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

Overcommunicate

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 | Inc.com

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Fresh Tri Partners with Walmart to Add Chronic Condition Management to its Habit Building App PRWeb (Press Release)

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”. developer.apple.com. Retrieved 30 April

MobiOne Developer 1.0 M4: Create App Store-ready Mobile Web Applications, Experience True Device Behavior on Windows”. PRWeb. Retrieved 31 May 2021

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Collins, Brian (March 6, 2014). “Community: TV Review: COMMUNITY 5.08 “App Development and Condiments””. Badass Digest. Retrieved March 7, 2014.

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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

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CBA To Automate Least Cost Routing For SME Merchants; Lower Merchant Fees

The Commonwealth Bank of Australia (CBA) announced that transactions between Eftpos and international schemes like Mastercard and Visa would be automated using the least-cost routing method (LCR), making it one of the first major banks to commit to automatically routing transactions for small business merchants.

“CBA will centrally route transactions in the most cost-effective and competitive way, so businesses don’t have to spend their valuable time managing their routing options, CBA said in a statement.

“CBA will also take the hassle out of payments by automatically routing transactions between eftpos and international schemes for eligible small business customers.”

Treasurer Josh Frydenberg pressed the Reserve Bank of Australia earlier in September to lower expenses for small businesses by requiring commercial banks to provide cheaper debit card solutions to consumers making tap-and-go payments in stores. Frydenberg wrote to the Payments System Board, urging it to require major and medium-sized financial institutions to issue dual-network debit cards.

Debit card costs for Mastercard/Visa are on average 0.5 per cent, compared to 0.3 per cent for eftpos. According to official statistics, small businesses suffer greater fees on average.

Commenting on the significance of LCR for newsagents and lottery agents, Australian Lottery and Newsagents Association (ALNA) CEO Ben Kearney said: “The substantially growing costs of accepting payments for newsagents and lottery agents is one of their top daily concerns.

“They do not have the same level of control over commercial levers in different parts of their business as some other retailers. They are characterised by modest margins on volume products with fixed pricing.”

As an example, the cost of acceptance of card payments for lottery tickets – one of ALNA members’ largest categories – can be as much as 9% of their total commission for the sale of these products.

“The most important mechanism for doing this recently, when customers increasingly want to use contactless payments, has been LCR.

“This generally increases the number of debit transactions that occur, saving hundreds of dollars a month for many of our members,” Kearney continued.

Meanwhile, the government has said that it aims to implement least cost routing for tap-and-go debit transactions so that commercial merchants can avoid paying higher fees by using the relatively cheaper domestic eftpos system instead of the services of US card giants Visa and Mastercard.

According to James Fowle, CBA’s Executive General Manager, Everyday Business Banking, the announcement follows feedback from its merchant customers on Least Cost Routing (LCR) in recent months

“The overwhelming feedback from our small business customers is that they want simple competitive pricing without the hassle. They want the benefit from least cost routing without having to manage the routing themselves,” Mr Fowle said.

“Our new flat rates are designed to offer that by removing complex pricing structures and managing the routing of transactions for them. We’ll automatically and centrally route transactions in the most cost-effective and competitive way, saving businesses a lot of time and money.”

Lower rates

In addition to automating transaction routing, the bank said that all in-store card transactions will be charged a flat rate of 1.1 per cent, and all online payment transactions will be charged a flat rate of 1.5 per cent, irrespective of interchange rate or card type.

“To lower the cost of doing business, CBA will be offering 1.1 per cent for all in-store card transactions, and 1.5 per cent for online transactions, regardless of the interchange rate or the type of card (debit, credit or Amex),” CBA said.

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To provide a further cash injection in the lead up to Christmas, CBA will automatically waive three months of merchant fees for small businesses that have been hardest hit by the latest COVID lockdowns. This equates to more than $7 million dollars back into the pockets of merchant customers.

“From next week, we’ll be letting more than 50,000 customers know we are automatically waiving their standard merchant fees for three months from September through to November.

This translates into approximately $7 million back into their pockets leading up to Christmas,” said Mr Fowle.

