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How these hackathon winners apply Machine Learning to minimize rash driving

Hackathons have become the go-to method for developers and companies to innovate in tech and build great products in a short span of time. With tens of thousands of developers participating in hackathons across the globe every month, it’s a great way to collaborate and work on solving real-life issues using tech.

“Along with being stimulating and productive, hackathons are fun” - says Team Vikings, who won the first prize (a brand new Harley-Davidson bike!) in the recently concluded GO-HACK hackathon. The team built Rashalytics - a comprehensive platform for analysing and minimising rash driving. And now, they have big plans of taking this hack live for the public.

Read on to know more about their amazing idea and how they built the platform.

What is Rashalytics?

Rashalytics is a system that promises to mitigate the problem of rash driving by intelligently incentivising or penalising the driver based on his driving style. It has been designed to reduce the number of accidents that have increased with the hyperlocal on-demand delivery requiring breakneck speeds of the various products.

The system is able to extract the data of rich metrics like sharp acceleration, hard braking, sharp turns, etc. from the driver's android phone, which is used to train the machine learning models.

Technologies/platforms/languages
  • Nodejs - To create the API server and the mock sensor data generator
  • Kafka - To build the data pipeline
  • Apache Spark - To process the real-time data stream and generate metrics to measure the driving quality
  • ReactJs - To create the dashboard web app
  • Google roads & maps API : To get the traffic and ETA data
Functionality

Machine learning challenge, ML challenge

The system primarily consists of 4 parts:.
  1. The Android app: Simulated by the team, this app aggregates locally and sends the chunks of sensor data to the API server via an HTTP endpoint.
  2. API Server: This matches the data with the schema and if valid, it puts the data in Kafka queue.
  3. Engine: Made with the Apache Spark, this helps sensor data to aggregate and form metrics such as sharp acceleration, hard braking, sharp turns, etc. These metrics, in turn, are used to generate a dynamic driving quality score for the driver. This score forms the basis of a lot of analytics and functionalities that this system provides.
  4. Dashboard: The dashboard provides a beautiful and intuitive interface to take proactive decisions as well as run analytics using the provided APIs. It has been written using ReactJS.
Here’s the flow diagram showing how the whole system works:





This system allowed the team to create:
  • A dynamic profile and the dashboard of the rider describing his driving style, which affects his rating.
  • An actionable "real-time" rash driving reporting system which allows the authorities and the hub incharges to react before it’s too late.
  • A dashboard usable by both the fleet managers and traffic police control board to visualise the data such as incident distribution by time, which tells at what time of the day a driver is more likely to drive in an unsafe manner.
  • A modular system in which the new data sources, metrics, and models can be added so that the third-party vendors can be easily on-boarded onto the platform.




ChallengesHere are some of the challenges that the team faced while building this application:
  1. Setting up the entire system architecture with different components by developing them in isolation and then combining them together to work seamlessly
  2. Deciding the thresholds for different metrics after which the driving will be considered rash
  3. Creating a linear predictor for the driving quality score vs time with only one data point
  4. Creating a synthetic feature as generating the score itself is challenging enough
What’s Next?

Project creators Shivendra Soni, Rishabh Bhardwaj and Ankit Silaich have great plans in store for their project. Here are some of their ideas:
  1. Create and SDK for easy data collection and integration with different apps and make it possible for third-party vendors to utilise this data
  2. Improve the driving score model to include even more parameters and make it more real-world oriented
  3. Create a social profile which lets the users share their driving score
  4. Enable enterprise grade plug-n-play integration support

Why the Machine Learning industry can't grow without Open Source

Toward the end of 2016, Google DeepMind made their machine learning platform, DeepMind Lab, publicly available. Despite warnings from experts like Professor Stephen Hawking, Google’s decision to expose its software to other developers is part of a movement to further develop the capabilities of machine learning. They aren’t the only ones though. Facebook made its deep learning software public last year, and Elon Musk’s non-profit organization OpenAI released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI, and others made their platforms public, and how will this affect the adoption of machine learning?

