Meet GitHub Copilot - The AI That Codes For You

2 years ago, GitHub announced they were teaming up with OpenAI - where they showed a technical preview of what they call “GitHub Copilot”. GitHub Copilot exceeded 1 million users in just 6 months and it’s popularity is no accident. 

GitHub Copilot is an extension for Visual Studio that takes the code you’re writing and gives you suggestions to complete lines or even entire functions that change while you write. The tool expedites finding alternative problem-solving approaches, writing tests, and delving into new APIs, all without having to search the internet or Stack Overflow. It will also adapt to your coding style, which is going to make your workflows perform much more efficiently.

Since it was created with OpenAI, GitHub Copilot relies on OpenAI Codex. Codex has extensive knowledge of coding practices and is great for code generation, primarily because of its training on a dataset rich in public source code. While Copilot is compatible with an array of frameworks and programming languages, it really shines when working with JavaScript, Python, Ruby, TypeScript, and Go.

Let’s look at an example:

As you can see in the video, writing code looks a lot faster with Copilot and not once did he have to look on Stack Overflow or GitHub to find solutions or code snippets. Some are even saying that the tool speeds up the coding process by 55% and handles 40% of writing the actual code.

So we have to ask then; Is this a revolution that will condense the scope of work for developers? Or at this moment, is it too good to be true?

What’s The Word Among Developers?

Even with how advanced GitHub Copilot is, developers seem to have mixed opinions on its implications and usefulness. Some of the main concerns about Copilot stem from security and copyright. Specifically, whether or not the system has access to the codes on GitHub, API keys, passwords, etc. This has actually landed Microsoft and OpenAI in some hot water - in the form of a lawsuit from developers who claimed that the system violated copyright laws.

Another key concern is that the quality of code the tool suggests for users is inaccurate, or not relevant to what the programmer is trying to accomplish. 

These are the two main pain points, but from a technical perspective, they don’t seem like anything that can’t be addressed and improved over time.

With that said, the main draws for developers obviously stem from the speed and precision with which Copilot can complete code. What’s more interesting though is that Copilot can keep the project's code consistent - for developers, this is huge because it means an easier debugging and maintenance process.

The other big attraction is how it aids in working with new frameworks and libraries that developers may not be completely familiar with. Often when developers are working in an unfamiliar environment, they’ll run into issues with the architecture, the codebase, what the business needs, and sometimes legacy code, all of which is very tedious work for developers. For this reason, Copilot is a great resource since it can synthesize code to fit the needs of the project.

Who Are GitHub Copilot’s Top Competitors?

Considering that the tool is still new and that it has strides to make before it reaches its maximum potential, it’s quite remarkable what it’s been able to accomplish. This “Copilot” movement with AI suggests a new future for software development, which we’ve always known was going to change dramatically as soon as mainstream AI tools caught momentum. The great thing about having tools is having options, and when we’re writing code in the 21st century we have tons of options.

Here are some code completion tools that are similar to GitHub Copilot: 

1. Tabnine: This is a top competitor for Copilot, and it also uses machine learning to complete code and offer suggestions. It too integrates with Visual Studio and other various IDEs and code editors like IntelliJ IDEA or PyCharm.

2. Codeium: Codeium supports multiple programming languages and can be used with editors like Visual Studio, Atom, and Sublime Text.

3. CodeGeex AI: CodeGeex AI is an AI-powered code completion tool that uses machine learning to suggest code completions. It too supports various programming languages and can be used with Visual Studio, Atom, and Sublime Text.

4. Code Whisperer: Code Whisperer is the same as the last two, it suggests code completions and also works with Visual Studio, Atom, and Sublime Text.

5. ChatGPT: ChatGPT is what we’re all likely most familiar with. It’s an AI chatbot that can help developers write code. It will suggest code completions and can also be used with Visual Studio, Atom, and Sublime Text.

6. Ask Codi: Ask Codi is another chatbot that can help developers write code. It performs the same functions as the rest and integrates with Visual Studio, Atom, and Sublime Text.

7. Google Bard: Google Bard is an AI chatbot that… You guessed it! Writes code for developers. It will also work with Visual Studio, Atom, and Sublime Text.

This list could go on seemingly forever and they’d all pretty much have the same descriptions. The point is that in a growing pool of so much competition, what is it that will give GitHub Copilot the upper hand?

Immediately what sticks out is it’s partners; GitHub, Microsoft, and OpenAI - the biggest names in artificial intelligence and software development. This inevitably raises the bar right away for GitHub Copilot. To meet the expectations of the market, Copilot is going to have to leverage being trained on the code from GitHub so it can generate original code more easily. It should also finalize, and effectively implement the experimental features such as Copilot Labs, Copilot Chat, and Copilot Voice. 

