Here’s How Tcodingo Build an App Without Knowing How To Code

Illustration by Julia Schwarz

What if you could build an app for your company without having to hire expensive and hard-to-find software developers? That’s the promise of low-code and no-code platforms: wresting the power to solve workflow problems from the hands of the engineers. Can you imagine? “Now,” says David Adkin, co-founder and CEO of no-code platform Adalo, “entrepreneurs can solve their own problems.”

It’s a huge shift. The fact that anyone, regardless of coding chops, can drag-and-drop their way to a fully functional mobile app, or even tell a computer in plain English what they want that app to do, means businesses now have a powerful tool for quickly improving their digital presence cheaply. 

Still, there are a few things worth knowing if you want to join the revolution. We’ve interviewed experts in the field to bring you some critical tips to going sans code


Even low-code apps require planning, governance, and training, according to Rachel Brennan, VP of product marketing at the Washington, D.C.-based software firm Bizagi. Loop in your IT and ­legal teams to monitor security, compliance, and scaling concerns associated with new software.

Beware Shiny Objects

Tara Reed, founder of no-/low-code school Apps Without Code, suggests test-driving different tools and choosing one that matches your skill level. If you already have design experience, she says, “pick a tool that gives you lots of freedom with design.”To guide your decision, see “Tools to Try” below.

Unlock Your Full Potential

No-code and low-code aren’t just for consumer-facing products. Ed Macosky, chief innovation officer at IPaaS firm Boomi in Chesterbrook, Pennsylvania, advises leveraging these platforms’ AI and machine-­learning power to automate workflows for internal teams.

Don’t Be Shy

Don’t try to perfect an app ­before bringing it to market, says Emmanuel Straschnov. The founder of NYC-based Bubble says no-code platforms like his enable entrepreneurs to iterate faster and collect more data than with traditional coding, so it’s worth getting user feedback as early as possible. Just be sure not to store any personal information like credit card or Social Secu­rity numbers on the app.

Tools to Try

Adalo: For creating ­native ­mobile apps

Bubble: For those who need flexibility

Glide: For making your very first app

Webflow: For creating­ ­websites and landing pages


Source: Here’s How to Build an App Without Knowing How to Code |


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Bibel, Sara (March 7, 2014).

“TV Ratings Thursday: ‘Scandal’ & ‘Grey’s Anatomy’ Dip, ‘The Big Bang Theory’, ‘The Vampire Diaries’ & ‘Parenthood’ Up, ‘American Idol’ Hits Low”. TV by the Numbers. Archived from the original on March 7, 2014. Retrieved March 7, 2014.

VanDerWerff, Emily (March 6, 2014). “Community: “App Development and Condiments””. The A.V. Club. Retrieved July 27, 2019.

Moss, Gabrielle (March 6, 2014). “Community: “App Development and Condiments” Review”. TVFanatic. Retrieved March 7, 2014.

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“Launching Your App on Devices”. Retrieved 30 April

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AI Can Write Code Like Humans Bugs and All

Some software developers are now letting artificial intelligence help write their code. They’re finding that AI is just as flawed as humans.

Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.

Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. “It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”

But Naka has found that errors can creep into his code in different ways. “There have been times where I’ve missed some kind of subtle error when I accept one of its proposals,” he says. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.”

The risks of AI generating faulty code may be surprisingly high. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time.

The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. “But the way Copilot was trained wasn’t actually to write good code—it was just to produce the kind of text that would follow a given prompt.”

Despite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. There’s growing interest in using AI to help automate more mundane work. But Copilot also highlights some of the pitfalls of today’s AI techniques.

While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else.

Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.

De Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe.

The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The algorithm ingested billions of lines of code stored on GitHub—not all of it perfect—in order to learn how to write code.

OpenAI has built its own AI coding tool on top of Codex that can perform some stunning coding tricks. It can turn a typed instruction, such as “Create an array of random variables between 1 and 100 and then return the largest of them,” into working code in several programming languages.

Another version of the same OpenAI program, called GPT-3, can generate coherent text on a given subject, but it can also regurgitate offensive or biased language learned from the darker corners of the web.

Copilot and Codex have led some developers to wonder if AI might automate them out of work. In fact, as Naka’s experience shows, developers need considerable skill to use the program, as they often must vet or tweak its suggestions.

Hammond Pearce, a postdoctoral researcher at NYU involved with the analysis of Copilot code, says the program sometimes produces problematic code because it doesn’t fully understand what a piece of code is trying to do. “Vulnerabilities are often caused by a lack of context that a developer needs to know,” he says.

Some developers worry that AI is already picking up bad habits. “We have worked hard as an industry to get away from copy-pasting solutions, and now Copilot has created a supercharged version of that,” says Maxim Khailo, a software developer who has experimented with using AI to generate code but has not tried Copilot.