Open source machine learning… Why?

The examples mentioned gives us a better picture. If you look closely, machine learning has always been open-source, and open R&D is the fundamental reason why machine learning is where it is today.

By making its machine learning platform available to the public, Google has validated an increased consciousness about its AI research. There are various advantages to making the software accessible such as finding new talent and capable startups to add to the Alphabet Inc. family. At the same time, developers can access DeepMind Lab, which will help address one of the key issues with ML research – the dearth of training environments. OpenAI has introduced a new virtual school for AI, Universe, which uses games and websites to train AI systems.

Making machine learning platforms publicly available is a much-needed move now.

5 advantages of open source in machine learning projects

1. Reproducing scientific results and fair comparison of algorithms: In machine learning, numerical simulations are frequently used to provide experimental validation and comparison of methods. Preferably, such a comparison between methods is based on a rigorous theoretical analysis. Open source tools and technology offer an opportunity to thoroughly conduct research using publicly available source code without depending on the vendor.

2. Quick bug finding and fixing: When you carry out machine learning projects using open source software, it becomes easy to detect and resolve bugs in the software.

3. Accelerate scientific development with low-cost, reusing methods: It is a known fact that scientific progress is always made based on existing methods and discovery, and the machine learning field is not an exception. The availability of open source technologies in machine learning can leverage existing resources for research and projects greatly.

4. Long-term availability and support: Whether it is an individual researcher, developer, or data scientist, open source might serve as a medium to ensure that everyone can use his/her research or discovery even after changing jobs. Thus, the chances of having long-term support are increased by releasing code under an open source license.

5. Faster adoption of Machine Learning by various industries: There are notable paradigms of the open source software that has supported the creation of multi-billion dollar machine learning companies and industries. The main reason for the adoption of machine learning by researchers and developers is the easy availability of high-quality open source implementations for free.

Machine learning challenge, ML challenge

Accelerating the adoption curve of open source machine learning

The advancement of open-source machine learning will enable a steeper adoption curve of Artificial Intelligence thus encouraging developers and startups to work towards making AI smarter. The availability of software platforms is changing the way in which businesses develop AI, encouraging them to follow in the footsteps of Google, Facebook, and OpenAI’s by being more transparent about their research.

The shift toward open machine learning platforms is an important phase in ensuring that AI works for everyone, instead of just a handful of tech giants.

From my perspective, there are three reasons for tech giants to release open­-source machine learning projects:

  • To hire engineers who have already started to engage with the open source community and have built an understanding via an open­-source project
  • To control a machine learning platform that works best into their broader SDK or cloud-platform strategy
  • To grow the entire market because their market share has reached a saturation point

When a start­up releases an open-­source project, it triggers awareness, some of which gets converted into paid customers and recruitment. Startups, by their very definition, are trying to get a foothold in a specific market instead of growing an existing market. Open-­source is frictionless. It costs nothing to serve another organic user and enable organizations to solve real problems, thus allowing the code to have a greater impact.

Instead of disrupting the start­ups that build proprietary technologies, open-source has given the world a taller pair of shoulders to stand on. One of the knock-­on effects may be a shift in focus on where the value lies. With the commoditization of the entire AI technology stack, the focus shifts from core machine learning technologies to building the best models–and this requires a vast amount of data and domain­ experts to create and train the models. Large incumbent businesses with an existing network effect have a natural advantage.

