May 1

Saturday, May 2

No Tampa Bay area tech events have been announced for this date…yet!

Sunday, May 3

No Tampa Bay area tech events have been announced for this date…yet!

Do you have any events or announcements that you’d like to see on this list?

Let me know at joey@joeydevilla.com!

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Product owners and managers: Pendo’s ProductCraft conference on May 7, 2020 is virtual and free! Here’s their summary of the event:

The ProductCraft Virtual Conference offers the same high-quality session content as our in-person events, just via a 100% online format. Our speakers are product leaders at some of tech’s fastest-growing companies, and will be sharing their best practices, unique perspectives, and experiences of what it means to work in product.

And as always, we’ll be putting a different spin on the traditional product conference format, with plenty of opportunities for networking.

Here’s the agenda:

Time Session
11:00 am – 11:35 am EDT BEST PRACTICES FOR BUILDING PRODUCTS WITH A FULLY REMOTE TEAM
Holly Kennedy, VP of Design, 15Five, Dianne Frommelt, VP of Product, 15Five
11:35 am – 12:05 pm EDT DO THIS, NOT THAT: GUIDING DECISION-MAKING WITH PRODUCT PRINCIPLES
Jeetu Patel, Chief Product Officer, Box
12:05 pm – 12:35 pm EDT YOUR PRODUCT IS NEVER GOING TO BE READY
Karen Rubin, Chief Revenue Officer, Owl Labs
12:35 pm – 1:05 am EDT INNOVATION SYSTEMS FOR COMPETITIVE PRODUCTS
Brian Crofts, Chief Product Officer, Pendo
1:05 pm – 1:35 pm EDT BUILDING RELEVANT AND IMPACTFUL INNOVATIONS AMID UNCERTAIN TIMES
Shravan Goli, CPO and Head of Consumer Business, Coursera

The stream starts on Thursday, May 7 at 11:00 AM EDT. If you can’t catch it live because you’re one of the fortunate ones still with a job, it’s being recorded and will be sent to you after the event.

 

 

 

 

 

 

 

 

 

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I was the guest author of this week’s Startup Digest Tampa Bay newsletter, which was sent out on Monday. I was free to write about any topic that I thought subscribers to the newsletter might find interesting or useful, and I chose to write about reasons startup founders and members have to be optimistic even though we’re in a global pandemic and corresponding economic crisis.

I’d like to thank Techstars Tampa Bay, Startup Digest Tampa Bay curator Murewa Olubela, and Startup Digest Tampa Bay curator manager Alex Abell for inviting me to write for the newsletter!

Here’s “director’s cut” of my guest editorial, which has some additional information and ideas…

Reasons for startups to be optimistic

Rather than bore you with a long preamble, I’ll give your week a good start by getting straight to my point: The global pandemic comes with a global business crisis, and crises are where startups shine. Here are three reasons — each with a litany of sub-reasons — why startup founders and team members should be at least a little optimistic about the current situation.

1. Sometimes you don’t know that you’re living in a golden age

The first reason to be optimistic is that recessions have been known to hide golden ages. As far as the last recession is concerned, Thomas Friedman has a theory: That 2007 was “one of the single greatest technological inflection points since Gutenberg…and we all completely missed it.”

He made his point very compelling by listing what happened then in What the hell happened in 2007?, the second chapter of his 2016 book, Thank You for Being Late. I’ve compiled his list in the table below, expanded the scope to cover the years 2006 through 2008, and threw in some additional notes.

Looking at that time through the lens of the leaps in technology shown below, it seems like a golden age:

The leap Notes
Airbnb

In October 2007, as a way to offset the high cost of rent in San Francisco, roommates Brian Chesky and Joe Gebbia came up with the idea of putting an air mattress in their living room and turning it into a bed and breakfast. They called their venture AirBedandBreakfast.com, which later got shortened to its current name.

This marks the start of the modern web- and app-driven gig economy.

Android

The first version of Android as we know it was announced on September 23, 2008 on the HTC Dream (also sold as the T-Mobile G1).

