Developer Insights

5 Mistakes to Avoid When Developing IoT Applications

It seems like just about everything is connected to the internet these days — or at least, on its way to being connected in some way. While adding a connected component to just about anything can make it more useful to the average consumer and help us live our lives better, when the IoT application isn’t developed appropriately, it can lead to more frustration and annoyance than usefulness.

With that in mind, it’s important to take your time when developing IoT applications and make sure you don’t overlook certain aspects that make the application work more efficiently. Making these simple mistakes can take your application from a great idea to a dud in no time at all, so be sure to avoid them.

Mistake #1: Not Using Existing Frameworks

When you develop an application for the IoT, there are typically four levels to consider: the device itself; the “ingestion layer,” or the infrastructure and software that collects the data and makes sense of it; the analytics layer, which takes the organized data and processes it; and finally, the end-user level, which is usually the actual app that the user interacts with.

In the case of a coffee maker, for example, the coffee maker is the thing itself, and is equipped with a microprocessor of some sort that collects the information that it’s time to make the coffee, adjust the amount of coffee, etc. The analytics layer takes that information and instructs the machine to conduct the task, adjusting as necessary, while all the user sees is the app that they can program to make one cup of French Roast at 6 a.m.

What does all of this have to do with using existing frameworks? Usually, developers only need to work within the analytics and end-user levels of the application development. There are development tools available that have already established the framework for the other levels, and by using them, you don’t need to reinvent the wheel. This not only makes it easier to develop new applications, but it also speeds up the time to market for new products.

Mistake #2: Forgetting About Scalability

How many people will be using your application on a typical day? While you can make predictions, you need to always be prepared for sudden load increases and fluctuations. Otherwise, you run the risk of interrupted or slow connectivity, which will reduce user satisfaction with your device. Therefore, when developing your application, you need to think about scalability, and be prepared for load switches and develop software that can be updated when necessary to accommodate the load without affecting user experience.

Mistake #3: Not Making Security a Priority

The security of the IoT is a hot topic these days, with many experts noting that the IoT is a prime target for hackers. While hacking, say, household appliances, hasn’t been a major issue yet, the potential is definitely there. For this reason, when developing your IoT application, you need to pay close attention to limiting the attack surface, keeping the app safe from code injection attacks, safeguarding any collected sensitive information, and planning for secure updates. Keeping security at the forefront of your application development ensures that your device won’t be the one that leads to a major attack or breach.

Mistake #4: Not Planning for the Future

There’s no denying that technology moves fast, and the potential for today’s technology to be obsolete within a few years is high. However, while you may not have a crystal ball for everything that is going to happen in the next five to ten years, there are some developments on the horizon that you want to pay attention to and plan for, since many IoT devices are not those that consumers are prepared to upgrade on a regular basis, such as refrigerators. For example, internet addresses are transitioning to IPv6 from IPv4. By planning your application for that change now, you can avoid a costlier update later.

Mistake #5: Not Hosting Appropriately

Finally, consider hosting your application in multiple data centers rather than a single location. This allows for reduced latency in response times, and ensures uninterrupted service in the event of a disaster. Users have little patience for sluggish response times or server errors.

Developing an IoT application for your device can help take it to the next level and increase customer satisfaction and brand loyalty — but only when the application is well developed. As you think about your next IoT project, remember these mistakes and your product won’t be in the “great idea, poor execution” hall of shame.

Developer Insights

What is an Algorithm and How It Shape Our World?

"For someone who doesn't watch many matches, Elihu Feustel makes a million dollars by winning bets simply by using algorithms"

Algorithms have revolutionized our world as we know it.

Today, a lot of things come to us a lot more easily than before. We have moved forward from the age of waiting to hear our favorite song on the FM radio and recording it on a cassette. Now, we visit YouTube to play your favorite song. It miraculously recommends all your favorite songs in the auto playlist.

When you visit IMDB’s website, it suggests the most relevant movies every time. When you Google something, you see the most relevant information on the first page. Have you ever wondered how each website knows exactly what to show you?

Algorithms.

We have advanced so much that we can create a painting that one cannot distinguish from the works of an artist who died in 1669!

We are talking about the new 3D-printed Rembrandt painting. Developers created an algorithm to collect data from portraits painted by Rembrandt. This data was then used to create a new 3D-printed painting, which took over 500 hours of rendering.

What is an algorithm?

“A process or set of rules followed in calculations or other problem-solving operations, especially by a computer”. At the simplest level, it is a procedure for accomplishing a task or solving the problem.

How algorithms shape our world every day

Algorithms are everywhere—sports, computer gaming, finance etc. Let us take a look at a few examples.

Healthcare: Algorithms help in areas where healthcare had no effective treatment. From matching appropriate organs to mining data for matching symptoms. They complement some of the most proficient doctors across the globe.