Best frameworks in open source machine learning

There is a wide range of open source machine learning frameworks available in the market, which enable machine learning engineers to:

  • Build, implement and maintain machine learning systems
  • Generate new projects
  • Create new impactful machine learning systems

Some of the important frameworks include:

  • Apache Singa is a general, distributed, deep-learning platform for training big deep-learning models over large datasets. It is designed with an instinctive programming model based on layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users.
  • Shogun is among the oldest and most revered machine learning libraries. Shogun was created in 1999 and written in C++, but isn’t limited to working in C++. Thanks to the SWIG library, Shogun can be used in languages and environments such as:
    • Java
    • Python
    • C#
    • Ruby
    • R
    • Lua
    • Octave
    • Matlab

Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, dimensionality reduction, clustering, etc. It contains several exclusive state-of-the-art algorithms, such as a wealth of efficient SVM implementations, multiple kernel learning, kernel hypothesis testing, Krylov methods, etc.

  • TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Nodes implement mathematical operations and can also represent endpoints to feed in data, push out results or read/write persistent variables. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors
  • Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages — NumPy, SciPy, and matplotlib — for math and science work. The resulting libraries can be used either for interactive “workbench” applications or be embedded into other software and reused. The kit is available under a BSD license, and therefore, it’s fully open and reusable. Scikit-learn includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). Since scikit-learn was developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in short order.
  • MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. Spark MLlib is regarded as a distributed machine learning framework on top of the Spark Core which, mainly due to the distributed memory-based Spark architecture, is almost nine times as fast as the disk-based implementation used by Apache Mahout.
  • Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. It connects to data that is stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on the said data to create a model
  • Apache Mahout, is a free and open source project of the Apache Software Foundation. It’s goal is to develop free distributed or scalable machine learning algorithms for diverse areas like collaborative filtering, clustering and classification. Mahout provides Java libraries and Java collections for various kinds of mathematical operations. Apache Mahout is implemented on top of Apache Hadoop using the MapReduce paradigm. Once Big Data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in these Big Data sets thus turning this into ‘big information’ quickly and easily

The final say

Machine learning can indeed solve real scientific and technological problems with the help of open source tools. If machine learning is to solve real scientific and technological problems, the community needs to build on each other’s open source software tools. We believe that there is an urgent need for machine learning open source software, which will fulfill several concurrent roles, which include:

  • Better means for reproducing results
  • Mechanism for providing academic recognition for quality software implementations
  • Acceleration of the research process by allowing the standing on shoulders of others (not necessarily tech giants!)

Feature Engineering + H2o Gradient Boosting (GBM) in R Scores 0.936

With less than 3 days to go, this script is meant to help beginners with feisty ideas, machine learning workflow and motivation for ongoing machine learning challenge.Here's a quick workflow of what I've done below:
  1. Load data and explore
  2. Data Pre-processing
  3. Dropped Features
  4. One Hot Encoding
  5. Feature Engineering
  6. Model Training
Good Luck!Note: For more feature engineering ideas, spend time on exploring data by loan_status variable. For categorical vs categorical data, create dodged bar plots. For categorical vs continuous data, create density plots and use fill=as.factor(loan_status).

To help the community, feel free to contribute the equivalent python / C ++ script in the comments below.

Update: You can get python script for this solution from Jin Cong Ho's comment below.

Script (R)

Resources - Handy Algorithms for this Challenge

State of Virtual Reality and Augmented Reality in India

Silicon Valley has been upbeat about Virtual Reality and Augmented Reality for a while now. But it appears that USA is not the only one riding this wave. Rest of the world, including India, has been creating a stir in these fields as well.

Although it is hard to predict how many Oculus devices or Google Cardboards are out there in the hands of consumers in this part of the world, India is doing a lot with different forms of Mixed Reality.

Alive

Source: Alive-App-Earn-Up-to-Rs-100-Daily-by-Watching+Videos-Playing+Quiz-Reading+News.jpg

2016 Summer batch YCombinator startup Innov8 chief, Dr. Ritesh Malik, a serial entrepreneur and investor, has already touched base with Augmented Reality four years ago with Alive. Alive App, now acquired by a leading Indian media publication company – Times of India (TOI), allows its readers to access videos, pictures, and polls related to select news items appearing in the newspaper, just by focusing the phone camera on any content in the newspaper carrying the Alive logo.

