devopsy.kinsta.cloud

Category: Low-Code/No-Code

Microsoft Power Platform Integrates ChatGPT: What This Means

Microsoft recently announced it integrated ChatGPT into its low-code Power Platform developer suite. This follows the news of ChatGPT-powered Bing search and Microsoft’s pledge to invest billions into its partner company, OpenAI.

ChatGPT, the large language model (LLM), has reached viral status due to its ability to generate impressively complex textual outputs given simple prompts. And now, enterprises are hoping to cash in on its power. Microsoft’s inclusion of ChatGPT in its Power Virtual Agents and AI Builder tools will likely signal more native inclusion of AI models within other low-code development environments throughout the industry.

However, not all is peachy in the world of generative AI. Users have compiled a laundry list of ChatGPT failures, ranging from simple arithmetic errors and inaccurate information to social biases and even one attempt to dissolve a journalist’s marriage. AI seems sure of itself, but the “hallucinations” it produces can be mind-bogglingly off-base. If such inaccuracies persist within production use cases, it could lead to false suggestions that confuse users and even harm a company’s reputation.

Below, I’ll consider the implications of this new paradigm of AI-driven development, exploring the benefits and potential risks of incorporating LLMs into low-code development frameworks. We’ll also highlight how this may upset the competition and consider things leaders should keep in mind as they seek to leverage this new technology.

Benefits of Incorporating ChatGPT

Low-code development platforms (LCDPs) excel at abstracting complex functionalities into usable components. They typically offer drag-and-drop capabilities and reusable templates to enable citizen developers and professional programmers alike. And there are many potential benefits of incorporating ChatGPT into such a low-code environment. For example, as a feature within Power Virtual Agents, ChatGPT can be used to create chatbots that connect to public company resources and internal company data. This could help users seamlessly develop a contextually-aware AI trained on a company’s documents.

Utilizing AI within low-code can spur development with deep inferences and guidance. Such AI assistance can drastically accelerate development efforts through enhanced auto-completion and intelligent insights, quickly generating boilerplate code and making suggestions more customized to the task at hand than your typical static templates.

GPT-based chatbots leveraging AI trained on a company’s documents could be incredibly more valuable than an off-the-shelf model without context. In the realm of customer support, an in-house trained AI could be beneficial in summarizing company information and automating manual support activities. Providing a low-code means to generate such chatbots also brings advanced AI to a broader range of users. All in all, this could advance the pace of development, especially for mobile interfaces, automated workflows and contextually-aware chatbots.

Implications For the Low-Code Space

In the wake of ChatGPT, the market has been scrambling to respond. Tech giants have debuted their own generative AI to mixed reviews. And some, myself included, are left pondering how low-code and AI will coexist in this new era. Will natural language-driven code generation replace the need for programming entirely, whether it’s traditional or ‘codeless’?

This remains uncertain. But what appears most plausible is that these recent developments will raise the bar throughout the software industry and augment how LCDPs are built and used. AI has the power to enhance LCDPs in a few ways, including augmenting the developer experience, training bespoke ML models and building more intelligent end-user experiences.

Yet, with Microsoft funneling billions into AI research and development, it could be challenging for mid-size low-code platforms to keep pace without their own AI-driven workflows. Thus, the LCDPs that do not integrate robust AI of their own may lose out on new subscribers. Plus, engineers hoping to create AI models that run on internal datasets may prefer working with a larger cloud technology suite in which their data is natively stored.

Downsides of AI-Infused Programming

ChatGPT and other generative models are absolutely impressive, but the results can’t be 100% trusted. And at the time of writing, ChatGPT usage within PowerApps is still experimental, which is indicative of ChatGPT and generative AI in general—still in the experimentation phase.

This doesn’t bode all that well for programmers relying on its outputs, given the potential for inaccuracies. Because although the results of ChatGPT sound authoritative, they are generated from knowledge scoured from the public web, which often contains bugs, errors and inefficiencies.

The outputs of ChatGPT may even suggest entirely nonexistent features! This is, unfortunately, a current issue with geocoding API provider OpenCage. ChatGPT routinely recommends users integrate OpenCage’s “phone lookup service” for certain prompts. But … OpenCage doesn’t even offer this feature. The team has received so many complaints from angry developers that they even had to issue a statement explaining what happened.

