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Blog URL: "https://www.hackerearth.com/blog/become-better-developer"

Last week when I sharedThe top programming languages that will be mostpopular in 2017, the frequent comment was, what does it take to be a better developer?

I’ve met some amazing developers in real life and through React Native Community, and I decided to ask them, “How do I become a better developer?” Thank you to everyone who took the time to answer these questions with passion!

This is a compilation of answers I received from them. Some of these quotes are not limited to answers from that specific question.

Interviewees / Current Position

  • Aravind Kumaraguru (Engineering Director @Pioneers in Engineering)
  • Brent Vatne (Front-end Developer @Exponent)
  • Charlie Cheever (Co-founder @Exponent)
  • Christopher Chedeau (Front-end Engineer @Facebook)
  • Dan Horrigan (Senior Back-end Developer @Futuri Media)
  • Frank W. Zammetti (Lead Architect @BNY Mellon)
  • Janic Duplessis (Co-founder @App & Flow)
  • Jake Murzy (Co-founder @commitocracy)
  • Jun Ho Hwang (Software Engineer @Coupang)
  • Keon Kim (Machine Learning Maniac @NYU)
  • Munseok Oh (Co-founder and CTO @Sketchware)
  • Satyajit Sahoo (UX Lead @ Glucosio & Front-end Engineer @Callstack.io)
  • Sonny Lazuardi Hermawan (Engineer @Sale Stock)
  • Sunggu Hwang (CTO @ScatterLab)
  • Timothy Ko (Software Engineer @Snapchat)


Q&A

Aravind Kumaraguru

Aravind is an undergrad at UC Berkeley pursuing a degree in Electrical Engineering and Computer Science and is Engineering Director for the nonprofit organization Pioneers in Engineering.

Q: How do you think I can become a better developer?

A: Obviously, never stay complacent with what you know – this field changes ridiculously fast, and you need to keep up with it. Follow along with the news in the tech industry, perhaps read up on some source code for a Python module that you recently used.

A friend of mine had some free time over winter break, so he decided to teach himself Django and build a webapp that he could interact with over SMS. It’s sort of a toy project, but he really enjoyed learning the different development paradigms. For context, he specializes in embedded systems and robotics, so this is nowhere near his comfort zone.

But pushing yourself to try different things will make you much stronger as an engineer. I personally wish I had done more web stuff before this year – in my organization (PiE), we’re developing a new iteration of a robotics kit to be used by high school students. While I have a good grasp of the low-level and systems stuff, I’m at a loss when it comes managing our UI design. Never had an interest in doing that type of stuff full-time, but having even a surface-level knowledge can be immensely helpful

Q: Do you have any projects you did to push yourself out of your comfort zone?

A: I built an automated door opener last summer, which operated a mechanical lever to open a door when an RFID card was scanned. The project used a really powerful motor and a mess of sensors to track the state of the arm, which proved to be quite difficult to coordinate. I learned real quick that I would need to do a bunch of offline testing before running my code on the device, which was very different from what I was used to up till then.

In terms of academics, I just finished CS 189, which was a massive crash course in data science, optimization, and probability theory. The programming I did in that class was also very different from what I’m used to, even though it was all in Python.


Brent Vatne

Brent is Front-end web/mobile developer working on Exponent and React Native. He contributes to tons of open-source projects.

Q: I really want to become a better developer; what would you say the first step is?

A: Do stuff you’re excited about and contribute to open source projects:-D

Q: How old are you and how much experience do you have as a programmer?

A: I am 30 years old, and very much 😮

Q: How did you join Exponent? What was the cause?

A: James (ide) and I were the most active contributors to a react-native outside of facebook and so we spoke a lot. He created exponent with Charlie. I ended up doing some consulting work with them and Charlie asked if I’d be interested in working with them full time and year, it was lots of fun so I joined.

Q: I should know objective C and Java thoroughly before I jump into React Native, right?

A: You can learn it as you go if you need to. there’s also tons of pure javascript stuff that need to be done. and documentation. lots of things 🙂


Charlie Cheever

Charlie Cheever is the co-founder of Quora, an online knowledge market. He was formerly an engineer and manager at Facebook, where he oversaw the creation of Facebook Connect and the Facebook Platform. Prior to Facebook, Cheever was employed by Amazon.com in Seattle. He left Facebook to start Quora in June 2009 to work on Exponent.

Q: What’s the motivation of Exponent being free and Open Source?

A: I really want to make something that like a 12-year-old version of me would use. So, someone who doesn’t know tons about programming but can learn new things and doesn’t have a credit card or lots of money, but has time and creativity and a phone and friends. I learned to program making calculator games on TI-85, it’s sad to me that kids can’t make stuff on their phones today.

Q: Why did you leave Quora?

A: I managed the mobile teams there and it was so slow to work on those apps even tho we had good people, I found it so frustrating And after I left I tried to build some mobile stuff and it was so annoying that I decided there needed to be a different way to make stuff. So James and I made something like react Native called Ion. It was strikingly similar actually. But React Native already had android support and 20 people working on it, and we had 2 people. So we decided to make everything else around it that we wanted to make!

Q: What did you do on Facebook?

A: I made the developer platform that all those games like FarmVille were on. Well, not all of it obviously but was one of two main developers. And I worked on the first version of facebook video, then did a lot of random other things. Then was a manager and did log in with Facebook on other sites, and then left to do Quora.

How to monetize your programming skills


Christopher Chedeau

Christopher has been working at Facebook as a Front-end Engineer for about 5 years. Previously, he worked at Curse Network.

Q: What do you do on Facebook?

A: I was on the photos team when I started, then I discovered React and started adopting and promoting it both internally and externally. I was there at the beginning of reacting native and pushed it through until 3 months ago. I just recently switched to the Nuclide team. I’m still #3 contributor on React Native.😛

Q: Do you have any prior work experience?

A: I was working for Curse (doing website for blizzard games) during my college to pay for it. It was fun to see the company go from 5 people in a guild to a 100 people company.

Q: What’s your day to day like on Facebook? The current project you’re working on?

A: I’m currently working on the Nuclide team, Facebook’s IDE built on top of Atom. I would say my time is spent half coding, half cheerleading all the cool stuff people are doing inside of FB.