In 2013, 4500 former footballers filed lawsuits against the NFL for concussion-related injuries. This issue came into the limelight when Hall of Famer, nine-time Pro Bowler, Mike Webster and a few other players were diagnosed with Chronic Traumatic Encephalopathy (CTE). The effects of repeated blows to the head cause CTE. Head injuries in sports like American football

1.6 to 3.8 million sports-related concussions occur annually in the United States. To this day, there are no effective tests/scans to detect this disorder. Sanford Health studied common symptoms of the disorder and developed a concussion-evaluation algorithm, which detects the signs and symptoms of CTE. Now, because of timely treatment and precautions, the effects of concussions are prevented in more players. Until an effective test or scan is developed, algorithms are the answer to detecting CTE in time.

Sports: The sports industry today is the result of what has moved from intuition to statistics. It is a big game of algorithms from the Duckworth-Lewis method in cricket to the ranking players by using the Elo rating in the game of chess.

Algorithms also dominate the world of sports betting. So when you see weird numbers like 12/25 for ManU and 17/25 for Arsenal, you can rest assured that they are correct. The complex mathematical concept of the Poisson distribution equation is the foundation to the sports betting. This concept is used to generate numbers in sports betting.

Let’s take the example of Elihu Feustel, who makes almost a million dollars a day by betting on tennis. You would think that he would recognize Serena Williams if he passed by her on the street. However, this isn’t the case, he neither recognizes any of the famous tennis players nor does he know their playing styles etc. So, just how does he know who to bet on? He relies on a big data model that the live betting market uses. This algorithm collects data from existing (almost 260,000) matches. This data is then manipulated to allow users to determine which player/match they should bet on.

It seems like it’s all out of this world, right?

Gaming: It's amazing how videos and computer games have evolved over the last 2 decades. We started with DOS games, Super Mario, Pac-Man, and went on to highly-advanced games like Need for Speed, Counter-Strike, and DotA.

The games are now getting more realistic, with life-like surroundings, smarter and scarier anti-heroes. There is an increased advancement in Augmented Reality (AR) and Virtual Reality (VR) technologies. We are now developing techniques to bring in games that require player involvement at an epidemic rate. This has led to the development of algorithms that are highly complex in nature to create complex games.

Finance: The financial world has not remained untouched by algorithms. Like the Rothschild

'Algorithms' - A word used by programmers when they do not want to explain what they did.

pigeon story of 1815

Helped Rothschild build a financial empire that’s going strong even today, the prediction in the field of finance needs to be quick and accurate. Black Box trading uses advanced and complex mathematical models to figure trading strategies for the most ideal returns. In stock markets, you have to make quick decisions. By 2011, algorithms governed 70% of the US stock market.

There is no doubt that irrespective of the field that you choose, algorithms have a strong influence on our lives and they are changing our world.

Soon there is a possibility that algorithms will take over most of the tasks done by human beings. This could lead to the creation of mundane jobs, which algorithms cannot do, such as stocking bottles, collecting blood samples, and other work that requires physical effort.

Until then, put your algorithm skills to test with these programming challenges.

Developer Insights

Data visualization packages in R-Part I

A good understanding of data is one of the key essentials to designing effective Machine Learning (ML) algorithms. Realizing the structure and properties of the data that you are working with is crucial in devising new methods that can solve your problem. Visualizing data includes the following:

Cleaning up the data
Structuring and arranging it
Plotting it to understand the granularity

R, one of the few widely-used programming languages for ML, has many data-visualization libraries.

In this article, we will explore two of the most commonly used packages in R for analyzing data—dplyr and tidyr.

Using dplyr and tidyr

dplyr and tidyr, created by Hadley Wickham and maintained by the RStudio team, offer a powerful set of tools for data manipulation. One of their best features is the pipeline operator %>%, which allows chaining multiple operations together in a clean and readable format.

tidyr Functions

`gather()`

Transforms wide-format data into long-format by collecting columns into key-value pairs.

gather(data, key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)
data %>% gather(key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)

key, value	Names of the columns to be created in output
`…`	Columns to gather. Use `-` to exclude specific columns
na.rm	If TRUE, rows with NA values will be discarded
convert	Convert key values to appropriate types
factor_key	Whether to treat key as a factor or character

`spread()`

Opposite of gather(); spreads key-value pairs into wide format.

spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)

fill	Value used to fill in missing combinations
drop	Whether to drop unused factor levels
sep	String used to separate column names if not NULL

`separate()` & `unite()`

separate() splits a column into multiple columns based on a separator. unite() merges multiple columns into one.

dplyr Functions

`select()`

Choose specific columns based on name, pattern, or position.

`filter()`, `slice()`, `distinct()`, `sample_n()`, `sample_frac()`

Filter rows, remove duplicates, and take samples from the data.

`group_by()` & `summarise()`

group_by() creates groups, while summarise() computes summary statistics for each group.

summarise(df, avg = mean(column_name))

`mutate()`

Add or transform columns using expressions like:

mutate(new_col = col1 / col2)

Joins

Supports all major joins: left_join(), right_join(), inner_join(), full_join(), semi_join(), and anti_join().

`arrange()`

Sort data ascending or descending using desc().