Originally started in 2003 and bought by Google in 2005, Android was at first a mobile operating system in the same spirit as Symbian or more importantly, Windows Mobile — Google was worried about competition from Microsoft. The original spec was for a more BlackBerry-like device with a keyboard, and did not account for a touchscreen. This all changed after the iPhone keynote.

App Store

Apple’s App Store launched on July 10, 2008 with an initial 500 apps. At the time of writing (March 2020), there should be close to 2 million.

In case you don’t remember, Steve Jobs’ original plan was to not allow third-party developers to create native apps for the iPhone. Developers were directed to create web apps. The backlash prompted Apple to allow developers to create apps, and in March 2008, the first iPhone SDK was released.

Bitcoin

The person (or persons) going by the name “Satoshi Nakamoto” started working on the Bitcoin project in 2007.

It would eventually lead to cryptocurrency mania, crypto bros, HODL and other additions to the lexicon, one of the best Last Week Tonight news pieces, and give the Winklevoss twins their second shot at technology stardom after their failed first attempt with a guy named Mark Zuckerberg.

Chrome

By 2008, the browser wars were long done, and Internet Explorer owned the market. Then, on September 2, Google released Chrome, announcing it with a comic illustrated by Scott “Understanding Comics” McCloud, and starting the Second Browser War.

When Chrome was launched, Internet Explorer had about 70% of the browser market. In less than 5 years, Chrome would overtake IE.

Data: bandwidth costs and speed In 2007, bandwidth costs dropped dramatically, while transmission speeds grew in the opposite direction.
Dell returns After stepping down from the position of CEO in 2004 (but staying on as Chairman of the Board), Michael Dell returned to the role on January 31, 2007 at the board’s request.
DNA sequencing costs drop dramatically The end of the year 2007 marks the first time that the cost of genome sequencing dropped dramatically — from the order of tens of millions to single-digit millions. Today, that cost is about $1,000.
DVD formats: Blu-Ray and HD-DVD In 2008, two high-definition optical disc formats were announced. You probably know which one won.
Facebook In September 2006, Facebook expanded beyond universities and became available to anyone over 13 with an email address, making it available to the general public and forever altering its course, along with the course of history.
Energy technologies: Fracking and solar Growth in these two industries helped turn the US into a serious net energy provider, which would help drive the tech boom of the 2010s.
GitHub Originally founded as Logical Awesome in February 2008, GitHub’s website launched that April. It would grow to become an indispensable software development tool, and a key part of many developer resumes (mine included). It would first displace SourceForge, which used to be the place to go for open source code, and eventually become part of Microsoft’s apparent change of heart about open source when they purchased the company in 2018.
Hadoop

In 2006, developer Doug Cutting of Apache’s Nutch project, took used GFS (Google File System, written up by Google in 2003) and the MapReduce algorithm (written up by Google in 2004) and combined it with the dataset tech from Nutch to create the Hadoop project. He gave his project the name that his son gave to his yellow toy elephant, hence the logo.

By enabling applications and data to be run and stored on clusters of commodity hardware, Hadoop played a key role in creating today’s cloud computing world.

Intel introduces non-silicon materials into its chips January 2007: Intel’s PR department called it “the biggest change to computer chips in 40 years,” and they may have had a point. The new materials that they introduced into the chip-making process allowed for smaller, faster circuits, which in turn led to smaller and faster chips, which are needed for mobile and IoT technologies.
Internet crosses a billion users This one’s a little earlier than our timeframe, but I’m including it because it helps set the stage for all the other innovations. At some point in 2005, the internet crossed the billion-user line, a key milestone in its reach and other effects, such as the Long Tail.
iPhone

On January 9, 2007, Steve Jobs said the following at this keynote: “Today, we’re introducing three revolutionary new products…an iPod, a phone, and an internet communicator…Are you getting it? These are not three separate devices. This is one device!”

The iPhone has changed everyone’s lives, including mine. Thanks to this device, I landed my (current until recently) job, and right now, I’m working on revising this book.

iTunes sells its billionth song On February 22, 2006, Alex Ostrovsky from West Bloomfield, Michigan purchased ColdPlay’s Speed of Sound on iTunes, and it turned out to be the billionth song purchased on that platform. This milestone proves to the music industry that it was possible to actually sell music online, forever changing an industry that had been thrashing since the Napster era.
Kindle

Before tablets or large smartphone came Amazon’s Kindle e-reader, which came out on November 19, 2007. It was dubbed “the iPod of reading” at the time.