Q: How do you think one can become a better developer?

A: I think that there are multiple levels.

The first level is mastering all the concepts. For example yesterday I had to write a function that removes certain keys from a big nested object. Because I’ve done this task so many times in the past, I was able to implement it in one go without even thinking and it worked the first time. For this one, exercises are really good. You want to code the same kind of things many many times to train your muscle memory.

The second level is how do you build things in a way that are not going to break in the future. Ideally, once you build something, you can move to the next thing and it’ll keep working without you there. This is challenging when there’s a ton of developers touching the codebase and product directions changing often.

Finally, the third level is how do you prevent a whole class of problems from even existing in the first place. A good example is with manual dom mutations, it’s very easy to trigger some code that interacts with a dom node that has been removed from the dom. React came in and made this problem go away. You have to go out of your way to do so, and even if you want to do those things, you have the tools to make it work: lifecycle events.

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Probably the most important thing is: tradeoffs, tradeoffs, tradeoffs. They are everywhere.

If you are working on some random throwaway feature that no one is going to use, who cares if the code is maintainable, you need it to work and now one mistake I see a lot is that people over-engineer the easy things but are not willing to make their architecture less clean from a CS perspective even though it actually provides the user experience you need.

At the end of the day, we write all this code for the users, we should first understand what the user experience should be and then do whatever it takes to get it. If the user just needs to display some content and needs to be able to edit it easily, just install WordPress, pick a good looking theme and call it a day

– Btw, pro-tip, if you want to be successful, always think about the value you are providing. If you are earning $100k a year, this means that the company should be making $200k because you’re here


Dan Horrigan

Dan is a Senior Back-end developer @Futuri Media. He has 20 years of programming experience in many different languages. He’s been contributing to React Native early/mid-2015.

Q: What’s your background as a programmer?

A: I started learning to program (with QBasic) when I was 11 and was hooked. I learned everything I could, as fast as I could. I learned a few languages like Visual Basic and started to dabble with C and C++. Then I found web development and dove in head first. First, learning HTML and CSS, then adding simple CGI scripts written in Perl, and eventually Classic ASP.

My first paying project was when I was 14: A website for the company my dad worked for, with a customer portal to let them see their job progress. This was all in ASP. After that, I started learning PHP, and have been using that as my language of choice ever since. However, I picked up a lot of experience with other languages along the way: JS, Python, Ruby (on Rails), Java, C#, Go, Objective-C.

Q: What are some projects you’re currently working on?

A: I work for Future Media (http://futurimedia.com). We provide SaaS solutions for Broadcast Radio and TV companies. We provide white label mobile applications, social engagement and discovery, audio streaming and podcast solutions, etc. I haven’t had much free time lately to contribute to many OSS projects, but hope to change that soon!

Currently, I am a Senior Back-End Web Developer, but I am transitioning into being the Director of Technical Operations.

Q: Is there something you wish you’d learned or knew earlier as a developer?

A: I wished I would have realized earlier in my career that it is OK to be wrong, and that failure is just a chance to learn.

Q: What’s the first step to becoming a good developer?

A: Come up with a small-ish project that you think would be cool, or would make your life easier, and just jump right in. Too many people try to learn without a goal other than “I want to learn to code.” Without a goal, you are just reading docs or copy/pasting from tutorials…you can’t learn that way.

To become a better developer, you need to do one simple thing: Never. Stop. Learning. Read other people’s code, figure out how that one app does that really cool thing you saw, read blogs, etc. No matter how good you are, or think you are, there is always someone better, and always more to learn.

Q: Is there a certain project you’re currently interested in? Next on your learning list?

A: I have been using, and occasionally contributing to, React Native since early/mid-2015, and continue to be interested in it.

Next, on my learning list is learning Erlang/Elixir. We build heavily distributed systems where I work and think we would really benefit from a language like that.


Frank W. Zammetti

Frank is a lead architect for BNY Mellon by day and the author of eight books on various programming topics for Apress by night

Q: How do I become a better developer?

A: I get asked this question quite a bit both at work from junior developers and from readers of my books. I always give the same answer: make games!

It sounds like a joke answer, but it most definitely is not! Games have a unique ability to touch on so many software engineering topics that you can’t help but learn things from the experience. Whether it’s choosing proper data structures and algorithms, or writing optimized code (without getting lost in micro-optimizations – at least too soon), or various forms of AI, it’s all stuff that is more broadly applicable outside of games. You frequently deal with network coding, obviously audio and visual coding (which tends to open your mind to mathematical concepts you otherwise might not be), efficient I/O and of course overall architecture, which has to be clean and efficient in games (and for many games, extensible). All those topics and more are things that come into play (hehe) when making games.

It also teaches you debugging and defensive programming techniques extremely well because one thing people don’t accept in games is errors. It’s kind of ironic actually: people will deal with some degree of imperfection in their banking website but show a single glitch in a game and they hate it! You have no choice but to write solid code in a game and you figure out what works and what doesn’t, how to recover from unexpected conditions, how to spot edge cases, all of that. It all comes into play and those are skills that developers need generally and which I find are most frequently lacking in many developers.

It doesn’t matter one bit if the game you produce is any good, or whether anyone else ever even plays it. It doesn’t matter if it’s web-based (even if your day job is), or mobile, doesn’t matter what technologies you use. The type of insight and problem-solving skills you build and tune when creating games will serve you well no matter what your day job is, even in ways that are far from obvious.

I’ve been programming games for the better part of 35 years now. No, none of them have been best-sellers or won awards or anything like that. In fact, it’s a safe bet that most people wouldn’t have even heard of my games, even the one’s still available today. None of that matters because the experience of building them is far and away the most rewarding part of it. Perhaps the best thing about programming games is that they are, by their nature, fun! You’re creating something that’s intended to be enjoyable so the process of creating it should absolutely be just as enjoyable. How many things can you do that are really fun while still being challenging and simultaneously help build the skills needed for a long career?

So yeah, make games, that’s my simple two-word answer!

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Hmm, tough question actually. I guess if there was one thing (and I’ll cheat and combine two things here because they’re related) I would say that early on I didn’t understand two very important phrases: “As simple as possible, but no simpler” and “Don’t let the perfect be the enemy of the good”.