Memesys Culture Lab

Source: http://www.thehansindia.com/assets/9895_Memesys.jpg

With a $2.5 million funding round raised last year by Round Glass Partners, national award-winning director Anand Gandhi pioneered storytelling in VR by means of his new venture Memesys Culture Labs. The company has successfully been able to tap into Bollywood with its VR production arm, ElseVR. A behind-the-scenes promo of Dangal (a massive blockbuster movie from last year) was shot in 360 degrees by ElseVR and captured over 2 million views.

AutoVRse

Source: http://autovrse.in/img/autovrse.png

More recently, AutoVRse came out with its Virtual Automobile Showroom. It is an interactive experience that enables customers to explore and customize their cars in real-time in a virtual environment. The company has installed systems at a number of showrooms, kiosks, and retail outlets, giving customers a surreal experience of the look and feel of its vehicle-to-be before actually making the purchase.

Imaginate

Source: http://yourstory.in/wp-content/uploads/2012/05/Imaginate.jpg

Imaginate Software Labs is another AR/VR-based technology enterprise that offers innovative visualization products and services. The company largely works with enterprises, helping them build their own products in this category, and has recently raised a $500K round to boost operations. Imaginate also boasts of products developed in-house such as Dressy, a virtual fitting room.

GridRaster

Source: https://www.gridraster.com/wp-content/uploads/2016/08/gridraster-1.jpg

GridRaster is an interesting company because the products it sells are technology products. The company is primarily based out of Palo Alto, California, but has its development office in Bengaluru and has already raised close to $1.65 million. GridRaster provides the infrastructure layer to overcome device limitations – compute and battery, by intelligently harnessing the computing power and providing a centralized cloud to power compelling VR/AR experiences. The solution provides high-performance graphics with ultra-low latency while hugely improving battery performance. The technology drives mass adoption of VR/AR by dramatically improving the reach of exciting content and bringing breathtaking, new experiences to users.

Incubators and Accelerators

Source: https://static1.squarespace.com/static/52e442c5e4b0ac5ff7e6b28d/t/53cec07ce4b0a7dd85188ca1/1406058662372/Lowes-Innovation-Lab.jpg

Adapting to this explosion of startups in Mixed Reality and a maturing developer ecosystem, a number of US tech accelerators and incubators are now focused on enrolling more Indian startups in their upcoming batches. Lowe’s, a home improvement retailer based in the U.S., opened its first startup accelerator, Lowe’s Innovation Lab, in the heart of the technology hub of India, Bengaluru, and is already home to many Virtual Reality startups. Target, Tesco, and Walmart Labs are other U.S. retail giants also focused on bringing more startups to the Mixed Reality space in their own incubation centers set up in India.

At a recent event hosted by Lowe’s Innovation Labs in association with VR Collective, many spokespersons from the above-mentioned retail giants were present, and all of them unanimously agreed that retail is going to be massively disrupted by Mixed Reality. The plan is to win back their long-losing market share to E-Commerce by placing important bets in Mixed Reality.

Other cool Indian startups in Mixed Reality

SmartVizX recently secured $500,000 seed capital to disrupt the architecture, engineering, and construction industry.

G for Gestures, a gesture recognition technology and VR integrated product, is disrupting the hospitality sector.

PlayShifu, run by Vivek Goyal (Stanford University Graduate of Business alumni), is another Indian startup making education fun for kids through a range of products in Augmented Reality.

House of Blue Beans is a startup revolutionizing the home design industry with its immersive application ‘Roomstyler’. Roomstyler is a VR-based consumer experience that allows designers and customers to collaborate on a visual platform to make the home-designing process extremely effective. It is one of the startups enrolled in the latest batch of Lowe’s Innovation Labs.