With AI code generation, the focus shifts from hardcoding toward creating and organizing prompts and debugging errors. And although it’s giving programmers newfound agility, it doesn’t solve all software development problems—namely, the DevOps side of the equation. There will still be friction in deploying code and piecing together an optimal architecture. Also, due to rising software supply chain vulnerabilities, code automation that loops in third-party dependencies must require careful security forethought. There’s also the chance of breaking changes and ongoing maintenance hurdles with any software.

Another important element is that low-code requires governance to be secured correctly. No-code business users may not have the security oversight to understand the implications of spinning up new services. And when you add AI into the mix, the technical implications escalate. AI can result in ethical violations, and bots have been found to communicate anger and irrational ideas when poked and prodded.

Are We All Just Hallucinating?

‘Hallucinating’ is when an AI model confidently spouts nonsense or inaccurate information. And although this is the case with some models currently, they are actively accepting feedback and being retrained. Over time, their outputs will improve. But for now, engineers should be aware that they are still under experimentation. As such, enterprises should use caution and test new AI innovations on internal processes first. And AI adoption must be governed carefully to avoid misconfigurations and abuse.

AI is here, and the future is now. But although it’s dismantling some coding barriers, developers still have their work cut out for them. This reality is a win for low-code solutions offering standard software delivery pipelines and centralized collaboration features. If LCDPs can maintain pace with AI and embed it into their workflows, both sides should fare pretty well in this new era.

Surprise — this entire article was written by ChatGPT. Just kidding! None of it was. I’m just tired of seeing this “gotcha” moment at the end of news segments…

This post was written by a human.

March 10, 2023
Why Low-Code/No-Code Tech is Important in a Competitive Job Market

The demand for tech professionals is increasing exponentially as digital skills become a requirement for businesses. However, the world is facing a serious shortage of tech professionals. According to a forecast by the World Economic Forum, 50% of the world’s labor force will need to upskill in order to keep pace with technological advancements and automation. Similarly, digital transformation will change over one billion jobs. Yet the majority of workers around the world lack the digital skills to meet the demands of their jobs. According to a recent study, three in four employers in the world (data from 40 countries) claim a work shortage. On the other hand, millions of young people find themselves unemployed as they lack the basic digital skills for in-demand jobs. Low-code/no-code software solutions can offer a solution to this labor shortage and close the gap for workers who quickly need to acquire digital skills. Proficiency in low-code/no-code applications will provide candidates with a competitive edge in the job market.

Low-code/no-code software allows citizen developers and business experts to build business applications without requiring high technical expertise. With citizen development becoming an important part of all organizations’ business strategy, the demand for workers with proficiency in low-code/no-code app development is increasing. Expertise in low-code/no-code app development has become an expected skill set for potential employees.

Benefits of Low-Code/No-Code App Development

Citizen Development

As discussed above, digital skills are becoming a part of almost all technical jobs. Employees should be proficient in using technology to streamline data collection, automate daily processes and leverage mobile technology. To achieve digital transformation, employees should be acquainted with advancements in digital technologies. This is where the skills gap seeps in. Learning code is not an easy task. It requires a lot of time, patience and effort. Add mobile development skills to the list, and there’s an even larger skills gap. Low-code/no-code app development can play a major role in addressing the skills gap. With pre-built apps and components, low-code/no-code development platforms offer an intuitive environment for designing software without the need to be proficient in coding. Such platforms can be easily used by people without programming skills to build flexible interfaces their companies need. However, low code can also be used by IT departments to deliver faster digital solutions while freeing up time to focus on more complex coding tasks.

Additionally, low-code naturally aligns with the millennial proclivity to be digitally savvy. This generation is more comfortable than ever with using digital technologies in all aspects of their lives. Thus, they are more inclined and open to using low-code/no-code platforms and gaining some expertise in the domain.