I have a natural perfectionist mentality, so I spend a lot of time pondering architecture, API design, etc. I once spent 33 hours straight working on a Commodore 64 demo because ONE lousy pixel was out of place and my perfectionist brain just couldn’t live with it! Sometimes, I have to force myself to say “okay, it’s good enough, you’ve planned enough, now get to work and actually BUILD stuff and refactor it later if needed”, or I have to force myself to say “okay, it basically does what it’s supposed to, it doesn’t need to be absolutely flawless because nobody but me is even going to notice”. Especially when you’ve got deadlines and people relying on you, you have to make sure you’re working towards concrete goals and not constantly getting stuck trying to achieve perfection because you rarely are going to, at least initially anyway, no matter how hard you plan or try – and the dirty little secret in IT is that perfection rarely matters anyway! Good enough is frequently, err, good enough 🙂

And, your design/development approach should always strive to be as absolutely simple as possible. Of course, what constitutes “simple” is debatable and doesn’t necessarily even always have the same meaning from project to project, but for me some key metrics are how many dependencies I have (web development today is a NIGHTMARE in this regard – less is GENERALLY better) and how many layers of abstraction there are. Developers, especially in the Java world, like to abstract everything and they do so under the assumption that it’s more flexible. But if there’s one thing I’ve learned over the years it’s that the way to write flexible code is to write simple code. It’s better than abstractions and extension points and that sort of stuff because it’s just far easier to understand the consequences of your changes.

As a corollary, a terse code is NOT simpler code! Simple code is code that anyone can quickly understand, even less capable developers, and even yourself years after. Terse and “clever” code tends to be the exact opposite. Often times, the more verbose code is actually simpler because there are fewer assumptions and often less knowledge needed to understand it, less “code hoping” you have to do to follow things. Related to this is that writing less code isn’t AUTOMATICALLY better. No, you shouldn’t re-invent the wheel, but you also shouldn’t be AFRAID to invent a marginally better the wheel when it makes sense. Knowing the difference is hard of course and comes from experience, but if you think it’s ALWAYS better to write less code then you’re going to make your life harder in the long run.

Of course, don’t over-simplify code either. Too simple and suddenly extending it almost MUST mean a refactor. You never want to completely refactor because you HAVE to in order to build an app over time. There’s a balance that’s difficult to strike but it should always be the goal.

Oh yeah, and I wish I knew how to express myself in fewer words… but actually, I’m still obviously working on that one 🙂


Janic Duplessis

Janic is the co-founder of App & Flow, a react-native contributor, and open-source contributor.

Q: Any tips to becoming a better developer?

A: Don’t think there’s anything in particular, you just have keep learning and getting out of your comfort zone. Like trying a new language or framework from time to time. At least that’s what I do but I’m pretty sure there are some other good ways haha 🙂

Q: How can I start contributing to React Native?

A: The best is to start with something small like a bug fix or adding a small feature like an extra prop on a component. Most contributors know either iOS or Android and a bit of JS. There are also some JS devs that work on things like the package and clip. We keep some issues with a Good First Task label that should be a good place to start


Jake Murzy

Jake is an Open-source Archaeologist. He writes buzzword compliant code. Co-founder at @commitocracy.

Q: Hey Jake, any tips to becoming a better programmer? 🙂

A: Number one thing you should do is to learn your tools before you learn the language you work in because it will lead to faster feedback loops and you will get to experience more in less time. So install a linter and it will catch most of your errors as you type. It statically analyzes your code and recommends best practices to follow. You should always follow best practices until you gain enough experience to start questioning them.


Jun Ho Hwang

Jun is a software engineer at Coupang, which is the $5 Billion Startup Filling Amazon’s Void In South Korea. He is a very friendly developer who loves to connect.

Q: How do you become a better developer?

A: The word ‘better’ can be described in various ways–especially in the field of programming. A good developer could be someone who is exceptionally talented in development, someone who is amazing at communicating, or someone who understands Business very well. I personally think a “good” developer is someone who is in the middle–a person who can solve his or her business problem with their development skills, and communicate with others about the issue. Ultimately, to achieve this, it requires a lot of practice, and I recommend you to create your own service. Looking and thinking from the perspective of the user and improving the service to fulfill their needs really helps you grow as a better developer.

Q: Is there something you wish you’d known or learned earlier as a developer?

A: I really wish I started my own service earlier on. The hardest thing to grasp before developing is realizing how you can apply what you learned. Many developers are afraid to start a “service” because it sounds difficult; however, pondering about what to make and where to start, and then connecting those points of thought help you grow as a better developer.

Q: What do you do at Coupang? What are you currently working on?

A: Coupling provides a rocket-delivery-service, and I am working on developing a system called “Coupling Car,” which is related to insurance and monetary management. Furthermore, I’m thinking about adding transportation control system and the ability to analyze data from the log.


Keon Kim

Keon is a student at NYU who is really passionate about Machine Learning. He is a very active GitHub member who tries to contribute to open source projects related to machine learning.

Q: What are your interests? What kind of projects have you worked on?

A: I’ve been working on machine learning projects these days. I am one of the project members of DeepCoding Project, a project with a goal of translating written English to the source code. I’ve been contributing to a C++ machine learning framework called my pack(https://github.com/mlpack/mlpack), which is equivalent to skit-learn in Python.

I’ve also done some fun side projects: DeepStock (https://github.com/keonkim/deepstock) project is an attempt to predict the stock market trends by analyzing daily news headlines. CodeGAN (https://github.com/keonkim/CodeGAN) is a source code generator that uses one of the new deep learning methods called SeqGAN.

Q: How do you become a better developer?

A: I think it is really important to understand the basics. By basics, I mean math, data structures, and algorithms. Deep learning is really hot right now, and I see people jumping into learning it without basic knowledge in computer science and mathematics. And of course, most of them give up as soon as mathematical notations appear in the tutorial. I know this because I was one of them and it took me really long time to understand some concepts that students with a strong fundamentals could understand in a fraction of the time I spent. New languages, libraries, and frameworks are introduced literally every day these days, and you need the fundamentals in order to keep up with them.