Developer Ecosystem

Developers are already driving the mobile gaming ecosystem in India. It shouldn’t take long for developers to turn their attention to building more applications for Augmented and Virtual Reality as the consumer groups get flooded with more devices.

Gaming engines like Unity3d and Unreal Engine that drive the Mixed Reality applications are popular with the tech talent as the same tools are used for developing mobile games.

It should not be surprising to see a multitude of AR/VR applications coming out soon from India as the ecosystem matures.

Key developments by existing players

Myntra

Source: https://i.gadgets360cdn.com/large/roadster_led_sign_1489744980128.jpg

Recently, Myntra came up with its first brick-and-mortar retail outlet in Indiranagar, Bengaluru. Ananth Narayanan, CEO of Myntra and Jabong, says, “The goal is not to drive sales – but to engage with the customer and as you’d expect from us, we’re using a lot of technology to do just that. We’ve got VR zone, multi-touch screens, full-screen displays that mimic the Myntra app, and selfie zones.”

The VR zone is placed with 4 Samsung Gear headsets which reels a 360-degree video showcasing the “Roadster life”. You can see around with a band playing on the road, airplanes rushing overhead, and bikers zooming past. Although it can seem a little out of place for the store, it gives a nice touch and indicates the interests of the established players.

Byju’s Classes

Source:https://edtechreview.in/images/byju_classes_test_prep_for_students_online.jpg

Byju’s Classes is also ramping up its development by hiring Unity engineers proficient in 360-video streaming, indicating its interest in including some of its content for VR as well. This is in sync with the future of learning and Byju’s Classes is moving forward with an initiative to drive experiential learning.

The space is heating up, with a lot of developer interest and institutional focus to discover the next big thing in Virtual and Augmented Reality.

What will you build next??

Come and Register at the UnitedByHcl hackathon.
Happy Mixing Reality!!

Best countries for software engineers and developers to work

[Bonus content – Read our latest blog – Top 10 cities to hire developers]

This time we decided to figure out which are the top countries to work with, for programming enthusiasts making a living as developers, software engineers, or data analysts.

From my experience, English speakers can find the most jobs in the U.S. (West Coast, obviously), United Kingdom (London), Ireland(IT employers always ask how to hire workers from Ireland), Netherlands (Amsterdam), Switzerland, and Belgium.

New Zealand and Australia are pretty popular among developers who love the laid-back lifestyle.

But the scenarios change when we talk about non-English speaking nations.

Japan is growing exponentially; Russia and China have a huge culture of programming, and IT companies are growing rapidly in these countries; and India, Southeast Asian countries (Singapore and Indonesia), and South Korea (Seoul) are other popular and growing markets.

Often, the lower median salary is easier to stomach because of the lower cost of living.

What is important to understand that the definition of “best country” may not be categorical, and depends on a lot of people’s preferences.

To keep things fair we decided to dig up data from some popular sources to identify the best countries to work in for software engineers.

We listed these countries in order of their Happiness index and technological advancement in the field of IT over the years.

Top 10 countries forSoftware engineers / Developers/ Data Scientists to work

  1. Switzerland
  2. Canada
  3. Australia
  4. Netherlands
  5. Germany
  6. USA
  7. Sweden
  8. Denmark
  9. Singapore
  10. United Kingdom

You can read the detailed research below and other picks of top countries list based on various job profiles

Google Trends

Google Trends is a public web facility of Google Inc., based on Google Search, that shows how often a particular search term is entered relative to the total search volume across various regions of the world, and in various languages (Wikipedia).

Read What is Google trends data – and what does it mean? if you want to know more.

The numbers in the table depict the popularity of one language over another, as searching on Google.

A programming language with a higher number shows that the interest is higher as compared to other languages.

This popularity could be due to academics, a professional requirement, or interest which leads to various job opportunities.

As discussed, Java is fairly popular.

Python is one of the most searched languages in Australia. C#, despite showing a high requirement in the job portal, is not really popular.