Empowering Users

Low-code/no-code platforms empower citizen developers or business users to “skill up” in an increasingly digital society. By empowering business users to develop applications without the need for coding, low-code generates jobs directly or indirectly. The rate of technological advancement tells us that it is no longer favorable for companies to take months to develop a single app. Since the software has a life of three to five years, taking several months to develop apps is not conducive to the competitive market. As such, low-code/no-code app development can empower business users to develop applications quickly. However, citizen developers should work with IT management to ensure that the app adheres to all the company’s compliance requirements and governance guidelines. This is because citizen developers may not be proficient in scaling apps.

Business Experts Understand Business Application Need

Business needs are continuously evolving, especially since after the pandemic, when so many people are remote or using mobile apps to run businesses. At this rapid pace, it is not always easy for IT departments to continuously craft new apps and immediately update apps to match evolving circumstances and needs.

Business users who are close to daily tasks, data collection requirements and ongoing processes, are in an excellent position to understand urgent needs for specific business applications. As a result, business users are in a much better position to collect department requirements and propose an application that caters to those specific needs. In such situations, low-code or no-code development solutions equip business users to develop applications directly based on their needs.

How Can Low-Code/No-Code Help Employees Advance Their Careers?

As discussed above, low-code has tremendous potential and benefits in the current digitally charged environment. Here are some benefits for candidates learning low-code skills:

1. Bridge Business and IT

Low-code/no-code is an easy entry point for someone in their early career phases interested in working in the IT department. By learning low-code solutions for streamlining business processes and automating workflows, employees can take a step towards interfacing with IT departments or understanding the basics of coding. With necessary low-code skills, employees can also become leaders in cross-functional groups or digital transformation teams responsible for introducing low-code/no-code solutions and rapidly introducing new business applications. There is a huge gap in this space, as only 29% of IT leaders feel confident that they have the right training mechanisms for non-developers to build applications.

2. Increase Your Salary

As employees grow their low-code skills, their salaries will correspondingly increase to reflect digital proficiency. Since low-code/no-code skills will increase in demand, proficient users will make significant gains in terms of the salary offered by major companies. Glassdoor suggests that the average base salary of a workflow automation specialist in the U.S. is $81,335 per year, while that of a data analyst is just $69,517 per year.

3. Higher Job Satisfaction

A full 71% of employees expect their work organizations to provide them with the same level of service using technology that customers’ products provide. Organizations can introduce low-code/no-code applications within the organization to increase the level of employee satisfaction.

Low-code/no-code skills are becoming highly coveted in the current competitive job market. Offering benefits to both employees and organizations, low-code app development is the key to digital transformation.

February 27, 2023
How Will Low-Code and AI Coexist?

Over the last few years, low-code solutions have become important to help organizations widen programming accessibility to a broader base of users and help fill development gaps. And these technologies are still gathering steam—Gartner estimated the market for low-code development technologies would grow to a total of $26.9 billion in 2023 and that 65% of applications would be developed using low-code by 2024.

However, AI-based code generation is set to further level the playing field with advanced auto-completion and AI coding assistants. Take GitHub’s Copilot, a helpful AI pair programmer, or OpenAI’s ChatGPT, which can generate complex functions given natural language prompts. Other AI tools trained on UIs, like Galileo, can derive frontends for any project in a matter of seconds. So, are low-code development platforms still relevant in this new paradigm of AI-driven software development? If so, how will low-code solutions and AI coexist?

Well, generic algorithms can only go so far in creating truly unique and innovative experiences that solve real-world business cases. And since most programmers don’t have the skills to train machine learning or deep learning models from their own datasets, this is one area where low-code solutions could lower the barrier, enabling users to tag unstructured data, generate models, run simulations and share reusable AI across departments.

I recently chatted with Johanna Pingel, MathWorks AI product marketing manager, to discuss the role of AI in low-code programming. According to Pingel, low-code solutions can lower the barrier to AI functionality, both in quickly integrating AI-driven functionality into applications and seamlessly generating new AI algorithms based on unique datasets. Below, we’ll consider some additional use cases for AI and how low-code platforms may work alongside it.

Use Cases For AI

First off, what are application developers using AI for these days? Well, AI has an increasingly wide range of uses. It began with computer image and processing, such as identifying cats and dogs or postal codes. Now, voice recognition, voice-to-text and text-to-voice are commonplace, and AI is being used to create ever more sophisticated applications. One area that Pingel is excited about is applying AI to help with battery life management, predicting the lifespan of batteries for electric vehicles and consumer electronics.