Munseok Oh

Munseok is a Full-stack developer and CTO at Sketchware. He previously worked at System Integration for ~7 years.

Q: How do I become a better developer?

A: When I was very young and cocky, I evaluated other developers based on their coding style. There were certain criteria they had to pass in order for me to judge them as a good developer. But now, I really don’t think that way. Now, I believe that every developer is progressive, which means he or she is becoming a better developer every day. It doesn’t really matter if the style is bad or code is good–as long as the program runs, I think it’s great! Whether the program has room for growth or has bugs, I think the motivation to develop is what really matters. Developers usually are never satisfied with their skills. They are always eager to become better–probably why you’re doing this. It’s really hard to justify “good developer”. People like you will become better than me in no time. I still don’t think I am a good developer.

Q: What was the most difficult thing when you were developing Sketchware?

A: Developing Sketchware wasn’t too difficult because we had a good blueprint for the item. The direction was very clear for us to follow, so developing it was a breeze. However, there was a line we had to maintain for Sketchware–this line had two conditions:

  1. Sketchware must be an easy tool for anyone to create applications.
  2. Whatever the user takes away from Sketchware can be applied in their future career

Since we wanted Sketchware to be an efficient tool that can help users learn programming concepts, I am very considerate and think a lot when it comes to adding new features in the application.

Q: As a developer, is there something you wish you knew or fixed earlier?

A: I really wish I jumped into the Start-up world earlier. When it comes to developing, you need to be passionate and really enjoy what you do. Even if you pull 3 all-nighters, ponder all day long about a new algorithm, or stress about a new bug, everything will be okay if you’re enjoying it. It really goes back to the question #1–I get my energy from the joy I have when I develop, and that joy eventually makes you a better developer. When life hits you, most developers lose the passion for developing if you think of it as work. I used to be like that. But now, I’m really not worried–since developing brings joy to me now. Even if we run out of funds or our company burns down, it’s really okay since I am making the most out of what I am doing.


Satyajit Sahoo

Satyajit is the UX Lead at Glucosio, and Front-end Engineer at Callstack.io. He is an amazing open-source contributor; he is one of the top 5 contributors in React Native

Q: What is your background as a programmer?

A: I don’t really come from a programming background. I did my graduation in Forestry. I left post-graduation after getting a job offer and never looked back.

Q: What’s your day like on day to day basis?

A: It’s pretty boring. I wake up, order some breakfast online or go out, then start office work. In evening I go out to a bar or take a long walk if there’s enough time left. At night I mostly watch TV series or hack on side-projects.

Q: Motivation behind contributing to open source projects?

A: I’ve been involved in Open Source for a long time. When I was doing my graduation I got into Linux and got introduced to the world of Open Source. I loved it how we could learn so much from other projects. It fascinated me that developers were selfless to let us see and use the there code for free (mostly). I did a lot of Open Source projects in form of themes and apps during my college days, and it always made me happy when people forked them and changed to meet their needs, and send pull requests to fix things.

As a developer, I contribute to Open Source projects most of the time because I need a feature, or it improves something on a project I love. I think it’s better if we work together to fix stuff that is important to us rather than just filing issues.

Q: How do I become a better developer?

A: I think it’s important that we are open to new things. There’s a lot to learn, and we cannot learn if we stay in our bubble. Try new things, even if you think you can’t do it, even it looks complex on the surface. I have failed to do things so many times, but eventually succeed. In the process, I understand the problem and the solution, and then it becomes really simple.


Sonny Lazuardi Hermawan

Sonny is a JavaScript Full Stack Engineer, a React & React Native player, and an Open source enthusiast. He currently works as an Engineer at Sale Stock.

Q: How do you become a better developer?

A: I think always eager to learn is the key. Try everything, make mistakes, and learn from that mistakes. I agree that code review from partners and senior engineers will make our code better. Try publishing your own open source projects, meet other great developers and learn from them.

Q: What’s your motivation behind creating open source projects?

A: I just want the people to know about our idea, and try implementing it so that others can use our project. I’m really inspired by people that work on open source projects that used by many devs such as Dan Abramov that created redux.


Sunggu Hwang

Sunggu worked at Daum Communications for 4 years. Then, he left Daum to work at Scatter Lab as the CTO. This is his 5th year at Scatter Lab.

Q: How do you become a better developer?

A: Hmm… Becoming a good developer… Every developer has his or her own personality when it comes to programming. As an analogy, think about blacksmiths! Not all blacksmiths are alike–some enjoy crafting the best sword, while some might enjoy testing out the sword more than crafting it. I am a thinker–who plans and organizes thoughts before I carry out an action. I think a good developer knows how to write concise and clean code; you should practice this habit. Even though the trend for programming is always changing, and many people use different languages, write a piece of code that anyone can understand without comments.

Q: What do you think is the next BIG thing?

A: I’ve observed the evolution of programming languages, and I think it’s becoming more abstract every generation–procedural programming, imperative programming, functional programming… I think in the future, maybe in about 20 to 30 years, we will live in the time where the computer writes the code for us, and we just put them together like legos.

Q: What should I focus on studying?

A: I think deep learning is a must. Try different tutorials and learn it with passion. Math, algorithms–anything will help you in the long run.


Timothy Ko

Timothy is a software engineer at Snapchat. He previously worked at many places such as Riot Games, Square, etc.

Q: What do you do at Snapchat?

A: I’m a software engineer on the monetization team, so I work on anything related to making money. Some example projects are Snapchat Discover, a news platform within the iOS and Android apps; Ad Manager, a control panel used by sales and ad operations to flight ads; Ads API, which allows third-party partners to integrate their own ad platforms into Snapchat. Also, I was a past intern at Snapchat so I occasionally give talks and Q&As to upcoming interns. I’m also heavily invested in hiring and conduct a lot of interviews there.

Q: What do you do on a day-to-day basis?

A: What I’ve mentioned previously. Also, even after I pass on the work to other people, sometimes I have to go back and help support it or be part of the technical discussions on future changes. When new people join the team, usually I’m the one to ramp people up on how the code base looks like the kinds of frameworks we use, how a typical engineer workflow looks like, etc.

Q: What languages/framework do you guys mostly use?