Data Visualization with ggplot2

Bar Chart Example


ggplot(data = gather(stocks, stock, price, -time) %>%
       group_by(time) %>%
       summarise(avg = mean(price)),
       aes(x = time, y = avg, fill = time)) +
       geom_bar(stat = "identity")

Scatter Plot Example


qplot(time, avg,
      data = gather(stocks, stock, price, -time) %>%
             group_by(time) %>%
             summarise(avg = mean(price)),
      colour = 'red',
      main = "Avg change of stock price for each month",
      xlab = "month",
      ylab = "avg price")

Regression models help uncover hidden trends. Libraries like dplyr and tidyr don’t just clean your data—they boost your data intuition and enable better decisions.

In the next article, we'll explore more data-visualization libraries.

Developer Insights

How Artificial Intelligence is rapidly changing everything around you!

We live in an interesting era in the history of mankind. You will be surprised to know that Apollo 11, the computer that put Man on the Moon in 1969, whose assembly language code was recently published on Github, operated on 64KB memory whereas today’s kids have 64GB iPhones to click duckface selfies to upload on Instagram and play PokeMon Go, a viral game that marginally broke all time daily active users’ record in a week! Technically, 1 Million times more memory and 100 million times computational power at your disposal. After all, Moore’s law is nothing less than a super genius prediction.

Thought provoking extrapolation of the same Moore’s law, that says that the number of transistors in an integrated circuit and hence the computational power doubles itself every 2 years resulting into exponential technological growth, is applicable to the most evolving field of technology, Artificial Intelligence. For me, as the kid of Gen-Y, it is mind-blowing to think how the first movie ever was produced in the year my great grandfather was born, the first computer was built when my grandfather was still in his teens, the first Star Wars movie was released when my father learned fishing and how only last month, I blew my mind watching the first movie named Sunspring written by an AI program that christened itself as Benjamin. Not going to be long before I know, my kid wakes me up in the middle of the night someday and tells me, ‘Hey dad! One super intelligent robot called Trump has waged a war against humanity to destroy the entire human race.’ And WHAM! The most dreaded Apocalypse is a reality. Who knows?

If you are still optimistic and think that it is just a trippy thought experiment, here’s the generalized version of Moore’s law, depicted by Kurzweil as The Law of Accelerating Returns.

Kurzweil provides an interesting way of looking at the advancement of Artificial in terms of CPS achieved per $1000. The exponential nature of this curve can be visualized with respect to human intelligence and how quickly we are advancing towards the human level intelligence.

Not a long ago, we were struggling to artificially replicate the brain, read intelligence, of an insect and just so overwhelmingly, we are not too far from artificially achieving the intelligence of the most superior specie on the planet earth. What lies beyond that is a mystery for a layman but we indeed are living in the golden era of the exponential curve of technological progress. The singularity is near!

The journey so far hasn’t been easy. Ponder over this deep. Starting from the Big bang, to the birth of life on the earth, to development of human civilizations, to the million science experiments that went wrong along the progress including the first computer and the first lines of code, followed by a gazillion more, everything has contributed in making today’s Machine learn with humongous data to take intelligent decisions of its own, to probably build their own society tomorrow.

The Need of Artificial Intelligence

Have you ever been so lazy to be stalled on your bed with packets of tortilla chips and the latest episodes of Game of Thrones, that you just fantasized a remote control with multiple buttons to open the door or turn the fan on or do all that boring stuff? Oh wait, that still requires you to hold the remote and press the buttons, right? Gee, why don’t we have a robot that would just read our mind and do everything from household stuff to attending the unwanted guests without asking anything in return. Firstly, such robot will have to be super intelligent. Not only will it have to be efficient to perform routine tasks, but also understand your emotions viz-a-viz, mood swings and your behavioral pattern by observing you every minute and processing the data of your actions and emotions. Apart from the hard-coded seemingly basic set of functions, which in itself is a mammoth task, the machine will have to progressively learn by observations in order to perform as good as a smart human to serve you.

While a lot of this has been significantly achieved, it is still a very hard task for a machine to detect, segregate and arrange scented towels, hairdryers, Nutella box or contact lenses from a pile of junk than computing the complicated Euler product for a Riemann Zeta function. Machines can be entirely clueless and result into wrong outputs for what seems obvious that humans can solve in just a second’s glance.

You know how they say, “Don’t reinvent the wheel”, researchers have recently recreated a complex Quantum Physics experiment using AI just in an hour, that had won a Nobel prize in 2001 followed by years of determination and hard work by renowned Physicists like Einstein and Bose. We need AI to take care of trivial life problems so that we can invest our time and build a better AI to solve more important problems, like treating cancer or fighting against global warming. Fascinatingly enough, AI is actually everywhere; but more often than not we fail to see it. Because as John McCarthy, rightly says: “As soon as it works, no one calls it AI anymore.”

How AI is Everywhere Facilitated by Machine Learning

Right from a smartphone App that suggests you the nearby fast food outlet you might be interested in, to Facebook’s photo tagging Algorithm that detects your face with or without a beard, to Google’s self driving car, AI is everywhere and is deeply embedded in our lives without us realizing it. Our perceptions of AI are biased by Sci-Fi movies with evil machines trying to take over the galaxy. Intelligent machines designed to learn in order to become more intelligent by themselves to achieve Superintelligence is not hard to imagine. Technically, this would lie on the steepest slope of the exponential curve of intelligence versus time that we discussed before.