“Anywhere there’s data, there’s an opportunity to create models and make predictions based on that data,” said Pingel. This especially rings true for scenarios involving data and visualization. For example, consider visual inspection in a pharmaceutical production line to ensure there are no defects with any pills. Another use case is visual inspections of parts in automotive assembly lines. Applied to these scenarios, AI can remove grunt work and make production safer and more efficient.

ChatGPT is another example of an AI application quickly becoming mainstream, not only for content creators but for scientific research. For example, ChatGPT can spin up MATLAB code and create functions and unit tests, which can be copied and pasted into IDEs. However, code written by ChatGPT should still be checked and verified by engineers and scientists to ensure its accuracy, said Pingel.

How Low-Code Lowers the Barrier to Bespoke AI

Low-code solutions can bring many benefits—these platforms help to speed up tedious tasks and level the playing field for those with different coding abilities and desires, said Pingel. Low-code platforms can also be a great tool for engineers looking to incorporate AI into their business systems. For example, having a low-code method could be helpful in generating code for machine learning algorithms and new models, which can help companies create their own algorithms and incorporate them into their own platforms.

More people are interested in incorporating AI into their applications, but the use cases are growing increasingly more sophisticated. Businesses may start with simple needs, such as identifying faces or identifying text from handwritten letters. These rely on simple datasets, and algorithms already exist to accomplish most of these tasks. Off-the-shelf AI is helpful at the start, but eventually, companies tend to want to use their own data and settings far more specific to their applications, said Pingel.

For example, a more niche case could be as specific as identifying cracks in a windshield. Or, in a financial industry, it might be analyzing mass amounts of loan applications to determine approval rates. In these situations, using your own internal data becomes necessary to create AI that understands the situation at hand. Yet, the hard part is assembling this data in a way that is accessible for a model to comprehend. Analysts estimate that upwards of 90% of data held within enterprises is unstructured, meaning it requires a heavy amount of tagging to become usable for AI/ML.

Low-code is a perfect solution to avoid the point-and-click aspects of cleaning, cropping and structuring this data, said Pingel. This can introduce automation into the process of labeling data while still involving some human intervention to fix mistakes. Machine learning models can take a while to train and simulate, underlining the need for a fast way of iterating on models. A common platform could help companies develop custom machine learning algorithms and models and keep track of experiments. This helps to ensure that the most effective model is used for the final application.

End Benefits of Democratized AI With Low-Code

Overall, low-code platforms can provide a number of benefits that lower the barrier to unique AI creations. Low-code platforms like MATLAB and Simulink are useful for structuring and labeling data and quickly generating ML models. Low-code also makes it easier to bridge different programming languages and quickly generate code for a variety of different languages, such as C, C++ and Java.

Additionally, low-code platforms enable users to collaborate easily with other users. But low-code doesn’t mean no-code, Pingel explained. In fact, “you can learn to be a better programming through low-code tools, too,” she said. For example, it could enhance understanding of the structure and syntax of code.

In general, democratized AI is an important capability for businesses to take advantage of. Although AI-based solutions are coming to market to displace old tools and processes, there is still an argument to be made for low-code—namely in empowering non-technical users and lowering the barrier to creating custom AI-driven functionality.

February 21, 2023
Dev of core-js Will Flip Table ¦ Another 451 PyPI Maldeps

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Denis Pushkarev is fed up with core-js freeloaders, and hundreds more malicious packages found at PyPI. (more…)

February 16, 2023
No-Code Automated Software Solves a Host of Problems

There’s a huge disconnect in corporate America, and it’s costing companies big time. The disconnect involves software testing, or, more precisely, the lack of it. More than nine out of 10 CEOs are confident that their company’s software is tested regularly, yet roughly eight out of 10 product testers reported that up to 40% of their software goes out the door with insufficient testing. This dichotomy came to light in the latest Leapwork survey of 500 CEOs and software testers.

The consequences of untested software are potentially dire and far from hypothetical. Flawed software that delayed the launch of new car models contributed to the dismissal of Volkswagen CEO Herbert Diess, while Facebook saw its market cap value plummet by billions in 2021 when a software glitch came to light.