Additionally, CBA said that an additional $3 million has been returned to retailers who have been facing financial difficulties since the outbreak began. Customers who are having financial difficulties can contact CBA for a refund on a variety of applicable fees for a period of up to 90 days.

Yajush Gupta

By: Yajush Gupta

 

Source: CBA to automate least cost routing for SME merchants; lower merchant fees

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Data Or Processes: Where To Begin With Intelligent Automation

Over the past year, many clients I’ve spoken with have been looking for ways to make processes smarter, more adaptable and more resilient. According to our recent research, many companies see the combination of AI and automation — or intelligent automation — as key to achieving these goals.

Despite the promise of better operational performance with intelligent automation, a common question is where to begin: with the process itself or with the data that will power the process? The answer lies in identifying which outcome you’re trying to achieve. Getting the sequence wrong could counteract the very goal you’re pursuing.

The right starting point 

Here are two examples that distinguish when a process-led vs. data-led approach makes the most sense with intelligent automation:

How can we improve our operational efficiency?

Amid global uncertainty, supply chain disruptions and social distancing requirements, improving operational efficiency has become a priority for many businesses. The goal in this case is to improve speed and accuracy across the value chain, and achieve outcomes faster without cutting corners.

Adding data intelligence can significantly reduce errors, remove process hurdles and reveal where corrections are needed. But doing so requires a strong process automation backbone in order to shape when and how the data is applied. So in this case, a process-led approach is best.

For example, we’re working with a major insurance provider to improve customer lifecycle management. Typically, insurance customers who file a claim experience long decision times, a lack of visibility into decision making and repeated or disconnected requests for information submission.

Insurers can distinguish themselves by being fast, frictionless and responsive in how they handle claims. However, operating in a highly regulated industry and with overt risks around claims fraud, speed can never be a trade-off for accuracy and compliance.

A contributing factor to the insurer’s process challenges was the dependence on third-party systems and disparate data sources to make decisions. We helped the company implement an automated and fully integrated process for claims handling, which was then supported with AI and data modeling to segment customer profiles and personalize services.

The system has helped reduce the turnaround on claims capture by as much as 80% and shorten overall claims procedure times from 14 days to just two, all while maintaining the necessary high levels of accuracy and regulatory compliance. The insurer has also received positive customer feedback on the effectiveness and quality of services.

How can we be more agile in our product and service offerings?

Leading retailers have an impressive ability to recommend relevant products and anticipate customers’ next actions. Whether shoppers search for a needed item, browse relevant sites or interact with brands across different channels, digitally savvy retailers can connect the dots in real-time and make recommendations with a high degree of precision.

With so many factors and variables at play in dynamic online customer environments, companies need an agile approach that allows them to test the market, gather feedback and continuously improve in order to meet customer needs.

We’re working with an online fashion retailer to deliver this level of personalization. The company is well aware of the speed at which consumers’ tastes and styles change, and realized it needed to move swiftly to gain and keep customers’ attention.

Because it was vital to gain insights into consumer preferences, we took a data-led approach. We helped the retailer use existing data to gain a deeper consumer understanding. Using this insight, we then designed a process that segmented the brand’s customer base and enabled all interactions and product recommendations across channels like chatbots, email and social media to have the highest degree of relevance, timeliness and usefulness.

The combination of process improvements and data insights allowed for an integrated digital thread to run through all phases of the customer lifecycle, including product design and development, sales and after-sales. As a result, the retailer can now drive more relevant customer interactions and next-best offers, which in turn has improved customer mindshare, loyalty and revenue.

Accelerating the path to Intelligent Automation

To get the most out of intelligent automation, process and data need to work in harmony. Automated processes enable greater efficiency, while data enables better decision-making.

By coordinating these attributes — and having a clear outcome in mind — businesses can add intelligence to how and where they automate processes in a way that accelerates business outcomes while ensuring the quality of service is enhanced.

To learn more, visit the Intelligent Process Automation section of our website. View our latest webinar on Redesigning Work for the Post-Pandemic Age.