It is a beautiful realization that the roots of Artificial intelligence of a self driving car taking decisions that involve lives on the road lie within countless trivial and complicated Machine Learning algorithms that actually start with a few lines of code on your computer. My friend who had no background of computer science whatsoever before college, started learning programming in his sophomore year and took a long time before he could write fine algorithms in C++. He then started reading about Image processing, right from how a black and white image can be represented with a metric of numbers and went on writing a simple algorithm to detect a stable human hand. Soon with his growing interest, the human hand was replaced with colonoscopy images to robust deep learning algorithms that could detect cancer with image processing. His research paper got accepted in an international conference and has many other industrial applications.

Machine Learning and the Power of Big Data

With unmatched computational power machines can process and be trained to make decisions using big data and predictive modelling. Imagine a person having superpowers who can predict future of everything, the world would fall at their feet. In a more logical sense, if a machine can process humongous amount of historical and real time data with prediction models, and learns over time to get better and better, imagine, the power of big data is nothing less than magical.

Several industries vastly use Machine Learning to take data driven decisions and make life smarter with the power of data accumulated over the years. Healthcare has already been positively impacted with the tremendous amount of work in this field that has contributed in saving thousands of lives. From Governance to Economy to Healthcare, name a domain and you have smart multi-variable regression models backed by big data, performing predictive analysis. Super intelligent machines can now decide your fate to survive in a Multi-Billion dollar stock market. In a way, AI has started affecting our lives tangibly even without us realizing it, and if you look back, it really happened in the blink of an eye.

Whether we live to see the empire of Artificial Superintelligence that surpasses our brain and the human race ends up being the “biological boot loader for digital Superintelligence” OR we produce extraordinary artificial intelligence to look beyond galaxies and travel to future, only time will tell. But one thing is for sure, whatever happens, at the heart of it lie several complicated Algorithms that actually started with a few lines of code.

Developer Insights

Machine Learning through Open Source: Podcast with Mr. Prajod S Vettiyattil, Enterprise Architect, Wipro

I just saw that beautiful dress that I was ogling at yesterday. And guess what, I saw it on a technical blog I was reading. How does the blog know my preference? Well, the answer is machine learning. The ability of a computer or a device to detect patterns and behave accordingly without being explicitly programmed to do so is machine learning. This term has literally taken the market by storm recently and its applications are vast, interesting and worth exploring.

While everyone in the field is either busy exploring machine learning or building machine learning applications, a lot still remains to be explored and unveiled. To take a further insight into the vast field and to also know how open source frameworks are used for building machine learning applications, we invited Mr. Prajod S. Vettiyattil from Wipro to HackerEarth office to speak to us at a podcast. The excerpts from the podcast can be accessed below:

The core driving principle for machine learning and what makes it a buzz word:

What has made machine learning become so mainstream in the last 5 years?

What is a good tech stack for machine learning?

Real world problems that are being tackled by ML:

What are the complex and illusive problems that ML may solve in future?

Can machines behave against our welfare and what are the checks being placed to avoid that?

Despite of the existence of big data, why is there a lack of tangible data to build ML systems?

Would large ML systems be concentrated with few big organizations because of data availability?

What pitfalls should be avoided while building ML systems?

Initiatives that Wipro has taken to promote open source generally and with respect to ML:

Tackling challenges when changes are to be made to open source framework while building systems:

Simple ways to start with machine learning and how to develop a career in it:

Developer Insights

IndiaHacks 2016 Offline Conference is here!

“While learning equips you, and practice improves your skills, competition helps you prove your worth”

We at HackerEarth strongly believe in the power of them all. We understand the value associated with learning, practicing and competing. With the same goals, we started our flagship event ‘IndiaHacks’ on January 8th, 2016.

IndiaHacks soon received a lot of enthusiasm and emerged as the largest conglomeration of developers. More than 1 lakh developers across the globe registered for IndiaHacks. It has been an amazing journey so far with tremendous zeal, tough competition, and global participation. The first phase is coming to an end and we are delighted with how the event has unfolded.

As much as we believe in competition, we also believe in preparing the participants and rewarding them for their efforts and triumphs. The phase 2 of IndiaHacks is an offline conference that will be held on 19th March at Vivanta by Taj, Yeshwanthpur. The conference will have experts from various domains of technology to talk about different topics and share their knowledge with the community. The experts will be a part of events such as tech talks, panel discussions, working sessions, hangouts etc. and will speak on a plethora of topics like data visualization, big data and analytics, artificial intelligence, machine learning, deep learning, fintech, competitive programming, robotics, scientific computing, open source, embedded systems programming etc.

It is a great opportunity to interact with them and gain knowledge in various domains.The conference will end with rewarding the best minds across each track.

The tickets to the conference are up for purchase and 100 early birds will receive a discount of 25% on each ticket.

Top 20 teams from each track will receive free entry to the conference. The competition doesn’t end here, there are other ways to win free tickets too.

Click on the link below to know more about the conference, what it has in store for you and what you can do to be a part of it.

https://www.hackerearth.com/indiahacks-conference/

Authors

Meet our Authors

Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.