It’s not as if anyone is unaware of such risks: A whopping 95% of CEOs surveyed admitted they’re concerned about losing their jobs if a software failure occurs, while 76% of testers shared that concern. Those feelings are certainly valid, as more than three out of four CEOs reported that software failures damaged their companies’ reputations during the past five years.

Despite these realizations, CEOs seem willing to gamble. A full 85% said they considered it acceptable to release software that wasn’t adequately tested, as long as it can be patched later. That attitude has made patching standard operating procedure. More than half the testers said their teams spend five to 10 days a year patching software.

Why are organizations willing to take unnecessary risks by releasing software that’s potentially not ready for prime time? There are several common reasons—and a solution that many are overlooking.

The Problem with Manual Testing

One of the major problems is the continued reliance on manual testing, which requires time and resources companies can’t afford. Only 43% of testers said they use automation to some extent in their testing, proving that the majority of testing is still a manual process.

That significantly slows product development, an issue for businesses anxious to get their products to market before the competition. Nearly 40% of CEOs said the time-consuming nature of manual testing is one of the primary reasons they release programs that haven’t been properly vetted. Testers clearly feel the crunch, with about one in three saying faster development cycles don’t allow enough time for testing.

Given the choice between rushing through testing or delaying a release, companies often roll the dice and opt for the former.

Two recent pandemic-related trends have made the situation even more difficult. More organizations are embracing digital platforms to cope with the effects of COVID-19 on their businesses, increasing the need for IT talent. At the same time, upheaval in the labor market has resulted in widespread resignations and job hopping, contributing to a serious shortage of skilled IT personnel.

The Coding Conundrum

Even in this chaotic business climate, experienced software developers remain a hot commodity, with many career options at their fingertips. The competition for talent has left many IT teams scrambling and understaffed, making it difficult to produce high-quality software at a breakneck pace.

Eliminating the need for manual testing could relieve the pressure to a great extent. By automating testing, organizations can reduce the burden on staff and minimize the risk of errors caused by deadline pressures. That would allow more testing of more software in less time, which would help move product out the door and into the hands of customers.

But automated testing isn’t without its challenges, as many organizations have discovered. Many solutions that are billed as “low code” still demand coding skills beyond the capability of typical business users and testers. That need for coding is an obstacle that can drag down the pace of testing and make it difficult for business users to participate in the process.

That challenge grows as the need for ongoing maintenance expands. The test engineers who came on board to set up an automation solution can find themselves bogged down in maintaining code. As a result, speed to market continues to suffer.

Democratizing the Test Process

There is a solution: true no-code test platforms. This type of solution allows both testers and business users to design, run and maintain tests without the need for coding. Business users who understand how the software deliverables are supposed to grow business can be intimately involved in testing, which enhances the quality and functionality of the product and the satisfaction of customers.

It also promotes speed. When testing is largely automated and more people are involved, it’s possible to test more software in less time. This eliminates bottlenecks and addresses the issue of scale. The use of automation on a no-code platform has been shown to speed time to market by a factor of 10—an improvement that largely eliminates the need to rush potentially flawed software to market without adequate testing.

Organizations can position themselves to meet the growing demand for digital solutions by revamping the way they test software. By eliminating manual testing and adopting an automated platform that doesn’t require coding skills, they can afford to test all their products thoroughly. The end result: They can minimize the risk of bugs that could undermine their success and threaten job security.

January 9, 2023
The Time for Automated Testing is Now

Skilled software testers continue to be in high demand, and it appears that trend won’t change any time soon. The current labor shortage and a looming recession is giving software development leaders good cause to think about how to attract and keep qualified personnel and, more importantly, enable them to work as efficiently as possible. Added to this scenario is a trend in the software development cycle toward shift left testing, which has put increased pressure on developers to take on testing responsibilities that often require taking on new perspectives and using new tools. Automated testing solves these problems by making it easier to learn how to build a consistent, reliable testing program in less time, requiring less maintenance.

Add to this the future promise of artificial intelligence or AI-augmented testing platforms and current machine learning (ML) capabilities, such as self-repairing of test scripts, and you have a platform that both levels the learning curve and accelerates efficiency. The sooner organizations can transition from manual to automated testing, the better they can withstand internal and external market fluctuations.

