Trending March 2024 # Don’t Like Coding? Here Are The Top 10 Tech Jobs That Don’t Require Coding # Suggested April 2024 # Top 11 Popular

You are reading the article Don’t Like Coding? Here Are The Top 10 Tech Jobs That Don’t Require Coding updated in March 2024 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested April 2024 Don’t Like Coding? Here Are The Top 10 Tech Jobs That Don’t Require Coding

There are hundreds of non-coding tech jobs across various tech firms, here are the top 10 job types

Tech jobs are offering much-needed cushion by opening doors to new positions and opportunities. Many IT companies are looking for talents as the demand for products and services has been gaining momentum with the shift towards digital technology and services. There are hundreds of tech jobs across various tech firms, that are indeed willing to pay a premium to retain and hire talents. Now, you must be thinking that for every tech job you need to know coding, right? But not always. This article lists the top 10 tech jobs that don’t require coding.

User Experience (UX) and User Interface (UI)

One of the non-programming technical jobs is in User Experience (UX) and User Interface (UI) development. User Interface and User Experience is a field that helps companies make products that people enjoy using. UX and UI positions are different, but they work closely together. UX designers aim to create the best feel for a website or mobile app, making sure that everything works as it should. UI specialists create visual designs and craft interfaces for websites or mobile apps.

QA Engineer

Quality Assurance engineers and QA analysts are responsible for testing the application. There are manual QA engineers as well as Automation QA engineers. Once the development of a feature is complete, the QA engineers perform various levels of testing and identify potential issues with the software. It does not involve heavy coding. Every application requires testing, and QA engineers are responsible for the overall quality of the product.

Information Architect

An information architect, a no-coding tech job, is an amalgamation of design and user interface experts at its primary level. The information architect would focus on improvising the experience while also optimizing the usability of the platform and website.

Project Manager

Project managers in the information technology (IT) world are tasked with planning projects. They ensure that these projects are executed on time and follow the roadmap through every stage of the process. This job requires daily evaluation of employees, as well as leadership and motivation skills to be successful. IT project managers must ensure that team members have the same shared vision and goals for projects. 

Web Analysis Specialist IT Business Analyst

Business Analysts are the individuals who determine the requirements of tools and processes for project completion. They actively assess its current operations, systems, products, and services and suggest cost-saving, higher efficiency strategies. 

Technical Writer

If you are interested in doing in-depth analysis on any topic, comfortable with technical terms, and willing to create engaging content. In that case, technical writing is the job for you.

Graphic Designer

Graphic designers are the creative staff that formulates and pitches graphic concepts to clients. They are responsible for developing, designing, and producing graphic art that meets the client’s demands. They have a good knowledge of typography, color, and production. The skills needed vary from creativity and versatility to deep knowledge of branding and marketing techniques.

Support Specialist

IT Support Specialists care for employees’ and customers’ hardware, software, and other networks/connectivity problems. Choosing this career would require a thorough understanding of different tech products, broad knowledge of software and network problems, and superior communication skills.

Data Analyst

If you love to play with data and are looking forward to getting a tech job that doesn’t require coding skills, then becoming a Data Analyst would be the perfect career option for you. Being logical and hyper-focused on finding patterns is extremely useful. If you’re good at taking the big picture and breaking it down into individual components, you may want to look into this ever-growing field.

You're reading Don’t Like Coding? Here Are The Top 10 Tech Jobs That Don’t Require Coding

Generative Ai Coding: Top 10+ Use Cases & 4 Tools In 2023

Software development tends to take longer and cost more than expected.

Though generative AI tools have potential to accelerate the software development process, they come with challenges such as hallucination which can slow down coding or introduce critical issues to software.

To help technical leaders form their generative AI approach about software engineering, we will

Explain the top 3 use case areas of generative AI in coding

Highlight 4 generative AI coding tools

Provide best practices

Generative AI in Requirements Analysis

Without clear requirements, software development process cannot start. Generative AI applications in requirements analysis include:

1- Generating requirements

Analysts can prompt their high level requirements to fine-tuned large language models that have been exposed to best practice requirements documents.

