Okay, people, in today’s coding world, being a software developer means more than just writing code. It means being comfortable with various ai tools for developers (ChatGPT vs. Tabnine vs. GitHub Copilot), environments, and the latest tech.
It’s not just about hitting our daily targets or how fast we can launch a project. It’s about something we call the developer experience.
This is all about how smoothly and efficiently we can work, get into that ‘zone’, and make a real impact in software engineering.
In one of the old blog posts, I talked about the topic of ‘GitHub Copilot vs. Tabnine‘ Now, let’s continue our ai tools for developers learning journey as we dive deeper into the world of coding assistants and introduce a new contender, ChatGPT.
Why because in the vast expanse of AI-powered coding tools, choosing a right tool for your programming journey can be a big decision.
Learning often occurs about our existing knowledge, a natural and intuitive process. But, the most common question arises:
‘What sets this tool apart?’ However, to answer this, you must establish a reference point, allowing you to say, ‘Similar to X, but distinguished by Y.
So today, let’s do a comparison of 3 different ai tools for developers.
What is GitHub Copilot
Table of Contents
GitHub Copilot is like having a smart assistant for your coding work. It’s a special ai tools for developers that works very well with Visual Studio Code. Imagine it as a pair programmer that understands what you’re trying to do and writes code for you.
For example, if you want to create a function, you just give it a name and a little description, and Copilot (ai tools for developers) magically writes the rest of the function for you.
// Define a function using Copilot's assistance
function greet(name) {
/**
* Greets a person by their name.
*
* @param {string} name - The name of the person.
* @returns {string} A greeting message.
*/
return `Hello, ${name}!`;
}
// Example usage
const personName = "John";
const greetingMessage = greet(personName);
console.log(greetingMessage);
In this JavaScript example, we’re creating a function called greet that takes a name parameter and returns a greeting message. Copilot has provided the function structure, including the parameter definition and return statement, based on the provided description.
This displays how Copilot can assist developers in generating code quickly and accurately, saving time and reducing the need to write boilerplate code manually.
Copilot an ai tools for developers has trained on billions of lines of code, GitHub Copilot turns natural language prompts into coding suggestions across dozens of languages and GitHub Copilot offers two management options “individual accounts” with GitHub Copilot for Individuals or “organization accounts” with GitHub Copilot for Business.
Verified students, teachers, and maintainers of popular projects can enjoy free access to Copilot.
However, there’s something important to know. While GitHub Copilot used to be free for everyone, now they ask for a small fee to use it. If you are not a student, teacher, or maintainer of a popular open-source project, you can explore GitHub Copilot with a complimentary 30-day trial. Following the trial period, a paid subscription is required for ongoing access. Despite this, many people believe GitHub Copilot has a bright future. They think it could become a game-changer for coders. So, while there are some things to consider, GitHub Copilot (ai tools for developers) holds a lot of promise for making coding a lot easier.
GitHub recently conducted a survey focusing on developer productivity and the use of AI coding tools.
To gather insights, they enlisted the help of an independent research agency to interview 500 software developers based in the US. These developers were specifically from big tech organizations with over 1,000+ employees.
- High Adoption of AI Coding Tools:
- 92% of developers are already using AI coding tools, both at work and in personal projects.
- Persistent Challenge: Waiting on Builds and Tests:
- Despite DevOps advancements, developers still find waiting on builds and tests to be a significant time-consuming activity.
- Strong Desire for Increased Collaboration:
- Developers work with an average of 21 other engineers on projects, emphasizing the importance of collaboration in performance evaluations.
- Positive Outlook on AI’s Impact:
- Over 80% of developers believe AI coding tools will enhance collaboration within their teams.
- Anticipated Benefits of AI Coding Tools:
- Developers expect advantages like improved code quality, faster completion times, and more effective incident resolution.
- Misalignment in Performance Metrics:
- Developers believe factors like code quality and collaboration should be prioritized in performance evaluations, whereas some are still primarily evaluated based on output quantity.
- Importance of Developer Experience:
- Investing in a positive developer experience, including factors like collaboration, productivity, satisfaction, and impact, is crucial.
- AI Tools for Upskilling:
- 57% of developers believe that AI coding tools can help improve their coding language skills, offering a significant benefit.
- Efficiency Gains with AI Tools:
- AI coding tools are seen as tools that create greater efficiencies within existing workflows, allowing developers to focus on developing solutions.
- Potential for AI in Developer Experience Enhancement:
- AI coding tools have the potential to significantly impact developer satisfaction, productivity, and organizational impact.