A Developer and a Tester Walk Into a Bar …

Traditional software testing is a manual process in which testers are employed to replicate users and are expected to bring that perspective to their work. As such, software testers have a very different mindset than software developers. Where developers are protective of their creative output, software testers are driven by a curiosity to probe the limitations of a system. It stands to reason that the qualities that make someone excel at either of these roles is, likewise, not the same. And yet, with the trend toward shift left testing and continuous integration/continuous development (CI/CD), the industry has blurred the distinction between coder and tester to the dismay of both sides.

Automated testing removes much of the monotonous work associated with repetitious test plans. It allows testers to focus on writing better scripts, extending the scope of their testing plans and scheduling more powerful load tests. From the organization’s perspective, once you have established a library of reusable test scripts, your testing program will be protected from future turbulence in the employment market, including recession and attrition.

Low-Code/No-Code

Regardless of the tool you use, code will be created if you are creating software — even test scripts. Ideally, however, a low-code/no-code tool can hide some of the complexity and promote best practices. Using a low-code/no-code tool can relieve a tester from creating test scripts from scratch by recording manual interactions and offering suggestions. In the case of shift left testing, a low-code tool can eliminate the need to learn entire testing frameworks while still enabling a developer to access code when desired. Automating testing also eliminates the human error inherent in repetitive manual tests. In short, test automation does not diminish the value of skilled software testers and developers; it increases it.

Placed in the hands of skilled testers, automated testing will only become more powerful, more effective and more critical to your development cycle. It elevates the skills of testers and makes them more productive, accurate and thorough. Test automation also makes for a smoother transition to shift left testing, allowing developers to spend less time testing their software and more time doing the creative work that makes them thrive. In a market where attracting and keeping top talent is only going to get more difficult, anything you can do to lighten the burden on your employees and make them more productive will be a big win for your business. So, the question is not whether you should transition from manual to automated testing but when.

January 9, 2023
Best of 2022: We Must Kill ‘Dinosaur’ JavaScript | Microsoft Open Sources 3D Emoji

As we close out 2022, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2022.

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: JavaScript is a bloated barrier to progress, and Microsoft’s emoji are on GitHub. (more…)

December 30, 2022
Salesforce Launches Low-Code DevOps Platform

Salesforce made generally available today a low-code DevOps Center service on its infrastructure through which developers are provided an opinionated platform for building custom applications. The Salesforce DevOps Center service is based on the same object model Salesforce uses to construct its own applications.

Karen Fidelak, senior director of product management at Salesforce, said after nearly two years of development effort, this platform will make it simpler for both professional and citizen developers to build applications consistently using a common Source Control repository. That’s especially critical for professional developers that may need to access source code previously created by a citizen developer, Fidelak noted.

In 2023, Salesforce will embed more robust continuous integration/continuous delivery (CI/CD) capabilities within the platform to make it simpler to deploy custom applications on the Salesforce platform, Fidelak added.

In general, the number of developers willing to embrace DevOps best practices to build Salesforce applications has been surprising, since the platform was initially launched in 2020, noted Fidelak. She noted that both citizen and professional developers are looking to adopt best practices that provide a set of guardrails that ensure high-quality applications are secure. Salesforce anticipated a much higher level of DevOps resistance, added Fidelak.

The DevOps Center platform enables development teams to track and deploy changes to custom objects on the Salesforce platform as Work Items that can be integrated with other Salesforce Flows, a set of process management tools the company provides. As development teams make changes in their sandboxes, DevOps Center automatically tracks them. Developers can then point and click to promote their desired metadata components via custom pipelines they define.

In addition, Salesforce also provides optional integrations with version control systems such as GitHub to centralize the management and governance of artifacts.

Finally, Salesforce is committing to tightening integration between DevOps Center and multiple software-as-as-service applications such as Tableau and middleware platforms such as Mulesoft, said Fidelak.

It’s not clear how much low-code is moving through DevOps pipelines these days, but the volume is increasing as more developers rely on these tools to accelerate application development. The issue that creates is citizen developers typically don’t have the expertise required to build and deploy secure applications at scale—at least not applications that anybody other than themselves really wants to use. Ideally, citizen developers should be supported by application development platforms and DevOps teams that ensure security guardrails are in place for building applications at scale. DevOps teams should also reduce the friction that a citizen developer will most certainly encounter when building any application.