2- Requirement completion

Analysts may forget important requirements (e.g. regarding security) which can be completed.

Generative AI in Coding 3- Code generation

Writing code with generative AI is possible through a technique known as neural code generation. This involves training a neural network on a large dataset of code examples, and then using the fine tuned network to generate code that is similar in structure and function to the examples it has been trained on.

Figure 1. Generating a simple Python script with OpenAI’s ChatGPT

4- Code completion 

One of the most straightforward uses of generative AI for coding is to suggest code completions as developers type. This can save time and reduce errors, especially for repetitive or tedious tasks.

5- Code review 

Generative artificial intelligence can also be used to make the quality checks of the existing code and optimize it either by suggesting improvements or by generating alternative implementations that are more efficient or easier to read.

Figure 2. The mechanism of Amazon’s CodeWhisperer for reviewing code

Source: Amazon

6- Bug fixing

It can help identify and fix bugs in the generated code by analyzing code patterns, identifying potential problems, and suggesting fixes.

Figure 3. ChatGPT is fixing a Python code that tries to calculate factorial of a number but runs into an error

7- Code refactoring

It can be used to automate the process of refactoring code, making it easier to maintain and update over time.

8- Style improvement

It can analyze code for adherence to coding style guidelines, ensuring consistency and readability across a codebase.

Generative AI in Testing

Testing is critical in software and generative AI can support testing in a similar way to its support for software coding. Generative AI applications in test automation and testing include:

9- Generating test cases 10- Generating test code 11- Test script maintenance 12- Test data generation 13- Test documentation 14- Test result analysis

Solutions are sorted by their introduction date, starting from the first introduced solution:

1- GitHub Copilot

GitHub Copilot, Microsoft’s AI system to write code, is an AI-powered code suggestion and generation tool developed in collaboration with OpenAI. It uses machine learning models trained on a vast corpus of public code to suggest code snippets and even entire functions as developers type.

 Figure 3. Survey responses measuring dimensions of developer productivity when using GitHub Copilot

Source: GitHub Blog

According to executives at GitHub and other companies, the aim of these tools is not to substitute developers, but to enhance their efficiency, similar to the way spell check and phrase auto-completion tools help people in writing documents.

2- ChatGPT

As an AI model developed by OpenAI, ChatGPT does not have a specific code generation function. However, as a language model trained on a large corpus of text data, it can generate text in natural language, which includes code snippets or examples, when prompted to do so.

For example, if a user asks “Can you provide an example of a Python function that calculates the sum of two numbers?” ChatGPT can generate a code snippet in response, such as in Figure 4.

Figure 4. An example of ChatGPT generating code for the given prompt and explaining it

While ChatGPT is not specifically designed for generating code, it can be trained on a dataset of code examples to improve its ability to generate code snippets and functions that are syntactically correct and functionally valid. However, it’s important to note that the quality of the generated code may vary depending on the quality and quantity of the training data and the complexity of the task being performed.

ChatGPT could be a good debugging companion; it not only explains the bug but fixes it and explain the fix 🤯 chúng tôi Amjad Masad ⠕ (@amasad) November 30, 2023

3- CodeWhisperer

Amazon’s CodeWhisperer is a code generation tool that utilizes diverse data sources, including chúng tôi and open-source code, to produce code that imitates the way a developer would write it. 

4- IBM Watson Code Assistant

Though not yet released to the public, Watson Code Assistant relies on IBM chúng tôi foundation models to generate code.

For other code generator tools and information about their features and pricing, you can check our generative AI tools article.

Best practices for software development with generative AI 1- Address Intellectual Property (IP) concerns

For IP of code generated by LLMs to be deployed in a commercial software, these are the minimum requirements from a generative AI IP perspective:

Its codebase needs to be licensed to the end user (potentially via the developer of the LLM). There is an ongoing lawsuit about the use open source code in Github’s Copilot.[etfn_note]Matthew Butterick.”GitHub Copilot litigation“.Accessed May 28, 2023[/etfn_note]

LLM to be used should have a commercial license. For example, some popular open source LLMs lack commercial licenses.

2- Ensure data security of code

Enterprises have already leaked valuable source data while using generative AI applications.