These findings collectively highlight the widespread adoption and positive perception of AI coding tools among developers, along with the potential benefits they can bring to the development process and let’s move on to github copilot alternatives and check them.
What is Tabnine
Tabnine (github copilot alternative) an ai tools for developers is an AI-powered code completion tool designed to assist you in writing code more efficiently. It integrates with various code editors and IDEs (Integrated Development Environments) and offers intelligent suggestions for completing lines of code as you type.
Tabnine (github copilot alternative) an ai tools for developers analyzes the context and patterns in a codebase to provide accurate and contextually relevant suggestions, helping developers save time and reduce typing effort. It supports multiple programming languages.
// Example of Tabnine providing contextually relevant suggestions
// Suppose we have an array of colors
const colors = ["red", "green", "blue", "yellow"];
// We want to filter colors that contain the letter 'e'
const filteredColors = colors.filter(color => color.includes('e'));
// Tabnine suggests the 'includes' method based on context
In this JavaScript example, we have an array of colors, and we want to filter out the colors that contain the letter ‘e’. Tabnine analyzes the context and suggests the includes method, which is a relevant array method in JavaScript.
This illustrates how Tabnine can provide contextually relevant suggestions to assist developers in their coding process.
Personally, It’s one of the best github copilot alternative for writing, coding, and more.
Have a look at the another example for Tabnine.
Tabnine an ai tools for developers uses advanced generative AI technology to anticipate and propose the following lines of code by considering the context and syntax. This includes providing complete lines of code, suggesting entire function implementations, offering natural language to code translations, and even facilitating in-IDE communication through chat features.
What is ChatGPT?
ChatGPT an ai tool for software development is a powerful AI language model developed by OpenAI.
It leverages the GPT-3.5 and GPT-4 series of large language models (LLMs) developed by OpenAI and has undergone fine-tuning through transfer learning methods, utilizing both supervised and reinforcement learning techniques.
It is made to assist with a variety of tasks, including writing, answering questions, creating conversational agents, providing tutoring in various subjects, translating languages, and much more.
Think of ChatGPT as a versatile digital assistant and a github copilot alternative that’s capable of understanding and generating text conversationally.
You can now ask GPT4 to review your site with the [Browse with Bing ] plugin I asked it to review my website and it gives few good improvements based on what it saw.
This was the complete answer given by GPT4.
- Professional Appearance:
- Your website has a neat and professional appearance with a clean layout, which is good.
- The use of images and white space is balanced, making the page visually appealing.
- Content:
- The content on your website is well-organized and informative, especially for individuals interested in software development and blogging.
- Your blog posts are detailed and cover a variety of technical topics, which can help in establishing your expertise in the field.
- It might be beneficial to include more visual elements like infographics or videos in your blog posts to cater to different learning styles.
- Navigation:
- The navigation menu is straightforward and easily accessible.
- However, it might be helpful to have a more detailed footer with additional links to important pages, a search function, or categories/tags to help visitors find the content they are interested in more easily.
- Engagement:
- Your website offers a variety of engagement points, including a newsletter and your books. This is great for building a community.
- It could be beneficial to include a comments section under your blog posts to foster discussions and engagement from your readers.
- Loading Speed:
- I didn’t experience any loading issues, which is good as loading speed is crucial for user experience and SEO.
- Mobile Responsiveness:
- I didn’t check the mobile responsiveness of your website. Ensure that your website is mobile-responsive to cater to users on various devices.
- SEO (Search Engine Optimization):
- Consider focusing on SEO to improve your site’s visibility on search engines. This includes optimizing your site’s loading speed, ensuring mobile responsiveness, and using relevant keywords within your content.
- Call to Actions (CTAs):
- Your website has clear CTAs for your blogging service and book offerings, which is good.
- It might be worth reviewing the placement of these CTAs to ensure they are in optimal positions to catch the visitor’s attention.
- Testimonials:
- The testimonials section is a good touch as it provides social proof. You might want to consider adding pictures of the individuals who provided the testimonials to make it more personable.
- Analytics:
- If not already done, integrating analytics tools like Google Analytics can help in understanding your audience better and making data-driven decisions.
- Newsletter Signup:
- Consider placing a newsletter signup form directly on your website besides linking to an external platform. This might improve the signup rate.
- Contact Information:
- Your contact information is visible which is good for transparency and trust. Consider adding a contact form for easier communication.
A big limitation of ChatGPT an ai tool for software development is that it was trained on data leading up to late 2021, and does not know the world after this time. It’s important to understand that ChatGPT, by default, utilizes the text you input through the web interface for its training. This means that any information provided is used to enhance its capabilities. There have been instances, such as at Samsung, where sensitive information was unintentionally disclosed when using ChatGPT to generate meeting notes.