Ruehie Jaiya Karri

Kumari Trishya

Hiring Strategies

Forecasting Tech Hiring Trends For 2023 With 6 Experts

2023 is here, and it is time to look ahead. Start planning your tech hiring needs as per your business requirements, revamp your recruiting processes, and come up with creative ways to land that perfect “unicorn candidate”!

Right? Well, jumping in blindly without heeding what this year holds for you can be a mistake. So before you put together your plans, ask yourselves this—What are the most important 2023 recruiting trends in tech hiring that you should be prepared for? What are the predictions that will shape this year?

We went around and posed three important questions to industry experts that were on our minds. And what they had to say certainly gave us some food for thought!

Before we dive in, allow me to introduce you to our expert panel of six, who had so much to say from personal experience!

Meet the Expert Panel

Radoslav Stankov has more than 20 years of experience working in tech. He is currently Head of Engineering at Product Hunt. Enjoys blogging, conference speaking, and solving problems.

Mike “Batman” Cohen is the Founder of Wayne Technologies, a Sourcing-as-a-Service company providing recruitment data and candidate outreach services to enhance the talent acquisition journey.

Pamela Ilieva is the Director of International Recruitment at Shortlister, a platform that connects employers to wellness, benefits, and HR tech vendors.

Brian H. Hough is a Web2 and Web3 software engineer, AWS Community Builder, host of the Tech Stack Playbook™ YouTube channel/podcast, 5-time global hackathon winner, and tech content creator with 10k+ followers.

Steve O'Brien is Senior Vice President, Talent Acquisition at Syneos Health, leading a global team of top recruiters across 30+ countries in 24+ languages, with nearly 20 years of diverse recruitment experience.

Patricia (Sonja Sky) Gatlin is a New York Times featured activist, DEI Specialist, EdTechie, and Founder of Newbies in Tech. With 10+ years in Higher Education and 3+ in Tech, she now works part-time as a Diversity Lead recruiting STEM professionals to teach gifted students.

Overview of the upcoming tech industry landscape in 2024

Continued emphasis on remote work and flexibility: As we move into 2024, the tech industry is expected to continue embracing remote work and flexible schedules. This trend, accelerated by the COVID-19 pandemic, has proven to be more than a temporary shift. Companies are finding that remote work can lead to increased productivity, a broader talent pool, and better work-life balance for employees. As a result, recruiting strategies will likely focus on leveraging remote work capabilities to attract top talent globally.

Rising demand for AI and Machine Learning Skills: Artificial Intelligence (AI) and Machine Learning (ML) continue to be at the forefront of technological advancement. In 2024, these technologies are expected to become even more integrated into various business processes, driving demand for professionals skilled in AI and ML. Companies will likely prioritize candidates with expertise in these areas, and there may be an increased emphasis on upskilling existing employees to meet this demand.

Increased focus on cybersecurity: With the digital transformation of businesses, cybersecurity remains a critical concern. The tech industry in 2024 is anticipated to see a surge in the need for cybersecurity professionals. Companies will be on the lookout for talent capable of protecting against evolving cyber threats and ensuring data privacy.

Growth in cloud computing and edge computing: Cloud computing continues to grow, but there is also an increasing shift towards edge computing – processing data closer to where it is generated. This shift will likely create new job opportunities and skill requirements, influencing recruiting trends in the tech industry.

Sustainable technology and green computing: The global emphasis on sustainability is pushing the tech industry towards green computing and environmentally friendly technologies. In 2024, companies may seek professionals who can contribute to sustainable technology initiatives, adding a new dimension to tech recruiting.

Emphasis on soft skills: While technical skills remain paramount, soft skills like adaptability, communication, and problem-solving are becoming increasingly important. Companies are recognizing the value of these skills in fostering innovation and teamwork, especially in a remote or hybrid work environment.

Diversity, Equity, and Inclusion (DEI): There is an ongoing push towards more diverse and inclusive workplaces. In 2024, tech companies will likely continue to strengthen their DEI initiatives, affecting how they recruit and retain talent.

6 industry experts predict the 2023 recruiting trends

#1 We've seen many important moments in the tech industry this year...

Rado: In my opinion, a lot of those will carry over. I felt this was a preparation year for what was to come...

Mike: I wish I had the crystal ball for this, but I hope that when the market starts picking up again...

Pamela: Quiet quitting has been here way before 2022, and it is here to stay if organizations and companies...

Pamela Ilieva, Director of International Recruitment, Shortlister

Also, read: What Tech Companies Need To Know About Quiet Quitting

Brian: Yes, absolutely. In the 2022 Edelman Trust Barometer report...

Steve: Quiet quitting in the tech space will naturally face pressure as there is a redistribution of tech talent...

Patricia: Quiet quitting has been around for generations—people doing the bare minimum because they are no longer incentivized...

Patricia Gatlin, DEI Specialist and Curator, #blacklinkedin

#2 What is your pro tip for HR professionals/engineering managers...

Rado: Engineering managers should be able to do "more-with-less" in the coming year.