3- Follow software development best practices

Like humans, software makes mistakes. Software engineering discipline has produced hundreds of paradigms like pair programming to improve the quality and effectiveness of software. They can help minimize the downsides of generative-AI-powered software development.

For more, check out AIMultiple’s guidelines for enterprise generative AI models.

Coding is one of many generative AI applications. For applications in other industries and business functions, check out:

If you have questions or need help in finding vendors, reach out:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Steve Jobs Top 10 Quotes

5 Management Mantras

#10. On Management

My job is to not be easy on people. My job is to make them better. My job is to pull things together from different parts of the company and clear the ways and get the resources for the key projects.

And to take these great people we have and to push them and make them even better, coming up with more aggressive visions of how it could be.

#9. On Hiring

Recruiting is hard. It’s just finding the needles in the haystack. You can’t know enough in a one-hour interview.

So, in the end, it’s ultimately based on your gut. How do I feel about this person? What are they like when they’re challenged? I ask everybody that: ‘Why are you here?’ The answers themselves are not what you’re looking for. It’s the meta-data.

#8. On Firing

We’ve had one of these before, when the dot-com bubble burst. What I told our company was that we were just going to invest our way through the downturn, that we weren’t going to lay off people, that we’d taken a tremendous amount of effort to get them into Apple in the first place — the last thing we were going to do is lay them off.

#7. On a CEO succession Plan

I mean, some people say, ‘Oh, God, if [Jobs] got run over by a bus, Apple would be in trouble.’ And, you know, I think it wouldn’t be a party, but there are really capable people at Apple.

My job is to make the whole executive team good enough to be successors, so that’s what I try to do.

#6. On Product Strategy

It’s not about pop culture, and it’s not about fooling people, and it’s not about convincing people that they want something they don’t. We figure out what we want. And I think we’re pretty good at having the right discipline to think through whether a lot of other people are going to want it, too. That’s what we get paid to do.

We just want to make great products. (I think he means “insanely great products!“)

5 Leadership Mantras

#5. On Leadership

So when a good idea comes, you know, part of my job is to move it around, just see what different people think, get people talking about it, argue with people about it, get ideas moving among that group of 100 people, get different people together to explore different aspects of it quietly, and, you know – just explore things.

#4. On Evangelism

When I hire somebody really senior, competence is the ante. They have to be really smart. But the real issue for me is, Are they going to fall in love with Apple? Because if they fall in love with Apple, everything else will take care of itself.

They’ll want to do what’s best for Apple, not what’s best for them, what’s best for Steve, or anybody else. (this actually reiterates my oft-repeated mantra of “ubiquitous evangelism” in companies)

#3. On Focus

People think focus means saying yes to the thing you’ve got to focus on. But that’s not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully.

#2. On the User Experience

Our DNA is as a consumer company — for that individual customer who’s voting thumbs up or thumbs down. That’s who we think about. And we think that our job is to take responsibility for the complete user experience. And if it’s not up to par, it’s our fault, plain and simply.

#1. On Creativity

That happens more than you think, because this is not just engineering and science. There is art, too. Sometimes when you’re in the middle of one of these crises, you’re not sure you’re going to make it to the other end. But we’ve always made it, and so we have a certain degree of confidence, although sometimes you wonder.

I think the key thing is that we’re not all terrified at the same time. I mean, we do put our heart and soul into these things.

And, my favorite, which nails the ethos of living the dream at your job (that I’ve written about here)

We don’t get a chance to do that many things, and every one should be really excellent. Because this is our life.

Life is brief, and then you die, you know?

And we’ve all chosen to do this with our lives. So it better be damn good. It better be worth it.

FTC: We use income earning auto affiliate links. More.

Humans Don’t Know How To Drive Self

The Slack channel at the University of Pennsylvania’s human-machine interaction lab, where I work, is typically a steady drip of lecture reminders and wall-climbing robot videos. But this past spring, news of the first Tesla Autopilot-related fatality turned the feed into a Niagara Falls of critical chatter:

Graduate Research Assistant: It’s a habit of all people launching products to claim things are working to keep people/investors excited before they actually even start it.