You might not remember this, but the first version didn’t have a touch-sensitive screen. Instead, it had a full-size keyboard below its screen, in a manner similar to phones of that era.

Macs switch to Intel

The first Intel-based Macs were announced on January 10, 2006: The 15″ MacBook Pro and iMac Core Duo. Both were based on the Intel Core Duo.

Motorola’s consistent failure to produce chips with the kind of performance that Apple needed on schedule caused Apple to enact their secret “Plan B”: switch to Intel-based chips. At the 2005 WWDC, Steve Jobs revealed that every version of Mac OS X had been secretly developed and compiled for both Motorola and Intel processors — just in case.

We may soon see another such transition: from Intel to Apple’s own A-series chips.

Netflix In 2007, Netflix — then a company that mailed rental DVDs to you — started its streaming service. This would eventually give rise to binge-watching as well as one of my favorite technological innovations: Netflix and chill (and yes, there is a Wikipedia entry for it!), as well as Tiger King, which is keeping us entertained as we stay home.
Python 3

The release of Python 3 — a.k.a. Python 3000 — in December 2008 was the beginning of the Second Beginning! While Python had been eclipsed by Ruby in the 2000s thanks to Rails and the rise of MVC web frameworks and the supermodel developer, it made its comeback in the 2010s as the language of choice for data science and machine learning thanks to a plethora of libraries (NumPy, SciPy, Pandas) and support applications (including Jupyter Notebooks).

I will always have an affection for Python. I cut my web development teeth in 1999 helping build Givex.com’s site in Python and PostgreSQL. I learned Python by reading O’Reilly’s Learning Python while at Burning Man 1999.

Shopify In 2004, frustrated with existing ecommerce platforms, programmer Tobias Lütke built his own platform to sell snowboards online. He and his partners realize that they should be selling ecommerce services instead, and in June 2006, launch Shopify.
Spotify The streaming service was founded in April 2006, launched in October 2008, and along with Apple and Amazon, changed the music industry.
Surface (as in Microsoft’s big-ass table computer)

Announced on May 29, 2007, the original Surface was a large coffee table-sized multitouch-sensitive computer aimed at commercial customers who wanted to provide next generation kiosk computer entertainment, information, or services to the public.

Do you remember SarcasticGamer’s parody video of the Surface?

Switches 2007 was the year that networking switches jumped in speed and capacity dramatically, helping to pave the way for the modern internet.
Twitter

In 2006, Twittr (it had no e then, which was the style at the time, thanks to Flickr) was formed. From then, it had a wild ride, including South by Southwest 2007, when its attendees — influential techies — used it as a means of catching up and finding each other at the conference. @replies appeared in May 2007, followers were added that July, hashtag support in September, and trending topics came a year later.

Twitter also got featured on an episode of CSI in November 2007, when it was used to solve a case.

VMWare After performing poorly financially, the husband and wife cofounders of VMWare — Diane Greene, president and CEO, and Mendel Rosenbaum, Chief Scientist — left. Greene was fired by the board in July, and Rosenbaum resigned two months later. VMWare would go on to experience record growth, and its Hypervisors would become a key part of making cloud computing what it is today.
Watson IBM’s Watson underwent initial testing in 2006, when Watson was given 500 clues from prior Jeopardy! programs. Wikipedia will explain the rest:

While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues, Watson’s first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. By 2008, the developers had advanced Watson such that it could compete with Jeopardy! champions.

Wii The Wii was released in December 2006, marking Nintendo’s comeback in a time when the console market belonged solely to the PlayStation and Xbox.
XO computer You probably know this device better as the “One Laptop Per Child” computer — the laptop that was going to change the world, but didn’t quite do that. Still, its form factor lives on in today’s Chromebooks, which are powered by Chrome (which also debuted during this time), and the concept of open source hardware continues today in the form of Arduino and Raspberry Pi.
YouTube

YouTube was purchased by Google in October 2006. In 2007, it exploded in popularity, consuming as much bandwidth as the entire internet did 7 years before. In the summer and fall of 2007, CNN and YouTube produced televised presidential debates, where Democratic and Republican US presidential hopefuls answered YouTube viewer questions.