Swedish people had been searching for Swift programming language more often than others.

Ruby leads in Ireland. MatLab is a popular Google search term in almost all the listed nations, showing its relevance in academics.

(Also read – How to hire a full stack developer)

The below graphs compares the popularity of programming languagesin order of Java, Python, PHP, C#, JavaScript, C++, C, Objective-C, R, Swift, Angular JS, Ruby, Perl, Matlab in each country respectively.

Which means Java and R are searched more often and in greater volume as compared to Swift and Angular Js in Denmark.

Would you like to get updates once a month on our latest articles? We won’t spam, we promise. Subscribe now to The HackerEarth Blog!

Indeed.co

Indeed.co, available in 50 countries and 28 languages, is one of the most high-traffic job websites in the United States and other countries.

Using country-specific search for the number of software engineers jobs listed on Indeed, we found data which matched our previous research on Top programming languages to learn.

While Java remained the favorite in all the top destinations. C, C++, and C# programmers are still in demand in these nations, making them “evergreen” programming languages and famous among software engineers and developers.

In the U.S., China, India, and Japan, PHP developers have quite sought after.

The requirement of R programmers is higher in Switzerland, USA, India, and much more so in Germany and France. Canada, Netherlands, UK, USA, India, and China clearly require MatLab skills.

If you are a Ruby developer, Japan needs you. But Canada gives first preference to Perl coders.

Top countries to work for software engineer

Median Salary – Programmer salary by country

What’s happiness without a handsome salary?

Hence, we listed the average salary for a particular job (Source – PayScale). These values have been expressed in US dollars.

Switzerland, Sweden, Australia, and the United States have higher software engineersalaries than other countries.

A data scientist is one of the highest-paid jobs across the globe. Argentina pays PHP developers generously compared to the country’s average pay for other IT skills.

France is looking for Java and front-end developers, paying them well for their skills.

Japan, Singapore, and, particularly China and India, offer fairly poor compensation despite having a high requirement for skilled employees.

Top countries for Java developers to work –

  • Switzerland
  • The United States of America
  • Australia
  • Germany
  • United Kingdom

Top countries for.NET developers to work –

  • The United States of America
  • Canada
  • Germany
  • Netherlands
  • Japan

Top countries for PHP developers to work –

  • The United States of America
  • United Kingdom
  • Germany
  • France
  • Sweden / Australia

Top countries for Data Scientist to work –

  • Switzerland
  • Canada
  • Netherlands
  • United Kingdom / Germany
  • The United States of America

Read here – 8 different job roles in Data Science / Big Data industry

We understand that the quality of life, safety, cost of living, state taxes, commute cost, etc. are some of the other major factors to be considered when deciding the top work destinations for a developer.

However, job listing, the popularity of the skill, median salary, and happiness index are equally important.

8 Latest Artificial Intelligence Software (Apps) Challenging The Human Brain

Introduction

“In the past 2,000 years, the hardware in our brains has not improved… In the next 30 years, AI will overtake human intelligence,” says Softbank CEO Masayoshi Son.

If you’ve read Ray Kurzweil’s “The Singularity is Near: When Humans Transcend Biology,” you’d expect that AI is going to exhibit human-level intelligence in a decade or two. The startlingly thought-provoking work by the futurist gives you a fair picture of the road ahead, a time when humans, with the aid of advanced technologies, will “transcend their biological limitations.”

And you know what? This plausible scenario is at our doorstep. With superintelligence on the brink of becoming a reality, his words ring true, although they are downright scary. Computers and their growing abilities are likely to outpace our skills sooner than we think. $16 trillion will be added to the global economy by 2030, thanks to artificial intelligence.

Terms like artificial intelligence and machine learning have been bandied about for a while now. Despite the groundbreaking strides, in terms of intuition, vision, common sense, and language, there are miles to cover. Machines can’t still beat us at everything we do, but they’ve surely have outsmarted us in some ways.