Post-doc Research Fellow: My opinion is that such failures are inevitable, at least until the technology improves. Tesla took the plunge first, and therefore is subject to increased scrutiny.

Student Researcher: If a driver was attentively behind the wheel, they wouldn’t have mistaken a tractor trailer for a road sign.

From a technical standpoint, that’s where the fault lay. The more important factor, to auto safety experts and to Tesla, is that the driver didn’t notice the impending collision either. So he didn’t brake—and his car ran under the trailer.

As autonomous cars begin to hit the road, it’s time to assess some long-held misconceptions we have about smart machines and robots in our lives. Many of us grew up with the promise of all-knowing partner robots like Knight Rider’s intelligent car sidekick, KITT. (“I expect a full simonize once this is over.”)

Fiction, yes, but our expectations were set—and perhaps cemented even further by set-it-and-forget-it home robotics like the Roomba and the ubiquitous task-mastering dishwasher.

Autopilot is not the KITT scenario we collectively had in mind. Its instructions clearly state that humans must remain part of the automation equation. (Even with smart machines, we still must read instructions!) Tesla publicly proclaimed its Autopilot software to be a beta program (meaning it was still working out the bugs), and cautioned drivers to stay alert and keep their hands on the wheel. But there are, if subtle, disconnects between engineering and marketing. Telsa’s October 2024 blog post announcing the software update was titled “Your Autopilot has arrived,” as if a robot chauffer was about to pull up and pick us up. Overseas, the company’s Chinese marketing translated this new feature as “self-driving”—a fact that caused one driver to blame Tesla for having sideswiped a parked car.

Early Autopilot adopters perpetuated our fantasies and desire to misuse the tech. Ecstatic YouTube videos began popping up, showing grown (and giggling) adults test-riding the cars with their hands in the air like they were on a roller coaster, and playing checkers and Jenga in traffic. One professionally produced video review, viewed a half million times, offered this not-so-helpful tip in its description: “DISCLAIMER:…The activities performed in this video were produced and edited. Safety was our highest concern. Don’t be stupid. Pay attention to the road.”

So, don’t do what we just did.


What they’re missing: Shared control is the name of the autonomous-driving game.

We can glean a lot about this type of relationship from fighter-pilot training. Professionals have flown with so-called fly-by-wire, a catchall term for any computer-controlled flight assistance, since the Carter administration. Like Autopilot, fly-by-wire is an assistive technology meant to augment, but not absorb, the pilot’s responsibility to manage the craft. Pilots undergo years of training before taking control of the cockpit, gaining an intimate awareness of what the computer is seeing and how it’s processing the information. They also learn to maintain situation awareness and be ready to react, despite the presence of technology—as opposed to taking a laissez-faire, let-the-plane-do-the-work attitude.

A casual driver cannot possibly go through the deep training that a pilot does. But automakers must find effective workarounds. For starters, they need something beyond the pages of software-release notes that display on-screen when a driver installs an Autopilot software update. They should develop short training programs—not unlike the Saturday courses some states require for a boater’s license—to help people understand how automation works, when it is and isn’t designed to work, and why human drivers need to be ready to step in. “A problem with automated technologies like Autopilot is that when an error occurs, people tend to be out of the loop, and slow to both detect the problem as well as understand how to correct it,” says Mica Endsley, former chief scientist of the U.S. Air Force and an expert in fly-by-wire and man-machine interaction.

Training smarter drivers is part of the solution. But self-driving software needs to reinforce that training. Engineers who design this software and these cars need to understand human behavior and cognition to become better able to communicate with the public. Thankfully, this human-to-machine interaction has become a growing research field for automakers and academics. At Stanford, interaction-design specialists are learning how to make an autonomous car’s camera, radar and sensor perceptions, and reasoning more transparent to humans. Automakers, they say, should employ colloquial vocal cues (“braking due to obstacle ahead”) and physical changes to controls (such as shifting the angle of the steering wheel when the driver needs to take over) to make drivers aware of changes on the road—say, trucks about to cut them off—or prevent them from daydreaming themselves into a ditch.