A: For server code, it’s usually Java and for UI we use React Redux. Most teams work in google app engine, which is why we use Java, but some teams switch it up a little bit due to some app engine limitations. And of course, the product teams work in objective C for iOS and Java for Android.

Q: How do you think I can become a better developer?

A: I think the best thing to do is to do as many things as possible. I did seven internships while in school so I already had two years of work experience before I graduated. Work experience is super important because coding in a hackathon, doing personal projects, and doing school assignments are totally different than working with enterprise software and apps with real users. But you have to start somewhere, so that’s where going to school, doing personal projects, and competing in hackathons comes in. And while at work, I think the best way to succeed is to ask lots of questions and learn by doing. You can read and study all you want, but you might not understand what’s going on until you actually do it. Another thing is code reviews — you can do so much knowledge transfer by having a more senior engineer tear your code apart and tell you how to make it better. Also, if you ever come up with a proposal on how to solve a problem, getting a tech lead to bombard you with hard questions forces you to make sure you have every little detail covered.


*The article was originally posted by Sung Park on Github*

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This guide covers how AI candidate screening works, where it underperforms, how to evaluate vendors against your ATS (Workday, Greenhouse, Lever, SmartRecruiters), and what compliance frameworks such as NYC Local Law 144 and the EU AI Act require before deployment.

Recruiter Time Allocation by Task
Source: LinkedIn Future of Recruiting Report, 2024; remaining categories illustrative based on article claims

Why resume-only screening breaks at scale

Resume screening was designed for a hiring environment that no longer exists. Recruiters reviewed education, work history, certifications, and keywords to determine whether an applicant should move forward.

The problem is that resumes were never designed to measure skills. A candidate may list Python, Java, or "cloud infrastructure" without being able to apply any of them; conversely, capable candidates get filtered out because their resumes don't hit keyword thresholds. Research summarized by SHRM and McKinsey consistently points to the weak predictive validity of unstructured resume review for job performance.

At high volume, this gets worse. When a recruiter has to clear 400 applications for one role in a week, decisions collapse toward surface signals — school name, employer brand, keyword density — rather than validated capability.

This is also why skills-based hiring frameworks such as O*NET and SFIA have gained traction: they give TA teams a structured vocabulary for what a role actually requires, which is a prerequisite for any AI screening system to score against.

Comparison of traditional resume screening and AI candidate screening workflows
Figure 1: Traditional screening centers on resume review; AI candidate screening incorporates additional candidate signals such as assessments and structured evaluations. Source: HackerEarth.
Dimension Traditional screening AI candidate screening
Primary input Resume, cover letter Resume + assessment data + structured interview signals
Evaluation basis Keywords, credentials Demonstrated skills, scored responses
Consistency Varies by recruiter Rubric-based, auditable
Scalability Linear with headcount Handles high-volume events (e.g., campus, RIF backfill)
Reporting Manual funnel metrics Funnel conversion, slate diversity, time-to-shortlist
Time-to-Shortlist: Manual vs. AI Screening at High Volume
Source: Illustrative based on article claims (days to shortlist)

What AI candidate screening actually is

AI candidate screening is the application of machine learning and rules-based automation to evaluate, prioritize, and organize candidates in the early stages of a hiring funnel.

Depending on the platform, an AI screening system may score resumes, application answers, assessment results, coding submissions, or recorded interview responses against a role-specific rubric. The output is typically a ranked shortlist plus explanations of why each candidate scored where they did.

The point is not to replace recruiter judgment. It is to reallocate recruiter time from administrative triage to candidate evaluation, and to make the triage step auditable enough that a Head of TA can defend the funnel to a CHRO or a regulator.

Modern AI screening tools generally integrate with an ATS such as Workday, Greenhouse, or Lever, and increasingly sit alongside skills assessments and structured interview platforms rather than replacing them.

How AI screening works in a technical hiring funnel

An AI candidate screening workflow begins when a candidate enters the funnel — application, referral, sourcing campaign, or talent community. From there:

  1. Ingest. Application data and resume are parsed and normalized against role criteria.
  2. Signal collection. For technical roles, the workflow adds skills assessments, coding challenges, or structured interview scores.
  3. Scoring. Each candidate is scored against a rubric derived from the job's must-have and nice-to-have skills.
  4. Ranking and explanation. Recruiters see a ranked slate with the reasoning behind each score, not just a number.
  5. Human review. Recruiters and hiring managers make the shortlist decision using the AI output as one input among several.

For TA leaders managing high-volume or campus hiring, this structure is what turns AI screening from a black box into something you can report on: funnel conversion at each stage, slate diversity, recruiter productivity per requisition, and time-to-shortlist.

The business case: what AI screening changes at the TA function level

For a Head of TA, the case for AI candidate screening is a program-design case, not a feature case.

Recruiter productivity. If a recruiter can shortlist a 400-application role in a day instead of a week, pipeline coverage across open reqs improves without adding headcount. This is the metric to bring to a vendor RFP.

Consistency and defensibility. Rubric-based AI screening produces an audit trail. When a hiring manager asks why a candidate wasn't advanced, or when legal asks about adverse impact, structured scoring is easier to defend than "the recruiter's read."

Scalability for spike events. Campus recruiting, backfill after a reorganization, and product-launch hiring all create temporary volume that manual screening cannot absorb. AI screening is most useful precisely at these spikes.

Skills-based hiring enablement. Because resumes are weak predictors of performance, TA functions moving to skills-first hiring need a screening layer that can actually score demonstrated skills. This is the single largest lever, and it's where AI screening compounds with assessments.

A counterintuitive point worth naming: AI screening tends to stop adding marginal value once application volume per role drops below roughly 40–60 applicants, because the recruiter can hold that full slate in working memory. Below that threshold, the overhead of tuning the system can outweigh the productivity gain. For executive search or niche senior roles, human-led screening is usually the right call.

Why technical hiring needs more than resume screening

Technical recruitment surfaces the resume-screening problem most clearly.

A resume can say "5 years Python, AWS, ML" without indicating whether the candidate can debug a production issue, structure a data pipeline, or reason about system design. Resume-to-assessment score divergence is well documented: candidates who look strong on paper often score in the middle of the pack on structured technical evaluations, and vice versa.