Chakradhar “Gooty” Agraharam is VP and Commercial Head of EMEA for Cognizant’s Digital Business Operations’ IPA Practice. In this role, he leads advisory, consulting, automation and analytics growth and delivery within the region, helping clients navigate and scale their automation and digital transformation journeys. He has over 25 years of consulting experience, working with clients on large systems integration, program management and transformation consulting programs across Asia, Europe and the Americas. Gooty holds an MBA from IIM, Calcutta (India’s Premier B school), and has executive management certifications from Rutgers, Henley Business School. Gooty has won reputed industry awards with MCA for his contribution to the digital industry in the UK and is a member of various industry forums. He can be reached at Gooty.Agraharam@cognizant.com

Source: Data Or Processes: Where To Begin With Intelligent Automation

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Dicker Data Case Study

Netflix And Boeing Among Today’s Trending Stocks

According to a report from the Washington Post dropped June 12, 1-year inflation is up 5%, while 2-year inflation sits around 5.6%. This has impacted everything from raw materials like lumber and glass to manufactured products. Used cars are up 29.7% in the last year, while gas has shot up over 56%, and washing machines and dryers sit up around 26.5%.

This comes as the global microchip shortage compounds retailers’ problems as they struggle to automate their supply chains. And while the economy (and the stock market) is certainly rebounding from covid-era recession pressures, consumers are stuck footing high-priced bills as both demand and the cost of materials continue to rise. Still, the Fed maintains that prices should stabilize soon – though “soon” may mean anywhere from 18-24 months, according to consulting firm Kearney.

Until then, investors will have to weigh their worries about inflation on the equities and bonds markets against the growing economy to decide which investments have potential – and which will see their returns gouged by rising prices across the board. To that end, we present you with Q.ai’s top trending picks heading into the new week.

Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don’t have to do the digging yourself.

Netflix, Inc (NFLX)

First up on our trending list is Netflix, Inc, which closed at $488.77 per share Friday. This represented an increase of 0.31% for the day, though it brought the streaming giant to down 9.6% for the year. The company has experienced continual losses for the past few weeks, with Friday ending below the 22-day price average of $494 and change. Currently, Netflix is trading at 47.1x forward earnings.

Netflix, Inc. trended in the latter half of last week as the company opened a new e-commerce site for branded merchandise. Currently, the store’s offerings are limited to a few popular Netflix tv shows, but the company hopes to increase its branded merchandise branded to shows such as Lupin, Yasuke, Stranger Things, and more in the coming months. With this latest move, the company hopes to expand its revenue channels and compete more directly with competitors such as Disney+.

In the last fiscal year, Netflix saw revenue growth of 5.6% to $25 billion compared to $15.8 billion three years ago. At the same time, operating income jumped 21.8% to $4.585 billion from $1.6 billion three years ago. And per-share earnings jumped almost 36% to $6.08 compared to $2.68 in the 36-month-ago period, while ROE rose to 29.6%.

Currently, Netflix is expected to see 12-month revenue around 3.33%. Our AI rates the streaming behemoth A in Growth, B in Quality Value and Low Volatility Momentum, and D in Technicals.

The Boeing Company (BA)

The Boeing Company closed down 0.43% Friday to $247.28, trending at 9.93 million trades on the day. Boeing has fallen somewhat from its 10-day price average of $250.67, though it’s up over the 22-day average of $240 and change. Currently, Boeing is up 15.5% YTD and is trading at 180.1x forward earnings.

The Boeing Company has trended frequently in recent weeks as the airplane manufacturer continues to take new orders for its jets, including the oft-beleaguered 737 MAX. United Airlines is reportedly in talks to buy “hundreds” of Boeing jets in the next few months, while Southwest Airlines is seeking up to 500 new aircraft as it expands its U.S. service. Alaskan Airlines, Dubai Aerospace Enterprise, and Ryanair have also placed orders for more Boeing jets heading into summer.

Over the last three fiscal years, Boeing’s revenue has plummeted from $101 billion to $58.2 billion, while operating income has been slashed from $11.8 billion to $8.66 billion. At the same time, per-share earnings have actually grown from $17.85 to $20.88.

Boeing is expected to see 12-month revenue growth around 7.5%. Our AI rates the airline manufacturer B in Technicals, C in Growth, and F in Low Volatility Momentum and Quality Value.