Radoslav Stankov, Head of Engineering, Product Hunt

Mike: Well first, (shameless plug), be in touch with me/Wayne Technologies as a stop-gap for when the time comes.

Mike “Batman” Cohen, Founder of Wayne Technologies

It's in the decrease and increase where companies find the hardest challenges...

Pamela: Remain calm – no need to “add fuel to the fire”!...

Brian: We have to build during the bear markets to thrive in the bull markets.

Companies can create internal hackathons to exercise creativity...

Also, read: Internal Hackathons - Drive Innovation And Increase Engagement In Tech Teams

Steve: HR professionals facing a hiring freeze will do well to “upgrade” processes, talent, and technology aggressively during downtime...

Steve O'Brien, Senior Vice President, Talent Acquisition at Syneos Health

Patricia: Talk to hiring managers in all your departments. Ask, what are the top 3-5 roles they are hiring for in the new year?...

Also, watch: 5 Recruiting Tips To Navigate The Hiring Freeze With Shalini Chandra, Senior TA, HackerEarth

#3 What top 3 skills would you like HR professionals/engineering managers to add to their repertoire in 2023 to deal with upcoming challenges?

6 industry experts predict the 2023 recruiting trends

Rado: Prioritization, team time, and environment management.

I think "prioritization" and "team time" management are obvious. But what do I mean by "environment management"?

A productive environment is one of the key ingredients for a productive team. Look at where your team wastes most time, which can be automated. For example, end-to-end writing tests take time because our tools are cumbersome and undocumented. So let's improve this.

Mike: Setting better metrics/KPIs, moving away from LinkedIn, and sharing more knowledge.

Metrics/KPIs: Become better at setting measurable KPIs and accountable metrics. They are not the same thing—it's like the Square and Rectangle. One fits into the other but they're not the same. Hold people accountable to metrics, not KPIs. Make sure your metrics are aligned with company goals and values, and that they push employees toward excellence, not mediocrity.
Freedom from LinkedIn: This is every year, and will probably continue to be. LinkedIn is a great database, but it is NOT the only way to find candidates, and oftentimes, not even the most effective/efficient. Explore other tools and methodologies!
Join the conversation: I'd love to see new names of people presenting at conferences and webinars. And also, see new authors on the popular TA content websites. Everyone has things they can share—be a part of the community, not just a user of. Join FB groups, write and post articles, and comment on other people's posts with more than 'Great article'. It's a great community, but it's only great because of the people who contribute to it—be one of those people.

Pamela: Resilience, leveraging data, and self-awareness.

Resilience: A “must-have” skill for the 21st century due to constant changes in the tech industry. Face and adapt to challenges. Overcome them and handle disappointments. Never give up. This will keep HR people alive in 2023.
Data skills: Get some data analyst skills. The capacity to transfer numbers into data can help you be a better HR professional, prepared to improve the employee experience and show your leadership team how HR is leveraging data to drive business results.
Self-awareness: Allows you to react better to upsetting situations and workplace challenges. It is a healthy skill to cultivate – especially as an HR professional.

Also, read: Diving Deep Into The World Of Data Science With Ashutosh Kumar

Brian: Agility, resourcefulness, and empathy.

Agility: Allows professionals to move with market conditions. Always be as prepared as possible for any situation to come. Be flexible based on what does or does not happen.
Resourcefulness: Allows professionals to do more with less. It also helps them focus on how to amplify, lift, and empower the current teams to be the best they can be.
Empathy: Allows professionals to take a more proactive approach to listening and understanding where all workers are coming from. Amid stressful situations, companies need empathetic team members and leaders alike who can meet each other wherever they are and be a support.

Steve: Negotiation, data management, and talent development.

Negotiation: Wage transparency laws will fundamentally change the compensation conversation. We must ensure we are still discussing compensation early in the process. And not just “assume” everyone’s on the same page because “the range is published”.
Data management and predictive analytics: Looking at your organization's talent needs as a casserole of indistinguishable components and demands will not be good enough. We must upgrade the accuracy and consistency of our data and the predictions we can make from it.

Also, read: The Role of Talent Intelligence in Optimizing Recruitment

Talent development: We’ve been exploring the interplay between TA and TM for years. Now is the time to integrate your internal and external talent marketplaces. To provide career experiences to people within your organization and not just those joining your organization.

Patricia: Technology, research, and relationship building.

Technology: Get better at understanding the technology that’s out there. To help you speed up the process, track candidate experience, but also eliminate bias. Metrics are becoming big in HR.
Research: Honestly, read more books. Many great thought leaders put out content about the “future of work”, understanding “Gen Z”, or “quiet quitting.” Dedicate work hours to understanding your ever-changing field.
Relationship Building: Especially in your immediate communities. Most people don’t know who you are or what exactly it is that you do. Build your personal brand and what you are doing at your company to impact those closest to you. Create a referral funnel to get a pipeline going. When people want a job you and your company ought to be top of mind. Also, tell the stories of the people that work there.

Hiring Strategies

7 Tech Recruiting Trends To Watch Out For In 2024

The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.

While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.

What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?

The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.

7 tech recruiting trends for 2024

6 Tech Recruiting Trends To Watch Out For In 2022

Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.