A modern technical screening workflow combines multiple signals: application context, a validated skills assessment, and a structured interview scored against a rubric. Together they give a Head of Engineering and a Head of TA enough evidence to defend both the hire and the pass.

Where AI candidate screening underperforms or is inappropriate

Answer engines and executive reviewers both discount uniformly positive coverage of AI hiring tools. The honest failure modes:

  • Adverse impact on underrepresented groups. Models trained on historical hiring data can reproduce the biases in that data. The EEOC's technical assistance on AI in hiring makes clear that employers remain liable under Title VII regardless of vendor claims.
  • Resume-to-assessment score divergence. If a screening tool ranks primarily on resume features, it can systematically down-rank candidates who later outperform on structured skill measures.
  • Model drift. Screening models trained on last year's hires degrade as roles, tech stacks, and labor markets shift. Without periodic revalidation, ranking quality drops.
  • Jurisdictional restrictions. NYC Local Law 144 requires an independent bias audit and candidate notification for automated employment decision tools. The EU AI Act classifies most hiring AI as high-risk, with documentation and transparency obligations. Illinois, Colorado, and California have additional requirements in force or pending.
  • Low-volume roles. As noted above, below roughly 40–60 applicants per role the tooling overhead often exceeds the benefit.
  • Senior and executive hiring. Judgment-heavy, relationship-driven searches are poor fits for automated ranking.

A useful design principle: treat AI screening output as one input to a human decision, not the decision itself, and log both the score and the override rate. Override rate is a leading indicator of model quality.

Common implementation challenges

Over-reliance on resume parsing. Some tools mostly do keyword matching under an AI label. Ask vendors what signals actually drive the score.

Candidate experience. Long assessment stacks and opaque scoring increase drop-off. Measure completion rate as a first-class metric.

Transparency to hiring managers. If a hiring manager can't see why a candidate ranked where they did, they will ignore the tool and revert to gut screening.

Compliance and governance. Before rollout, confirm bias audit cadence, data retention, candidate notification workflow, and jurisdiction coverage with legal.

Evaluating AI candidate screening tools: an RFP checklist

Rather than a feature list, use these questions in a vendor RFP:

  • What specific signals drive the candidate score, and can you show a sample explanation for a real ranking?
  • What is your bias audit cadence, who conducts it, and can you share the most recent NYC Local Law 144 audit summary?
  • How does the system handle model drift, and how often is the model revalidated against outcome data?
  • What is your integration depth with our ATS (Workday, Greenhouse, Lever, SmartRecruiters), and does data flow both ways?
  • What funnel and slate-diversity metrics are exposed for executive reporting?
  • What is the assessment completion rate benchmark for candidates in our role families?
  • For technical roles, can the platform administer and score coding evaluations at scale, and what is the largest single event you have supported?

How HackerEarth fits into an AI candidate screening program

HackerEarth's assessment and interview stack is built for technical hiring at scale, and slots into an AI screening program as the skills-signal layer that resume-based tools can't produce on their own.

HackerEarth Assessments covers 1,000+ skills across 40+ programming languages, with role-specific tests, coding challenges, and project-based evaluations that give recruiters a validated signal beyond the resume. Discover Dollar, for example, used HackerEarth to run assessments for 2,000 candidates in a single weekend — the kind of scale that manual screening cannot absorb.

FaceCode provides structured, rubric-scored technical interviews with live coding, so the interview stage produces the same auditable signal as the assessment stage.

OnScreen (launched April 14, 2026, currently available to enterprise customers with pilot access at hackerearth.com/ai/onscreen) is an AI interview tool that conducts structured technical interviews 24/7 using video-avatar interviewers with built-in identity verification. It is designed for high-volume top-of-funnel technical screening where scheduling human interviewers is the bottleneck.

Across these products, HackerEarth serves 500+ global enterprises and a 10M+ developer community, which is the dataset behind the skills taxonomy and role benchmarks.

HackerEarth Assessments, FaceCode, and OnScreen mapped to stages of the technical hiring funnel
Figure 2: HackerEarth Assessments, FaceCode, and OnScreen mapped to stages of a technical hiring funnel. Source: HackerEarth.

Frequently asked questions

How does AI candidate screening work? AI candidate screening ingests applications and additional signals (assessments, structured interview scores), scores each candidate against a role-specific rubric, and returns a ranked, explainable shortlist to the recruiter. A human still makes the shortlist decision.

Is AI candidate screening biased? It can be. Models trained on historical hiring data can reproduce historical bias, and the EEOC has clarified that employers remain liable under Title VII regardless of vendor claims. Regular independent bias audits — required under NYC Local Law 144 for tools used on NYC candidates — and monitoring adverse impact ratios are the standard mitigations.

Is AI candidate screening legal? It is legal in most jurisdictions but increasingly regulated. NYC Local Law 144 requires bias audits and candidate notification. The EU AI Act treats most hiring AI as high-risk. Illinois, Colorado, and California have additional obligations. Confirm coverage with legal before deployment.

What is the best AI screening software for technical hiring? The right tool depends on volume, role mix, and ATS. For technical hiring specifically, look for validated skills assessments, coding evaluation at scale, structured interview scoring, and native integration with your ATS. HackerEarth Assessments, FaceCode, and OnScreen are built for this use case.

When does AI candidate screening stop adding value? Below roughly 40–60 applicants per role, or for senior and executive searches, the overhead of tuning and monitoring the system often outweighs the productivity gain. Reserve AI screening for high-volume and repeatable role families.

How do I measure whether AI candidate screening is working? Track time-to-shortlist, recruiter productivity per requisition, funnel conversion by stage, slate diversity, assessment completion rate, override rate (how often recruiters overrule the AI ranking), and quality-of-hire at 6 and 12 months.

Next steps

If you're evaluating AI candidate screening for a technical hiring program, the fastest way to pressure-test whether it fits your funnel is to run a scoped pilot against one high-volume role family.

Request a HackerEarth demo to see Assessments, FaceCode, and OnScreen against your own role requirements, or explore OnScreen pilot access if 24/7 structured technical interviews are your current bottleneck.