Nvidia Corporation (NVDA)

Nvidia Corporation jumped up 2.3% Friday to $713 per share, trending with 10.4 million trades on the books. Despite its sky-high stock price, Nividia has risen considerably from the 22-day price average of $631.79 – up 36.5% for the year. Currently, Nvidia is trading at 44.44x forward earnings.

Nvidia is trending this week thanks to surging GPU sales amidst the global chip shortage, as well as its planned 4-for-1 stock split at the end of June – but that’s not all. The company also announced Thursday that it also plans to buy DeepMap, an autonomous-vehicle mapping startup, for an as-yet undisclosed price. With this new acquisition, Nvidia will improve the mapping and localization functions of its software-defined self-driving operations system, NVIDIA DRIVE.

In the last fiscal year, Nvidia saw revenue growth of 15.5% to $16.7 billion compared to $11.7 billion three years ago. Operating income jumped 20.8% in the same period to $4.7 billion against $3.8 billion in the three-year ago period, and per-share earnings expanded 22.6% to $6.90. However, ROE was slashed from 49.3% to 29.8% in the same time frame.

Currently, Nvidia is expected to see 12-month revenue growth around 2%. Our AI rates Nvidia A in Growth, B in Low Volatility Momentum, C in Quality Value, and F in Technicals.

Nike, Inc (NKE)

Nike, Inc closed up 0.73% Friday to $131.94 per share, closing out the day at 5.4 million shares. The stock is down 6.7% YTD, though it’s still trading at 36.8x forward earnings.

Nike stock has slipped in recent weeks as the athleticwear retailer suffers supply chain challenges in North America. And despite recent revenue growth in its Asian markets, it also continues to deal with Chinese backlash to its March criticism of the Chinese government’s forced labor of persecuted Uyghurs.

In the last fiscal year, Nike saw revenue grow almost 3% to $37.4 billion, up 5.8% in the last three years from $36.4 billion. Operating income jumped 40.9% in the last year alone to $3.1 billion – though this is down from $4.45 billion three years ago. In the same periods, per-share earnings grew 33.7% and 82.8%, respectively, from $1.17 to $1.60. And return on equity nearly doubled from 17% to 30%.

Currently, Nike is expected to see 12-month revenue growth around 10.3%. Our AI rates Nike average across the board, with C’s in Technicals, Growth, Low Volatility Momentum, and Quality Value.

Mastercard, Inc (MA)

Mastercard, Inc ticked up 0.33% Friday to $365.50, trading at a volume of 2.7 million shares on the day. The stock is up marginally over the 22-day price average of $363.86 and 2.4% for the year. Currently, Mastercard is trading at 43.64x forward earnings.

Mastercard has faltered behind the S&P 500 index for much of the year – not to mention competitors like American Express. While there’s no one story to tie the credit card company’s relatively modest stock prices to, it may be due to a combination of investor uneasiness, already-high share prices, and increased digital payments. But with travel recently on the rise, it’s possible that Mastercard will be making a comeback.

In the last three fiscal years, Mastercard’s revenue has risen 3.3% to $15.3 billion compared to $14.95 billion. In the same period, operating income has fallen from $8.4 billion to $8.2 billion, whereas per-share earnings have grown from $5.60 to $6.37 for total growth of 16.4%. Return on equity slipped from 106% to 102.5% at the same time.

Currently, Mastercard’s forward 12-month revenue is expected to grow around 4.7%. Our deep-learning algorithms rate Mastercard, Inc. B in Low Volatility Momentum and Quality Value, C in Growth, and D in Technicals.

Q.ai, a Forbes Company, formerly known as Quantalytics and Quantamize, uses advanced forms of quantitative techniques and artificial intelligence to generate investment

Source: Netflix And Boeing Among Today’s Trending Stocks

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Critics:
The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges (including the 30 companies that compose the Dow Jones Industrial Average), and covers about 80 percent of the American equity market by capitalization.
The index is weighted by free-float market capitalization, so more valuable companies account for relatively more of the index. The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although called the S&P 500, the index contains 505 stocks because it includes two share classes of stock from 5 of its component companies.

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