You probably winced at this infamous YouTube video, which was posted on August 24, 2007: Miss Teen USA 2007 – South Carolina answers a question, which has amassed almost 70 million views to date.

How did most of us miss all this? Friedman says that it’s because our collective attention was directed toward the credit crunch of 2008, which he calls “the deepest recession since the crash of 1929.”

Back then, everybody compared the financial collapse of that time to the stock market crash of ’29. Now that we’re in the middle of a pandemic, 2008 has become the new benchmark for economic catastrophe. As founders, entrepreneurs, and technologists, there’s a good chance that you’re already asking this question: Is there a chance that the current situation is also hiding a golden age for technology and startups?

In case you think that the golden age of 2006 – 2008 was just an outlier, here are a few examples from previous crises:

  • Thomas Edison founded the company that would eventually become General Electric during an economic slump brought about by the Baring Crisis and “the world’s first bailout”, whose effects were felt worldwide, and it took an international consortium and a number of Rothschilds to prevent an economic catastrophe. In the 1960s, one of the few companies in the 1960s making computers and operating systems whose influence extends to this very day (GE, along with Bell Labs and MIT, made Multics, which would inspire the creation of Unix).
  • The Tabulating Machine Company, which would evolve into IBM was born during The Panic of 1896, a sequel to the Panic of 1893. This was a time when the unemployment rate climbed as high at 15%. IBM is still a big player in mainframes, which are in the news again, thanks in part to the pandemic. 
  • Apple and Microsoft came about in the mid-1970s, just after the 1973 – 1975 recession, the oil crisis of 1973, the Nixon shock, and the end of the gold standard and Bretton Woods, and the start of the U.S. dollar being a fiat currency (a term that you’re probably familiar with with you even just dabble in cryptocurrencies). You’re probably quite familiar with what these companies went on to do.

2. Startups that last get founded during downturns

Putting aside the chance that I might be a victim of survivorship bias, there are a number of reasons why a recession or economic downturn is the optimal time to create or join a startup:

  • New situations create new needs: Just look at Zoom’s fortunes right now. What other needs has the “New Normal” created, in both the short and long term?
  • Available brainpower: With an economic downturn comes increased unemployment, which means that the talent you need for your startup is more likely to be available (this pool of people includes myself). This brainpower can be key to a startup’s success.
  • Available startups: If you’re not looking to be a founder and are looking for a place to work, you may find a number of founders looking to create their own company, often because they can’t find employment themselves.
  • Recession pricing: The price of goods and services tends to drop during downturns, which is an advantage to a company that’s trying to operate “lean and mean.” There may also be some savings opportunities in other developments, including the drop in interest rates and other economic stimuli, as well as companies selling off assets that startups may find useful. You may even find it easier to get coverage, as the media will be looking for some “good news” stories to tell.
  • Chaos: Your likely competition — large, established players — are probably in disarray or too focused on survival or reorganizing to take notice of you. A business that’s lean and nimble is better-positioned to navigate the changes that a downturn brings about, and better able to take advantage of the eventual turn-around.
  • Pressure makes diamonds: There’s no comfort zone in a downturn! A company that starts during one has a culture of resilience and grit baked into its DNA, and the lessons that risk and non-terminal failure during difficult times teaches makes for a powerful team. The “war stories” that come about from shared challenges also make for a loyal team.

A downturn doesn’t guarantee success for a startup, but it’s a crucible that can strengthen one.

3. We’re in the middle of a number of natural experiments

The final item in this least of reasons to be optimistic is that we’re in a set of unprecedented natural experiments — that is, we’re witnessing “what if” scenarios that are no longer hypothetical. In these scenarios, there are object lessons, opportunities, and problems that the right startup idea could solve or ameliorate.