This post talks about some amazing artificial intelligence software that are just so smart.

Latest Artificial Intelligence Software

1. Deep Mind’s AlphaGo

In 2016, AlphaGo was in the news for beating the 9-Dan top player Lee Sedol at Go. According to Wikipedia, the ancient Chinese game of Go is “an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent.”

Watch this2 minute video:

The AI software from Google beat the South Korean grandmaster in a five-game match, winning 4­–1. Brute-force calculations will not work with this complex game. It needed much more.

AlphaGo used deep neural networks and advanced tree search to win. “AlphaGo learned to discover new strategies for itself, by playing millions of games between its neural networks, against themselves, and gradually improving,” said David Silver, Go team’s main programmer. Of the two artificial networks used, the policy network predicted the next move and the value network evaluated the winner of every position on the board.

The team used the Google Cloud Platform for the massive computing power it needed. With advanced machine learning techniques, such as reinforcement learning, and fantastic engineering skills, DeepMind did much better than expected. The cyborg had to figure out how to win, and not just know how to mimic human moves.

This highly publicized event marked the beginning of a new era. Considering the magic of Moves 37 and 78, it was more a case of a human and machine than human against machine. This outcome has immense possibilities. Like computer scientist Andy Salerno says, “AlphaGo isn’t a mysterious beast from some distant unknown planet. AlphaGo is us. AlphaGo is our incessant curiosity. AlphaGo is our drive to push ourselves beyond what we thought possible.” You can read more here.

2. DeepStack

Quite like Go, Poker fell to the magic of AI as well. In a hands-on no-limit Texas hold’em game, DeepStack beat pro poker players. The algorithm had a staggering 450 milli big blinds per game when a professional player typically has a win rate of 50 milli big blinds per game. This is quite an achievement considering this version of poker has 10160 paths that are possible for each hand!

DeepStack is based more on “intuition” than on working out the moves ahead of time. The algorithm makes real-time decisions by computing fewer possibilities in a matter of seconds.

In their paper, a team of researchers from the Czech Technical University and Charles University in the Czech Republic and the University of Alberta in Canada, talks about the winning AI algorithm DeepStack, which “combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning.” A team from Carnegie Mellon has also developed another winning AI software called Libratus. However, game theory won’t hold for multi-player games.

This approach has important implications in other fields that have imperfect information such as medicine, finance, cybersecurity, and defense.

Machine learning challenge, ML challenge

3. AI Duet

An artificial “pianist” from Google’s Creative Lab, AI Duet was built in collaboration with Yotam Mann, developer/musician. Watch this short video and see it working:

In this video, he tells you how this AI software works using the concept of neural networks. This interactive experiment is part of Magenta, an open-source project from Google’s Google Brain unit. You can access the code here.AI Duet is built with Tone.js, TensorFlow, and other Magenta tools.

Who needs a partner when this virtual piano player will accompany you in a lilting duet!

Even if you are no Chopin, this intelligent software will respond to you and create a rhythm. It could even inspire you. It is not going to get you ready for a concert in Boston Symphony Hall, you could have some real fun hitting random notes and waiting for the computer to come back with something improvisational based on melodies it has been trained on.

4. COIN

It looks like artificial intelligence is revolutionizing investment banking. JPMorgan’s software COIN, which is an acronym for contract intelligence, has worked magic by “interpreting commercial loan agreements” in seconds, a task that previously cost 360,000 man hours.

COIN is based on machine learning concepts. The software is naturally less error-prone while checking loan-servicing agreements. A Bloomberg report said that JPMorgan is keen on “deploying the technology which learns by ingesting data to identify patterns and relationships. The bank plans to use it for other types of complex legal filings like credit-default swaps and custody agreements. Someday, the firm may use it to help interpret regulations and analyze corporate communications.”