Trend #1—Leverage data-driven recruiting

Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.

Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.

The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.

A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.

Trend #3—Focus on candidate-driven market

The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.

Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.

Recruiters need to think creatively to attract and retain top talent.

Recommended read: What NOT To Do When Recruiting Fresh Talent

Trend #4—Have a diversity and inclusion oriented company culture

Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.

McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.

It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

Trend #5—Embed automation and AI into your recruitment systems

With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.

Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.

Trend #7—Be proactive in candidate engagement

Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.

As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.

Our tech recruiting platform enables you to:

Tap into a 6 million-strong developer community
Host custom hackathons to engage talent and boost your employer brand
Create online assessments to evaluate 80+ tech skills
Use dev-friendly IDEs and proctoring for reliable evaluations
Benchmark candidates against a global community
Conduct live coding interviews with FaceCode, our collaborative coding interview tool
Guide upskilling journeys via our Learning and Development platform
Integrate seamlessly with all leading ATS systems
Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide

Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Developer Insights

Code In Progress - The Life And Times Of Developers In 2021

Developers. Are they as mysterious as everyone makes them out to be? Is coding the only thing they do all day? Good coders work around the clock, right?

While developers are some of the most coveted talent out there, they also have the most myths being circulated. Most of us forget that developers too are just like us. And no, they do not code all day long.

We wanted to bust a lot of these myths and shed light on how the programming world looks through a developer’s lens in 2021—especially in the wake of a global pandemic. This year’s edition of the annual HackerEarth Developer Survey is packed with developers’ wants and needs when choosing jobs, major gripes with the WFH scenario, and the latest market trends to watch out for, among others.

Our 2021 report is bigger and better, with responses from 25,431 developers across 171 countries. Let’s find out what makes a developer tick, shall we?

Developer Survey

“Good coders work around the clock.” No, they don’t.

Busting the myth that developers spend the better part of their day coding, 52% of student developers said that they prefer to code for a maximum of 3 hours per day.

When not coding, devs swear by their walks as a way to unwind. When we asked devs the same question last year, they said they liked to indulge in indoor games like foosball. In 2021, going for walks has become the most popular method of de-stressing. We’re chalking it up to working from home and not having a chance to stretch their legs.

Staying ahead of the skills game

Following the same trend as last year, students (39%) and working professionals (44%) voted for Go as one of the most popular programming languages that they want to learn. The other programming languages that devs are interested in learning are Rust, Kotlin, and Erlang.

Programming languages that students are most skilled at are HTML/CSS, C++, and Python. Senior developers are more comfortable working with HTML/CSS, SQL, and Java.

How happy are developers

Employees from middle market organizations had the highest 'happiness index' of 7.2. Experienced developers who work at enterprises are marginally less happy in comparison to people who work at smaller companies.

However, happiness is not a binding factor for where developers work. Despite scoring the least on the happiness scale, working professionals would still like to work at enterprise companies and growth-stage startups.

What works when looking for work

Student devs (63%), who are just starting in the tech world, said a good career growth curve is a must-have. Working professionals can be wooed by offers of a good career path (69%) and compensation (68%).

One trend that has changed since last year is that at least 50% of students and working professionals alike care a lot more about ESOPs and positive Glassdoor reviews now than they did in 2020.

To know more about what developers want, download your copy of the report now!

We went a step further and organized an event with our CEO, Sachin Gupta, Radoslav Stankov, Head of Engineering at Product Hunt, and Steve O’Brien, President of Talent Solutions at Job.com to further dissect the findings of our survey.

Tips straight from the horse’s mouth

Steve highlighted how the information collated from the developer survey affects the recruiting community and how they can leverage this data to hire better and faster.

The insight where developer happiness is correlated to work hours didn’t find a significant difference between the cohorts. Devs working for less than 40 hours seemed marginally happier than those that clocked in more than 60 hours a week.

“This is an interesting data point, which shows that devs are passionate about what they do. You can increase their workload by 50% and still not affect their happiness. From a work perspective, as a recruiter, you have to get your hiring manager to understand that while devs never say no to more work, HMs shouldn’t overload the devs. Devs are difficult to source and burnout only leads to killing your talent pool, which is something that you do not want,” says Steve.

Roughly 45% of both student and professional developers learned how to code in college was another insight that was open to interpretation.

“Let’s look at it differently. Less than half of the surveyed developers learned how to code in college. There’s a major segment of the market today that is not necessarily following the ‘college degree to getting a job’ path. Developers are beginning to look at their skillsets differently and using various platforms to upskill themselves. Development is not about pedigree, it’s more about the potential to demonstrate skills. This is an interesting shift in the way we approach testing and evaluating devs in 2021.”

Rado contextualized the data from the survey to see what it means for the developer community and what trends to watch out for in 2021.

Node.js and AngularJS are the most popular frameworks among students and professionals.

“I was surprised by how many young students wanted to learn AngularJS, given that it’s more of an enterprise framework. Another thing that stood out to me was that the younger generation wants to learn technologies that are not necessarily cool like ExtJS (35%). This is good because people are picking technologies that they enjoy working with instead of just going along with what everyone else is doing. This also builds a more diverse technology pool.” — Rado

22% of devs say ‘Zoom Fatigue’ is real and directly affects productivity.