The fact is, in the absence of that support, most citizen developers—who typically have other tasks that require their attention—will simply abandon an application development project should projects become overly complicated. None of this means organizations shouldn’t be working toward building their own army of citizen developers. The increased reliance on citizen developers is part of an inexorable wave of the democratization of IT. The challenge is making sure those applications are of the highest quality possible regardless of who actually developed them.

December 15, 2022
From Frontend to Full Stack: How I Leveraged Modern Dev Tools to Make the Leap

A few years ago, I had an idea about how to make publishing recipes on the internet more accessible and profitable. As a front-end engineer-turned-product manager, I could write code as long as it involved JavaScript. But I needed someone who could handle the whole stack, especially the back end—you can’t build a platform with frontend tools only. So I searched for a technical co-founder, poked around online forums and talked to friends. Eventually, I found a candidate (let’s call him Tom) who seemed like a good fit. He’d built a recipe app in the past and was comfortable with both the front and the back end. It seemed great. We struck a deal and dove in.

Tom started building the API and database while I made prototypes with a no-code builder called Bubble. I loved using Bubble. Without writing any code, I could build things fast and see what I was creating. Eventually, though, there were too many limitations with the no-code approach, including the lack of a Git repository for version control and history. So we decided to switch to full code with React Native mobile apps.

A few years prior, I’d switched career paths and became a product manager, but it had been a while since I had dug deep into writing code. And although I had a background in React, I felt insecure about building the frontend of a whole web platform on my own. But alas, you do all sorts of scary and undesirable things when you’re a startup founder!

After jumping into it, though, I quickly remembered that I really enjoyed coding. I picked up React Native pretty quickly and started building screens. Tom took care of the data while I took care of the UI. All was good.

Well, all was good until it wasn’t. We were a few weeks away from launch day and were finishing up. We even had a few early-access bloggers lined up to launch with us. And then, surprise! Tom decided to take a job as the CTO of another startup. His parting words were, “You’re technical enough; seems like you can handle it.”

I was floored. Aside from the front end, I had no idea how anything worked. We had cloud functions running on Pulumi hosted in Google Cloud, and I had no idea how they worked. I didn’t know how to train the machine learning AI models we used for ingredient recognition. The most I knew about SQL was that you used it to get stuff from a database. And to top it all off, our recipe scraping API was written in Python, a language I had no experience with, running a version that did not work on my M1 Mac. What in the heck was I supposed to do?

Well, I guess I did what any engineer would do. I figured it out step by step. When the Postgres database ran out of rows on the free Heroku plan, I figured out how to migrate it. When the recipe scraper API needed updating, I learned how to code in Python and deployed it with Docker. It was an incredibly stressful time. I was up against a looming launch deadline with a codebase teetering on collapse at any moment—mostly because I didn’t know how to hold it up.

The silver lining was the GraphQL API platform, Hasura. It was by far the easiest part of the stack for me to grasp. Understanding how our data was laid out was easy enough—I could simply explore it like a spreadsheet. I wasn’t sure how to build GraphQL queries, but the tutorials on the Hasura website helped me learn the basics and the query builder in the console did the rest. It’s no-code, but better—a no-code console makes it super easy to add new tables and modify permissions, and a Git repository keeps everything in check and easy to reference.

Once I understood how Hasura and the rest of the stack worked, I was able to finish the screens that I needed for launch.

Today, the company has grown significantly. We have a new co-founder, but she’s nontechnical. After figuring out how to run the entire stack, I decided I was technical enough to be the primary engineer. I’ve learned how to use all sorts of Hasura functionality that makes our lives so much easier—listening for events, firing off actions in our API to run business logic, and much more. Through all this, I now consider myself a full-stack engineer.

December 14, 2022
AWS re:Invent — Top 4 Things We Learned This Week

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week, four things that caught my eye from Amazon Web Services’ re:Invent 2022 conference: SnapStart turbocharges Lambda, Graviton3E ARM HPC SoC, AWS continues hiring, and AWS’s origin story. (more…)

December 1, 2022