The Takeaway

The average computer science degree is around $20,000/year. Chat GPT is free along with most other AI tools. The highest you’ll get with code completion tools is about $20 per month, you see the pattern here?

More people can learn to code and do it a lot easier, perhaps even at scale, than ever before. Tools like GitHub copilot aren’t just “Shortcuts” - every developer should absolutely familiarize themselves with these tools and leverage them as much as they can before they are replaced by someone who’s already beat them to it. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

AI’s Revolutionary Strides in Custom Software Development

As custom software developers, design thinking is standard practice when it comes to any project. We put the user first and build the solution around their needs… This is nothing new. What’s new is that design thinking has changed and evolved into “platform thinking.”

Platform thinking is the understanding that modern consumers have evolved from passive observers in the product lifecycle to active contributors in the value creation process. For example: 

• Uber uses platform thinking to connect drivers with people who need rides. The more drivers and riders who use Uber, the more valuable it becomes, because there are more rides available and more people to connect with.

• Instagram connects people who want to share pictures and videos. The more people use Instagram, the more valuable it becomes; there are more photos and videos to see and more people to connect with. 

Simple enough? Hope so, because now we’re really going to change pace.

Artificial Intelligence Tools in Software Development

AI can assist people in creating and enhancing things, no matter how skilled they are. This approach to platform thinking will become something every business grasps. It’ll eventually get to the point where all employees are also materializing their ideas quickly.

Bear in mind that 41% of all code on GitHub is AI-generated, and as AI becomes an important part of making software, the teams and skills needed will change. AI is not a replacement, so much as it is an extension of work, which in software development always comes back to the team around platform engineering.

The Impact of AI on Software Development Roles

Businesses must anticipate AI's role in platform engineering as they look ahead. With the evolving approach to development, the following are some jobs that will change.

Interaction design roles will surpass UI design roles in demand. As visual AI progresses, the need for a manual UI layout and structuring of business processes will diminish. Interaction designers will guide AI in crafting user interfaces and user experiences through JavaScript design systems, visual guidelines, and consistent user testing.

Business analysts will be dramatically more important in shaping business strategies. AI will likely take on tasks like writing user stories, defining requirements, and even setting acceptance criteria. Instead of just documenting these criteria, BAs will evaluate the AI-generated concepts and align them with the platform-oriented mindset. AI will become the key driver of business strategies, with analysts guiding it in the right direction.

The role of test architecture will be a highly sought-after and well-paid position. With autonomously generated software, continuous testing will be crucial. As the development cycle shortens, the demand for testing will skyrocket. Simply automating user tests based on acceptance criteria won't work anymore. 

Test architects will be responsible for designing, implementing, and maintaining intricate test architectures, conducting end-to-end testing of new features, consistently performing exploratory testing, and executing dynamic regression suites that evolve with time.

Software architects will arguably get the most out of AI under this umbrella. Even though we’re still technically in the early stages of AI integration in software development, we are seeing tons of growth in platform engineering. Businesses are shifting away from single-point SaaS solutions and consolidating their efforts on custom-built and SaaS-enabled platforms like ServiceNow, Salesforce, and Workday. 

In addition, software architects are devising governance systems to shape code standards, development processes, and other aspects along those lines. Going forward, they will leverage AI to create, enforce, and evolve these systems autonomously.

Putting It All Together

Custom software development is in an interesting spot because even though the industry itself is changing, it too has the power to influence and change other industries. Every facet of a business's operations spiderwebs with AI integration.

In healthcare, maybe it’s automating diagnosis and treatment recommendations. In finance, there could be new approaches to financial planning. Maybe in manufacturing, it’s personless warehouses. These are the kinds of visions we need to dream up as software developers while the change happens in real-time. Clients are looking for partners in transformation which means as a software development company, AI needs to be a priority internally and externally.

The Takeaway

Integrating AI in your business processes starts by knowing when you’re ready and where it’s needed. With that in mind, we created a free tool to help you determine whether or not your business is ready for AI.

How is your industry changing right now? How do you think it will continue to change in the next 5 years? These are the questions you need to be asking yourself in today’s marketplace because there will be stark differences between the companies who do ask and the ones who don’t.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

How AI Chatbots Have Recovered in eCommerce

9 months ago marked one of the biggest transitions in the way humans communicate that will become an irreversible change shortly in every industry. E-commerce is no exception, and the implications of Chat GPT and tools alike pose massive advantages for businesses that can leverage AI effectively.

Like anything new, there has to be the trial and error stage where businesses figure out how the tool fits into their processes. The first issue that immediately stands out with Chatbots is the generic and repetitive responses. If your site implements a Chatbot to manage customer support, how are you going to want the experience to be? For most business owners, the answer is a simple “Unlike anything they’ve ever seen before” which is great but we should also add “And can’t get anywhere else”.