The company believes that it is only the start of smart automation of processes in the financial industry. JP Morgan is committed to new initiatives. “We’re willing to invest to stay ahead of the curve, even if in the final analysis some of that money will go to product or a service that wasn’t needed,” said Marianne Lake, the finance chief.

5. LipNet

Lip reading has become so easy with University of Oxford’s Department of Computer Science’s AI software, LipNet. The team of researchers have detailed it in the paper titled Lipnet: End-to-end sentence-level lipreading.

The paper says, LipNet “maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end.”

Watch this short interesting video:

When you compare this neural network-based software to human lip readers where the accuracy is 12.3%, it has an accuracy of 46.8% while annotating video footage. “All existing [lip-reading approaches] perform only word classification, not sentence-level sequence prediction…. To the best of our knowledge, LipNet is the first lip-reading model to operate at sentence-level,” say the researchers. AI will soon be able to transcribe footage that has a low frame rate and poor image quality sooner than we think.

Apart from the immense help it will be to people who suffer from disabling hearing loss, the team is also interested in its practical possibilities such as “silent dictation in public spaces, covert conversations, speech recognition in noisy environments, biometric identification, and silent-movie processing.”

6. Philip

For those who fear the dark side of AI, this new “killer” program is just another factor reinforcing their misgivings. MIT’s Computer Science and Artificial Intelligence Laboratory has come up with “Philip,” who is out for blood in the popular Super Smash Bros Melee multiplayer video game.

It is based on neural networks and is an “in-game computer player that learned everything from scratch.” The team led by Vlad Firou fed the vicious AI coordinates of the gameplay objects. In their deep reinforcement learning technique, the computer played itself repeatedly in Nintendo’s popular console game.

The team used algorithms such as Actor-Critic and Q Learning to beat 10 top-ranked human players. Philip bested the players with a reaction time of 33 milliseconds and being 6 times faster than humans.

You can read the research paper here.

7. DeepCoder

Cambridge University and Microsoft have come up with deep learning-based software, called DeepCoder, that can write code on its own. “The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network’s predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver,” says the team in its research paper.

They used a domain-specific language to teach the system to solve online programming challenges involving 3 to 6 lines of code. The system practices and figures out what code combinations work best. Using program synthesis, DeepCoder puts together pieces of code from software that already exists just like a programmer would.

One of the researchers Marc Brockschmidt says, “We’re targeting the people who can’t or don’t want to code, but can specify what their problem is.”

8. GoogLeNet

A deep learning AI system from Google can detect cancer with better accuracy and speed than pathologists. Identifying tumors scanning images can be error-prone and laborious.

Here’s a video tutorial on learning about googlenet in detail:

Google says, “After additional customization, including training networks to examine the image at different magnifications (much like what a pathologist does), we showed that it was possible to train a model that either matched or exceeded the performance of a pathologist who had unlimited time to examine the slides.”

“We present a framework to automatically detect and localise tumours as small as 100 × 100 pixels in gigapixel microscopy images sized 100,000×100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumour detection task,” writes Google’s team in its white paper.

Google will continue its research, working on larger datasets, to improve patient outcomes.

Summary

New possibilities and advances in artificial intelligence are pushing the boundaries of the human brain like never before. The brilliant artificial intelligence programs outlined in this post is only a glimpse into a terrifying future. If these trends continue, scientists believe that machines could surpass human capabilities sooner than later. But there really is no reason for mass hysteria as of now argues the other camp. Only time will tell, right?

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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

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 Cohen

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

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

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

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

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.

  1. 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.
  2. 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!
  3. 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.

  1. 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.
  2. 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.
  3. 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.

  1. 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.
  2. 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.
  3. 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.

  1. 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”.
  2. 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

  1. 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.

  1. 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.
  2. 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.
  3. 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.

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

Recommended read: Diversity And Inclusion in 2022 - 5 Essential Rules To Follow

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.

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

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.

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?

2023 Round Up Of Layoffs In Big Tech

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! :)

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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