How AI-Generated CVs Are Breaking Technical Hiring (and What Actually Works Now)

How AI-Generated CVs Are Breaking Technical Hiring (and What Actually Works Now)

AI-generated CVs are breaking technical hiring by flooding the top of the funnel with resumes that look qualified, read as tailored, and often fail to reflect actual technical ability. The problem isn't simply more applications it's lower-quality hiring signals at much higher volume.

Many hiring teams responded by tightening resume filters. Unfortunately, that only delays the problem. If resumes are already an unreliable signal, adding more resume-based screening simply pushes poor matches further into recruiter screens, technical interviews, and engineering calendars.

What "AI-Generated CVs" Means in 2026

Not every AI-assisted resume represents the same challenge.

Tailored writing refers to candidates using AI tools to rewrite an accurate resume for a specific job description. The experience is genuine; AI simply improves presentation.

Inflated writing is more problematic. Candidates exaggerate projects, technical depth, or ownership using AI, creating resumes that appear impressive but don't hold up during interviews.

Fully synthetic applications involve fake identities, automated submissions, or proxy candidates attempting to move through the hiring process. While less common, they create significant hiring risk.

According to LinkedIn's Future of Recruiting report, AI is rapidly changing how candidates apply for jobs. As application volumes rise, many organizations are seeing resume quality decline rather than improve.

Why Resume Screening Isn't Working Anymore

Resume screening has always been an imperfect predictor of technical ability. What has changed is how easy it has become to create an optimized resume.

Today, candidates can generate resumes that closely match job descriptions within minutes. Keyword-based ATS filters often rank these resumes highly, even when the underlying skills don't match the role. As a result, recruiters spend more time reviewing candidates who appear qualified on paper but struggle during technical evaluations.

What Actually Works

Organizations seeing the best hiring outcomes are shifting their focus from resumes to stronger evaluation signals.

Start with Skills

Instead of reviewing resumes first, many teams now begin with a role-specific technical assessment. The assessment becomes the primary hiring signal, while the resume provides supporting context rather than acting as the initial filter.

Design AI-Friendly Take-Home Assignments

Rather than trying to prevent AI use, successful teams design assignments that assume candidates will use AI. Evaluation focuses on decision-making, technical reasoning, and the candidate's ability to explain trade-offs instead of whether AI helped write the code.

Standardize Technical Interviews

Structured interviews improve consistency by ensuring every candidate is evaluated using the same questions, scoring criteria, and rubrics. For remote hiring, identity verification also helps reduce proxy interview risks.

Review Every Signal Together

Strong hiring decisions rarely come from a single assessment. Teams that review technical assessments, interviews, take-home assignments, and recruiter feedback together are better able to distinguish genuine talent from polished resumes.

Where the Impact Is Greatest

The effects of AI-generated resumes vary across hiring scenarios. High-volume campus hiring often struggles with resume inflation, making skills assessments especially valuable. Remote senior engineering hiring faces greater risks from proxy candidates, while regulated industries require structured, well-documented hiring processes that can withstand audits.

What to Avoid

Adding more resume filters rarely improves hiring quality. AI detection tools continue to produce unreliable results, and requiring cover letters simply encourages candidates to generate more AI-written content. Likewise, "AI-proof" assessment questions often frustrate genuine candidates without preventing misuse.

Key Takeaways

AI-generated resumes have fundamentally changed technical hiring by reducing the reliability of resume-based screening. Organizations that shift toward skills-first assessments, structured interviews, and evidence-based hiring decisions are better equipped to identify genuine technical talent while delivering a fairer candidate experience.

Vibecoding Assessment: 2026 Guide for Engineering Teams

What Is Vibecoding? A 2026 Guide to Vibecoding Assessment for Engineering Teams

A vibecoding assessment — an evaluation of how candidates collaborate with AI coding assistants to build software — has emerged as a distinct hiring signal in 2026, separate from traditional algorithmic screens. Vibecoding itself is the practice of building software by directing an AI model in natural language: describing intent, reviewing generated code, refining prompts, and shipping working software instead of manually writing most of the code. As of 2026, a growing number of engineering teams are treating vibecoding assessment as a core part of technical hiring.

The term originated with Andrej Karpathy's February 2025 post on X describing the experience of "giving in to the vibes," where AI handles most of the typing while the developer focuses on direction, review, and decision-making.

Engineering teams are incorporating vibecoding into hiring because software development itself has changed. GitHub's 2024 Octoverse Developer Survey found that a large majority of surveyed developers (reported as more than 97%) had used AI coding tools at work, and Stack Overflow's 2024 Developer Survey reported that 76% of developers are using or planning to use AI tools in their development process (figures should be re-verified against the primary source before publication). Some practitioners report that senior engineers who cannot effectively use AI coding assistants are becoming less productive than peers who can, though this observation is largely anecdotal at this stage. At the same time, candidates who rely entirely on AI without understanding the generated code create risks that traditional coding interviews do not measure well.

This guide explains what vibecoding is, what companies should evaluate, where a vibecoding assessment fits into the hiring funnel, and the trade-offs teams should consider. It's written primarily for engineering managers and technical hiring leads designing AI coding assessment and AI coding interview workflows for AI-native development.

What a Vibecoding Assessment Measures vs. Traditional Coding Interviews
Source: Illustrative based on article framework; 1 = measured by traditional interview, 0 = not measured by traditional interview

Defining vibecoding

Vibecoding is a workflow, not a tool.

Developers work inside AI-powered coding environments — the current market includes tools like Cursor, Windsurf, Claude Code, and GitHub Copilot Workspace, among others (listed as factual acknowledgment of the tooling landscape, not as endorsed alternatives). Instead of writing every line manually, they describe the problem, review AI-generated code, refine prompts, debug mistakes, and ship working code.

The AI generates much of the code, but the developer remains responsible for intent, architecture, validation, debugging, and overall code quality.

Core skills behind vibecoding

Effective AI-assisted developers consistently demonstrate four measurable skills.

Prompt specificity

They know how much context and which constraints to provide so the AI produces useful output.

Output review

Strong developers quickly identify hallucinated APIs, logic errors, security concerns, poor abstractions, and missing edge cases instead of trusting AI blindly.