Such handoff signals are still fairly subtle but should become less so. On the Model S, an audio tone and color change in the Autopilot dashboard icon are all the cues drivers get when they need to take control. Cadillac’s SuperCruise and Volvo’s Pilot Assist subtly vibrate the seat or steering wheel to draw attention to achieve the same goal. But automakers need to be more aggressive in helping us; a recent study from Stanford suggests that a multisensory approach—in which, say, a shimmying steering wheel is combined with a vocal prompt and a flashing light—might be a better way to speed reaction times.

When it comes to new technology, no one wants to go slowly. Not in an age of instant apps and maps and finger-swipe transactions. But drivers should proceed with caution (and attention!) into the world of autonomous autos. Technology that might lull people into relaxing their focus while barreling down the highway requires both better training for the humans and smarter alert systems for the machines.

This past May’s fatal autopilot accident may have been a worst-case scenario, but it underscores the importance of humans and machines figuring out how to share the driver’s seat.

This article was originally published in the November/December 2024 issue of Popular Science, under the title “Don’t Blame the Robots; Blame Us.”

Don’t Get Scammed By Fake Qr Codes

Countries like China have been obsessed with QR codes for a while now—an obsession that pre-dates the pandemic. But the US is catching on. We’ve seen a recent uptick in uptake as businesses have looked to strategies that would reduce person-to-person contact. Shops and food trucks have started posting QR codes linking to online menus or even Venmo accounts. But as helpful as they can be in some cases, they come with certain risks. 

QR codes found in public places are transporting more and more people to fraudulent websites run by scammers. The latest trend in this rising new form of financial crime is centered around pay-to-park meters. 

Early in January, the Austin Police department issued an announcement warning residents that “fraudulent QR code stickers were discovered on City of Austin public parking meters. People attempting to pay for parking using those QR codes may have been directed to a fraudulent website and made a payment.” 

Those QR codes—that stands for “quick response,” by the way—are ubiquitous these days. The small 2D square mazes of black and white pixels can carry up to 4 kilobytes of data (around 4,000 characters). They were invented in the 1990s in Japan by Toyota subsidiary Denso Wave to track parts and components during the vehicle manufacturing process. Since then, variants of QR codes have circulated around the world. In these QR codes, “you can embed anything you want. People have put in music files, images, all kinds of things,” says Jason Hong, a professor of computer science at Carnegie Mellon University. “But the most common is a web address.” 

WiFi boxes, instruction manuals, and even lightbulbs can come with a QR code for easy access. “They have them anywhere you need to look up instructions or find some app,” Hong says. 

They’ve actually had slow growth, despite being around for a while. When smartphones blew up, they became more popular. “It used to be the case that you had to download a special app that would use your camera to read these things,” Hong says, but now, most smartphones have built-in software that will translate the camera scan into a link that will load through the web browser. 

[Related: Can smartphone apps track COVID-19 without violating your privacy?]

Yet, Carnegie Mellon computer scientists noted that QR code phishing scams could pose a problem for smartphone users as far back as 2012.

“People have known for a long time that the problem with QR codes is that they’re lacking ‘mutual authentication,’” says Hong, which means that there’s no way to tell if the data or link associated with the QR code is bad, or legitimate. He compares it to seeing a business card someone dropped on the ground that has a web address: “You have no idea where it will take you to.” 

But in most cases, like with instruction manuals or menus, this probably won’t be an issue. “There’s no sensitive data that they would retrieve from you, there’s also no easy way for a scammer to get their QR code onto the instruction manual,” Hong says. 

It’s very easy to generate a QR code and create a fake website that looks legitimate, says Hong. And since anybody can place a sticker anywhere, scammers can purposefully choose a location that’s convenient for intercepting information. In the parking payment scam, these QR code stickers were planted on top of the parking meters.  

[Related: QR codes are everywhere now. Here’s how to use them.]

The QR code allows criminals to cut a step out of the classic phishing website scam, “because you don’t have to type in the web address yourself,” Hong says.  

“For generic QR codes [that go through smartphone cameras], there’s no way to verify, but the city of Pittsburgh, where I’m at right now, there’s a parking app that you can use,” says Hong. “These apps can check the QR codes… and if it’s not one of the 2,000 codes that it already knows that exists, it can say it’s a fake one. But there’s no way to do that without additional context about what’s legitimate and what’s not.” 