Khailo says it might be possible for hackers to mess with a program like Copilot. “If I was a bad actor, what I would do would be to create vulnerable code projects on GitHub, artificially boost their popularity by buying GitHub stars on the black market, and hope that it will become part of the corpus for the next training round.”

Both GitHub and OpenAI say that, on the contrary, their AI coding tools are only likely to become less error prone. OpenAI says it vets projects and code both manually and using automated tools.

De Moor at GitHub says recent updates to Copilot should have reduced the frequency of security vulnerabilities. But he adds that his team is exploring other ways of improving the output of Copilot. One is to remove bad examples that the underlying AI model learns from. Another may be to use reinforcement learning, an AI technique that has produced some impressive results in games and other areas, to automatically spot bad output, including previously unseen examples. “Enormous improvements are happening,” he says. “It’s almost unimaginable what it will look like in a year.”

Source: AI Can Write Code Like Humans—Bugs and All | WIRED


Related Contents:

The Top Coding Schools of 2019

Coding as a vocation is seen as a very recent phenomenon, but its roots lie back in the middle of the 19th century, when, in 1842, Ada Lovelace, the only child of the poet Lord Byron, is widely viewed to have written the first computer program. Lovelace spent two years translating an article by Charles Babbage, a renowned mathematician, who was honing a method for using punch cards to store ‘programs’ on a device he had created called the ‘Analytic Engine’.

When Ada’s notes were examined, they were determined to contain a description of the world’s first computer program, hence forth she had a programming language ‘Ada’ named after her!

In 1906, a man called Herman Hollerith used the punch cards that Lovelace had described in his ‘electric tabulating machine’ which could perform separate tasks without being reconstructed between them – one of the first computers was born, and Hollerith’s company went on to become IBM.

Up until the 1980s, computer programing was perceived as being the preserve of specialized companies or teams of individuals, and from an outsiders perspective, it was an opaque and somewhat impenetrable industry, compounded by the fact that most of the programs produced at the time were for industrial or military applications.

This all changed with the advent of the ‘home computer’, when, for the first time, programming platforms were available to households who could afford the relatively high cost of entry. Of course early PC coders had to be undaunted by the prospect of manually typing in pages and pages of ‘BASIC’ code when one misplaced ‘:’ would cause the program not to run, as well as watching a flickering screen for minutes at a time while programs were loaded via cassette tape.

Nevertheless, the computing generation had arrived and, nourished by a generation brought up in video game arcades starting to realize they could create and enjoy the same sort of experiences at home on their tv screens, it flourished and matured. The profusion of ‘open source’ software in the 2000’s and the (relatively) recent advent of the smart phone supercharged the industry and made it what it is today.

Talking of today, the industry demand for coders is skyrocketing as it is increasingly recognized as a rewarding, future-proof and truly flexible and democratic vocation. Unlike previous generations, we are living in a digitized world and are constantly surrounded by, and engaged with, the products that coders produce.

This has allowed coding to become a much more relatable and desirable career for many of us. Consequently, coders have gone from being yesterday’s low profile and detached specialists to today’s workplace Rockstar!

A snapshot of today’s coders:

419,000 active coders in the U.S. workplace

Average age of 42.5

Average Salary – $87,870 in 2017, which is $35,805 more than the average national salary of $52,065.

Almost a 25% of coders are female – a proportion that is rising exponentially as concerted efforts are made to encourage girls to code.

Data from

Education in Coding

To complement this coding ‘goldrush’, an abundance of great coding schools have sprung up that can quickly and efficiently furnish you with the necessary skills needed to join the digital revolution.

Unlike a traditional school vocational degree, Coding School courses typically last for just 12 weeks (but can vary from 6 to 28 weeks) and will suit those who prefer hands-on, project-based learning and feel a sense of accomplishment from mastering a real-world concept. In addition to instructor-based learning, students are encouraged to explore concepts with others and on their own volition.

Since students usually work in teams, much the same way as they would in the working world, participants learn first-hand the importance of effective communication and teamwork, as well as understanding the fundamentals and adopting best practices.

Coding Schools also offer individuals who have not got a background in coding, an affordable, efficient and accessible way to switch tracks and get involved – indeed many of today’s coders started their work lives in unrelated areas but decided to carve out a new direction and join the coding movement!

Source: The Top Coding Schools of 2019

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When it comes between you getting a 4-year computer science degree, specializing in a high demand skill by attending a coding bootcamp, or learning to code on your own, what is the best path? This video isn’t sponsored by Team Treehouse, but I am an affiliate for them. Just by signing up for a free trial, you’re helping support this channel. Sign up for a free trial here ➡… I’ve spoken about this topic in the past with Quincy Larson, the creator of Free Code Camp. He spoke about his opinion between computer science, coding boot camps and being self taught. Now it’s my turn. Rich Friends, Poor Friends (free video series):… Are you looking to work remotely? Find a remote job on our job board: Social Media:
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