  • Internet bandwidth: One of the major arguments that telcos have given us for bandwidth caps was that without this sort of control (they never mention the associated cash-grab), the internet would become so congested as to be unusable. With the pandemic, telcos have lifted bandwidth caps, and the internet still works, even with the additional usage from being home-bound as well as Tiger King.
  • Remote work: In recent years, Yahoo! and IBM famously ended their remote work policies, which led other, smaller organizations to consider doing the same. With “safer at home” measures, we’re all global participants in a remote work experiment, and to the surprise of doubters, it seems to be working. Remote work is likely to be a fixture of office life even after this crisis, and this change will create a need for new and expanded services and technologies.
  • Remote school: Just as working people are being subjected to a natural experiment, so are students and teachers, who are being thrown into the deep end with distance-learning tools and technologies. There are a number of challenges to overcome, such as  usability, adjusting teaching and learning styles, bringing it to students who can’t afford internet access or the right technology at home, to the disruption that this brings to students’ and teachers’ lives, and to the school curriculum in general.
  • Unemployment: I’m in this category, as one of at least 10 million people in the U.S. who’ve suddenly found themselves without work. What happens when this many people are jobless, in a world with ubiquitous connectivity and computing? Remember, the smartphone as we know it was in the hands of a small number of people in 2008, while 4 out of 5 adults in the U.S. has a smartphone today. How can a startup help them get back to work?
  • Additional online learning: You may have seen the advice in news stories or online: If you’ve been laid off, this is the perfect time to take an online course and “upskill.” With record numbers of people applying for unemployment assistance, we’re seeing a strong uptick in online course enrollments.
  • Business and government systems under strain: While the internet seems to be handling the increase in use, other systems have been put under strain by the pandemic. The hospitality industry has largely been shut down. Supply chains are being stressed. Our pandemic response infrastructure was already gutted before the pandemic struck. Our governments are unprepared in all sorts of ways, from a piecemeal response to the pandemic, to aging, COBOL-powered systems unprepared (and in Florida’s case, unprepared by design) to process the massive influx of requests for unemployment assistance. This sounds like a job for a startup!
  • Healthcare: The United States remains the only industrialized nation without universal healthcare. To my Canadian-raised mind, this is baffling; to many Americans, universal healthcare is an unaffordable luxury. The U.S. government’s ability to “magic up” trillions of dollars to stimulate the economy (or at least Ruth’s Chris Steak House) on incredibly short notice proves that if the political will existed, it could choose to bankroll universal healthcare. With 1 in 4 Americans expected to be unemployed and healthcare insurance generally being tied to employment, universal healthcare is no longer as “unthinkable” an idea as it once was.
  • 3D printing’s first mass test: We’ve seen 3D printing useful in one-off situations (including a time when they needed a specific kind of wrench on the International Space Station), but with volunteers creating large numbers of face shields, masks, and even ventilator parts and adapters, this is 3D printing’s first at-scale test. The lessons from this effort have yet to be learned, and what we learn could launch printing into its next phase.
  • Media and communications: This is the first worldwide crisis where publishers, from the largest media empire to individual vloggers, have become much relied-on sources of both information and misinformation. We’re not done seeing the full extent of their effects yet.
  • Social systems put to the test: The disruption of normal life, including staying at home to social distancing, has resulted in widely different responses, from science-based to conspiracy theory-based. The major social media players have put in some measures to fight the spread of accidentally or deliberately incorrect information. I have no doubt that even nation-states are playing the misinformation game; after all, the saying is “never let a good crisis go to waste.”
  • New political movements: The economic downturn of 2008 left a lot of people dislocated and in dire situations from which they still haven’t recovered, giving birth to a new populism, a willingness to follow brutish, xenophobic, and nationalistic leaders, and movements like Brexit and MAGA. What will this new situation — one brought about at least partially by the movements that arose after 2008 — bring?
  • Emerging cultures of control: I’m going to end this list on a slightly darker note, in spite of my general optimism. We’re already seeing signs of an emerging hygiene culture and an awareness of the importance of hand-washing, which is good. Perhaps there are startup opportunities that might come about from people being more aware of the power of microorganisms and viruses (viruses are technically “not alive”, and exist in their own category). More worrisome are other cultures of control, namely those of surveillance and authoritarianism, which are also rearing their heads during this crisis. Let’s take care so that the things we create don’t turn the world into another Black Mirror episode.

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