Why Vpn Logs Don’t Measure Worker Performance

Yahoo recently made waves when CEO Marissa Mayer revoked all work-from-home arrangements and mandated that employees show up at the office. However, the real-world data Mayer reportedly based her decision upon is not a valid metric for work-from-home performance.

According to reports, Mayer reviewed VPN logs to determine how much time remote workers spend connected to Yahoo. She found that many were connecting infrequently, if at all, implying that those working from home were doing more “home” and less “work.” Was Marissa Mayer’s decision misguided?

Hopefully Yahoo’s CEO looked beyond the virtual private network (VPN) logs before making a decision. VPN logs alone are not enough to prove that people working from home are slacking off, because connecting to the company network is not the same thing as delivering results.

What are you paying for?

If Mayer had found that remote workers were all connected to the Yahoo VPN, would that prove they’re not slacking off? Does being connected to VPN 24/7 indicate that a remote user is a dedicated dynamo putting in 168 hours of productive work each week? No.

Here’s the first question companies need to consider when it comes to managing remote workers. What are you paying for, time or results?

Plenty of workers show up at an office and sit for 40-plus hours per week without doing much productive work. Time is not a good measure of performance.

Obviously, if you’re literally paying users an hourly rate as opposed to a set salary, then you are, in fact, paying for time. That’s why paying hourly is a poor model of compensation. It rewards sloth and encourages workers to drag out tasks to fill as many hours as possible.

How much time do workers in an office spend on breaks? How much time is wasted talking sports or politics with co-workers? How much time is abused surfing Amazon, or watching YouTube videos of cute kittens?

As a boss, you’re not paying for hours, but for the value that people bring.

Measure results instead of time

Do you really care how long a worker sits at a desk, or are you more concerned with how productive she is and how much she contributes to the company?

If you assign a report to an employee—whether they’re working at the office or from home—and you establish a Thursday deadline, what’s important is that he delivers a well-researched, quality report on time. It doesn’t matter if that person spends eight hours a day working to meet the deadline, or cranks it out by lunch on Monday before a round of golf.

Sitting at a desk doesn’t equate with productivity.

That’s the fundamental flaw in how companies monitor and measure employee performance. It rewards the slow and weak, and penalizes the best, most productive workers.

Assume that there are two employees making the same salary and assigned the same project and deadline. In an ideal world, the more productive employee would turn the report in on Monday, and the attitude and initiative would be recognized with a raise or promotion. What often happens instead is that the employee is “rewarded” with additional work, while the slower worker is still praised for making the assigned deadline, and they both continue to be paid the same.

Raises and promotions are too small and infrequent at many companies. Some employees are capable of producing the same or better results than others in half the time, but they know they won’t actually be paid twice as much, so there’s no motivation.

Let users hang themselves

So, maybe the remote workers at Yahoo didn’t stay connected to the VPN very long. Who cares? Did they fulfill their duties and produce results?

Remote workers are typically more productive. A study commissioned by Microsoft and released in London on Monday found that 70 percent of workers believe they produce more and better results outside of the office.

While office workers are still showering and getting dressed, those who work from home just grab a cup of coffee and get to work in their pajamas. While the office workers spend two hours or more each day battling insane rush-hour traffic, remote workers are being productive. While office workers gossip and take breaks, remote workers get laundry done or take out the trash so they can kick back and enjoy their families later. While office workers put in their allotted time, then bolt back to their homes and families, remote workers often end up spending more time working, both before and after regular work hours, because it’s convenient.

By all means, establish guidelines for remote workers. Define expectations, and make sure remote workers are available for calls or meetings. But beyond that, just measure the results and let the slackers hang themselves.

The bottom line is that great employees are great employees, and slackers are slackers whether they work at home or sit in a cubicle for 40 hours a week. Making a slacker show up at the office doesn’t magically make that person more productive.

Update the detailed information about Don’t Like Coding? Here Are The Top 10 Tech Jobs That Don’t Require Coding on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!