Additionally, it’s worth noting that ChatGPT retains user data, even for those who are subscribed or paying for its services. If you’re uncomfortable with this arrangement, it’s crucial to take steps to opt out of data retention.
My Insights on (ChatGPT vs. Tabnine vs. GitHub Copilot) in a one Sentence
ChatGPT excels in natural language understanding and generation, making it a versatile conversational AI, while Tabnine is a proficient code completion tool leveraging generative AI, and GitHub Copilot stands out as an integrated powerhouse for AI-powered code suggestions.
- ChatGPT: A versatile AI companion or pair programmer
- Tabnine: Turbocharge your day to day coding experience in software development.
- GitHub Copilot: Revolutionizing code creation in software development.
Do these AI tools (ChatGPT, Tabnine, and GitHub Copilot ) make a developer more efficient?
- ChatGPT
- Helps developers quickly generate code snippets or get assistance with coding-related queries, saving time and effort.
- Offers contextual suggestions and explanations, aiding in problem-solving and reducing debugging time.
- Enhances productivity by providing an extra layer of assistance during the development process.
- Tabnine
- It employs advanced generative AI to swiftly predict and propose the subsequent lines of code, significantly expediting the coding process.
- Offers whole-line and full-function code completions, improving code-writing speed and accuracy.
- Facilitates natural language to code translations, making it easier to convert ideas into functional code.
- GitHub Copilot
- Employs AI-powered code completion to automatically suggest and generate code snippets as developers type, significantly reducing manual coding efforts.
- Speeds up the development process by offering accurate and contextually relevant code suggestions.
- Enhances collaboration and reduces the time spent on routine tasks, allowing developers to focus on higher-level problem-solving.
Overall, these tools streamline coding tasks, provide intelligent suggestions, and assist in problem-solving, ultimately leading to increased efficiency and productivity for developers.
What are areas where these AI tools are not that helpful?
In my experience, since I started using tools like ChatGPT, Tabnine, and GitHub Copilot excels at assisting with routine coding tasks and providing helpful suggestions, they may fall short when it comes to tackling highly complex algorithmic challenges.
Developers often need solutions that not only work efficiently but are also optimized for factors like speed, memory usage, and scalability.
Developers still rely heavily on their expertise and domain-specific knowledge to craft solutions that meet the demands of the task. While AI tools can provide valuable assistance, they may not fully replace the need for human ingenuity and specialized algorithmic expertise in these scenarios.
Let’s consider a scenario where a software developer needs to implement a highly specialized algorithm for finding the shortest path in a weighted graph, a classic problem in graph theory.
This task requires a deep understanding of algorithms like Dijkstra’s or A* and their specific implementation.
Here’s an example of a Python function
import heapq
def dijkstra(graph, start):
distances = {vertex: float('infinity') for vertex in graph}
distances[start] = 0
priority_queue = [(0, start)]
while priority_queue:
current_distance, current_vertex = heapq.heappop(priority_queue)
if current_distance > distances[current_vertex]:
continue
for neighbor, weight in graph[current_vertex].items():
distance = current_distance + weight
if distance < distances[neighbor]:
distances[neighbor] = distance
heapq.heappush(priority_queue, (distance, neighbor))
return distances
# Example Usage
graph = {
'A': {'B': 1, 'C': 4},
'B': {'A': 1, 'C': 2, 'D': 5},
'C': {'A': 4, 'B': 2, 'D': 1},
'D': {'B': 5, 'C': 1}
}
start_vertex = 'A'
shortest_distances = dijkstra(graph, start_vertex)
print(shortest_distances)In this example, This algorithm requires a deep understanding of graph theory and specialized knowledge of the algorithm itself.
While AI-powered coding tools can certainly assist with more routine coding tasks, developing and optimizing algorithms like Dijkstra’s often requires a level of expertise and domain-specific knowledge that may not be easily replicated by AI models.
Conclusion
The integration of powerful language models and AI coding assistants like ChatGPT, Tabnine, and GitHub Copilot is reshaping the landscape of software development. Many engineers report heightened productivity and accelerated learning of new languages, libraries, and frameworks.
These tools have proven invaluable in streamlining routine tasks, providing context-aware code suggestions, and enhancing collaboration among development teams.
I highly recommend reading Gergely Orosz’s blog post for further reading. He writes about software engineering and startups.
And, if you enjoyed this post, why not read an in-depth comparison of Github Copilot vs Tabnine?
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