Iteration control

They understand when to refine a prompt, edit code manually, or discard the AI's output and start over.

Scope discipline

They keep the AI focused on the current task instead of allowing it to rewrite unrelated parts of the codebase. In practice, scope discipline may be a stronger hiring signal than prompt quality — strong prompts are easy to imitate, but consistent scope control under time pressure reveals engineering judgment.

Why traditional technical assessments miss these skills

Most technical interviews were designed for a world where candidates manually wrote every line of code. Today's workflow looks different.

Take-home assignments no longer measure the right thing because AI assistance has become commonplace. The real question is no longer whether candidates use AI, but how effectively they use it.

Similarly, anti-AI proctoring methods like browser lockdowns or disabled copy-paste simulate outdated workflows rather than real engineering environments.

Algorithm-based interviews also measure less than they once did. AI models can often solve many standard algorithm challenges from memory, so memorizing textbook solutions has become a weaker predictor of on-the-job performance. In our experience, HackerEarth's technical assessment library has been moving toward more scenario-based problems for this reason.

What a vibecoding assessment should measure

A well-designed vibecoding assessment gives candidates access to an AI coding assistant, a realistic engineering task, a fixed time limit, and visibility into their workflow.

Rather than evaluating only the final submission, interviewers should assess how candidates approach the problem.

They should observe whether candidates break complex problems into manageable steps, write clear and context-rich prompts, carefully review AI-generated code, iterate intelligently when things go wrong, and ultimately deliver code that is reliable and maintainable.

Some practitioners report that output review and iteration strategy often provide stronger hiring signals than the final implementation itself — a contestable claim, but one that anecdotally holds up when interviewers review recorded sessions.

Where a vibecoding assessment fits in the hiring funnel

Organizations are adopting vibecoding assessment workflows in several ways.

Some companies are replacing lengthy take-home assignments with 60–90 minute AI-assisted coding sessions where interviewers observe both the candidate's workflow and final solution. As an illustrative example, one mid-sized fintech engineering team described (in an interview with our team) replacing an eight-hour take-home with a 75-minute AI-assisted screen and reported meaningfully reduced top-of-funnel drop-off, along with faster time-to-hire, because candidates preferred the shorter format. This is presented as directional feedback, not a benchmark.

Others keep a traditional coding screen to evaluate core problem-solving skills before introducing a dedicated AI coding interview round.

For senior engineering roles, companies increasingly conduct collaborative pair-programming sessions where the hiring manager, candidate, and AI assistant solve realistic engineering problems together. Many teams find this approach produces stronger hiring signals because it closely mirrors day-to-day work.

Challenges of vibecoding assessments

Like any interview method, a vibecoding assessment comes with trade-offs.

Evaluating AI-assisted workflows is inherently more subjective than grading algorithm questions, making clear rubrics and reviewer calibration essential. This is one reason rubric-based leaderboards — which turn subjective review into structured, comparable scoring — have become a common approach for teams building out AI coding assessment programs.

AI coding assistants also evolve rapidly, so assessments should be reviewed and updated regularly to stay relevant.

Another consideration is candidate familiarity with AI tools. Whenever possible, organizations should provide a standardized environment and clearly explain which tools are available during the interview.

Finally, AI cannot replace engineering fundamentals. Candidates still need strong knowledge of data structures, databases, system design, debugging, and software architecture. A vibecoding assessment should strengthen technical assessments — not replace them. It's worth noting a contestable prediction here: some argue vibe coding interviews will replace whiteboard interviews within two years. That view understates how much system design and architectural reasoning still matter for senior roles, and we expect whiteboard-style interviews to persist for design rounds well beyond 2028.

How HackerEarth supports AI-assisted hiring

Two HackerEarth products map most directly to the workflow described above. VibeCode Arena is a hands-on practice environment where developers can work across multiple LLMs, with rubric-based leaderboards that generate data usable for AI literacy programs, LLM selection, and L&D calibration — directly addressing the subjectivity problem raised in the Challenges section by turning reviewer judgment into structured, comparable scoring. For live whiteboarding or extended pair-programming with the hiring team — the senior-role scenario described above — FaceCode is the collaborative interviewing product, and it pairs naturally with Skill Assessments that measure the foundational engineering knowledge which remains essential regardless of AI adoption.

Frequently asked questions

Is vibecoding just prompt engineering?

No. Prompt engineering is only one part of the workflow. A vibecoding assessment also evaluates reviewing AI-generated code, debugging, managing iterations, and maintaining scope throughout development.

How long should a vibe coding interview be?

Many teams find 60–90 minutes works well for mid-funnel screens, where the goal is to observe the full loop of prompt, review, and iteration. Senior pair-programming interviews are often structured tighter — around 45–60 minutes — not because seniors need less time, but because the interviewer is present to steer the session, so less unstructured exploration is required. Both durations are practitioner conventions rather than fixed rules; calibrate to your role and rubric.

Can candidates game an AI coding assessment?

It is harder than gaming take-home assignments, primarily because prompt history and iteration steps are captured in real time. That makes post-hoc rationalization visible: a candidate who cannot explain why they refined a prompt a certain way, or who accepts obviously flawed AI output without comment, is easy to spot in the recording. Rotating assessment tasks regularly further reduces the risk.

Should junior candidates also use AI?

Yes, but fundamentals should carry greater weight. Junior engineers are more likely to accept incorrect AI output without sufficient verification, making foundational knowledge especially important.

What changes for senior engineers?

Senior interviews become less about scoring isolated coding tasks and more about collaborative engineering. Interviewers focus on technical judgment, AI collaboration, code review skills, and communication.

Key takeaways

Vibecoding reflects how software is increasingly built in 2026. The strongest AI-assisted developers know how to guide AI effectively, critically review its output, iterate intelligently, and maintain code quality. Traditional coding interviews miss many of these capabilities, making a vibecoding assessment a useful addition to hiring. When combined with strong evaluations of engineering fundamentals, vibe coding interviews provide a more complete picture of candidate ability.

Try VibeCode Arena for AI literacy and LLM calibration

CTA: If you're building AI literacy programs or calibrating LLM choice for your engineering org, request a VibeCode Arena walkthrough to see how rubric-based leaderboards can support your team's AI adoption.

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