“Especially for younger people who still haven’t figured out a routine to develop their skills, there is something I’d like you to try out. Start using noise-canceling headphones. They help keep distractions to a minimum. I find clutter-free working spaces to be an interesting concept as well.”

The last year and a half have been a doozy for developers everywhere, with a lot of things changing, and some things staying the same. With our developer survey, we wanted to shine the spotlight on skill-based hiring and market trends in 2021—plus highlight the fact that developers too have their gripes and happy hours.

Uncover many more developer trends for 2021 with Steve and Rado below:

View all

Hiring Strategies

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.

What is Pre-Employement Assessment?

Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.

By exploring and evaluating the best pre-employment assessment tools and tests available, you can:

Improve the accuracy and efficiency of your hiring process.
Identify top talent with the right skills and cultural fit.
Reduce the risk of bad hires.
Enhance the candidate experience by providing a clear and objective evaluation process.

This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.

Why pre-employment assessments are key in hiring

While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:

Improved decision-making:

By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.
Reduced bias:

Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.
Increased efficiency:

Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.
Enhanced candidate experience:

When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.

Types of pre-employment assessments

There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:

1. Skill Assessments:

Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.

2. Personality Assessments:

These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.

3. Cognitive Ability Tests:

These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.

4. Integrity Assessments:

These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.

By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.

Leading employment assessment tools and tests in 2024

Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:

HackerEarth:

A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.
SHL:

Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Pymetrics:

Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.
Wonderlic:

Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.
Harver:

An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.

Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.

Choosing the right pre-employment assessment tool

Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:

Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
User experience: Consider including user reviews or ratings where available.

Comparative analysis of assessment options

Instead of a comprehensive comparison, consider focusing on specific use cases:

Technical skills assessment:

Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.
Soft skills and personality assessment:

Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.
Candidate experience:

Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.

Additional tips:

Encourage readers to visit the platforms' official websites for detailed features and pricing information.
Include links to reputable third-party review sites where users share their experiences with various tools.

Best practices for using pre-employment assessment tools

Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:

Define your assessment goals:

Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?
Choose the right assessments:

Select tools that align with your defined goals and the specific requirements of the open position.
Set clear expectations:

Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.
Integrate seamlessly:

Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.
Train your team:

Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.

Interpreting assessment results accurately

Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:

Use results as one data point:

Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.
Understand score limitations:

Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.
Look for patterns and trends:

Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.
Focus on potential, not guarantees:

Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.

Choosing the right pre-employment assessment tools

Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:

Industry and role requirements:

Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.
Company culture and values:

Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.
Candidate experience:

Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.

Budget and accessibility considerations

Budget and accessibility are essential factors when choosing pre-employment assessments:

Budget:

Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.
Accessibility:

Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.

Additional Tips:

Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.

By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.

Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.

Future trends in pre-employment assessments

The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:

Artificial intelligence (AI):

AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.
Adaptive testing:

These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.
Micro-assessments:

Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.
Gamification:

Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.

Conclusion

Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.

Hiring Strategies

Tech Layoffs: What To Expect In 2024

Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?

Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.

The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.

This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.

What are tech layoffs?

The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.

In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.

Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.

This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

Yes, the market is always uncertain, but why resort to tech layoffs?

Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.

In addition, some common reasons could be:

Financial struggles

Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.

Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)

Changes in demand

The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.

Restructuring

Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.

Automation

With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.

Mergers and acquisitions

When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.

But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.

Will layoffs increase in 2024?

It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.

But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.

The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.

So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.

Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech

What types of companies are prone to tech layoffs?

Tech layoffs can occur in organizations of all sizes and various areas.

Following are some examples of companies that have experienced tech layoffs in the past:

Large tech firms

Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.

Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.

Startups

Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.

Small and medium-sized businesses

Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.

Companies in certain industries

Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.

Companies that lean on government funding

Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.

How to track tech layoffs?

You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.

Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on layoffs.

In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.

Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.

News articles

Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.

Social media

Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.

Online forums and communities

There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.

Government reports

Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.

How do companies reduce tech layoffs?

Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:

Salary reductions

Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.

Implementing a hiring freeze

Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.

Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze

Non-essential expense reduction

Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.

Reducing working hours

Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.

These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.

Tech layoffs to bleed into this year

While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.

So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)

Hiring Tools

What is Headhunting In Recruitment?: Types & How Does It Work?

In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.

What is Headhunting in recruitment?

In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.

How do headhunting and traditional recruitment differ from each other?

Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.

In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.

AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams

All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.

Types of headhunting in recruitment

Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.

Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.

How does headhunting work?

Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.

Identifying the role

Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.

Defining the job

Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.

Candidate identification and sourcing

Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.

Approaching candidates

Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.

Assessment and Evaluation

In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.

Interviews and negotiations

Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.

Finalizing the hire

Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.

The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.

Common challenges in headhunting

Despite its advantages, headhunting also presents certain challenges:

Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.

Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.

Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.

Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.

Advantages of Headhunting

Headhunting offers several advantages over traditional recruitment methods:

Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.

Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.

Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.

Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.

Conclusion

Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.

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