What Happened With Chatbots in eCommerce at The Start (The Epic Fail)

In the early days of eCommerce Chatbots, rather than expediting processes, they ended up causing delays. Chatbots would struggle to locate information, resulting in sluggish responses that left customers feeling more frustrated than if they had waited for a human representative to assist them.

Even when Chatbots managed to provide fast responses, they frequently failed to address the specific questions customers posed. For instance, if a customer inquired about the precise location of their package, the chatbot might respond with a generic message like "Your package is in transit".

In their initial stages, Chatbots were limited in their ability to handle anything beyond basic requests. While they could handle queries like "How do I start a return?" they were incapable of handling more complex requests like "I'd like to check the status of an ongoing return."

The biggest downfall of these early-day Chatbots was their struggles to retain previously gathered information. If a customer was transferred to a human representative, that representative often had none of the information the chatbot had already collected.

Even today, Chatbots are not universally trusted. Under the Bot Disclosure Act implemented in California in July 2019, retailers are required to inform consumers when Chatbots are in use, with non-compliance resulting in fines of up to $2,500 per violation. 

What’s Changed?

We can’t come off talking about this Chatbot dystopia without telling you about the strides the technology has made in recent years. So with that said, here’s a look at what’s been going on: 

From a technological perspective, this is what’s gotten better: 

1. Natural Language Processing (NLP): NLP lets Chatbots understand and interpret human language, which makes interactions feel more natural and meaningful.

2. Machine Learning (ML): ML algorithms let Chatbots remember and learn from past interactions, which over time makes them more efficient. This is essential for personalization and handling any issues brought to light by customers.

3. Chatbot Architectures: The design and development of Chatbots have evolved to include components like user interfaces, NLP engines, and ML algorithms, which make the Chatbots more powerful and enhance their responsiveness.

4. Rule-Based vs. AI-Based Chatbots: Rule-based Chatbots use predefined rules to respond to queries, and AI-based Chatbots leverage NLP and ML to understand and respond to user queries. Match those up against each other, and AI Chatbots are the clear winner. 

5. Best Practices: Developers now follow best practices in chatbot design, focusing on clear purposes, and the user experience, and prioritizing ongoing testing and refinement.

With this part covered, let’s shift to what these technological advancements have translated into:

1. Human-Like Chatbots: Chatbots have become more human-like, thanks to the advancements in Natural Language Processing and machine learning algorithms. This makes interactions with Chatbots more relatable and user-friendly. Recall that in the past, Chatbots often provided generic and robotic responses.

2. Deep Customer Insights: Modern Chatbots are designed to use deep customer insights to inform their responses. This is a fancy way of saying; they can analyze user data and give those personalized/relevant responses that companies want their users to have.

3. Voice Bots: Voice bots (Siri, Alexa, etc.) have obviously become a massive deal since they also give a more natural and intuitive interface for users. Think about booking appointments, ordering food, or making reservations using voice commands. This was a massive improvement over text-only Chatbots.

4. Improved Customer Satisfaction: Chatbots are now designed to create a sense of connection between the customer and the company instead of simply being a means to automate support services. They provide quick, personalized experiences that improve customer satisfaction and loyalty. In the past, as we know, Chatbots often left customers feeling disconnected and dissatisfied.

What a Successful Chatbot Implementation Looks Like

A few good examples of companies leveraging Chatbots effectively include Rawbank, Starbucks, and Lyft. To break down what each of these companies is doing as straightforwardly as possible, we’ll say that effective Chatbots can be recognized under three pillars:

1. How it understands language

2. How it personalizes the experience

3. How it continues to get better

With Rawbank for example, it has over 50 different use cases which is what makes it so well regarded. With this amount of ground covered, it’d mean that there isn’t a whole lot users could throw at the system that it wouldn’t be able to handle. This brings me to the next point; how it personalizes.

We’ll use Starbucks for this one just because it’s super simple. The chatbot can access a customer's order history, it lets them customize things, it gives recommendations, and it’s a barista in your pocket. This is a system that’s going to set the standard for any local coffee shops now and guess what? The companies who leverage it better than others, will get more customers and retain them longer. 

Lastly, Lyft. They recognize that their market is heavily controlled by Uber - which puts a lot of pressure on them when it comes to the customer experience. At first glance, you can see that the Chatbot interface for Lyft closely resembles an iMessage chat which is certainly user-friendly, but how does it stand out? Well, Lyft beat Uber to market. Which has given them time to get some mileage on their Chatbot and optimize the user experience. 

The Takeaway

It’s no surprise that a lot of people’s knowledge and understanding of AI stops at Chat GPT. These Chatbot interfaces are setting a new standard for how people find and interact with information, which is now pouring over into the business world. Want to get behind the shift? Find out if your business is ready for AI today.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.