An AI coding assistant is a type of software tool that uses artificial intelligence (AI) to help developers write and debug code more efficiently. These tools can be used to provide suggestions and recommendations for code improvements, detect and fix errors, and offer real-time feedback as the developer is writing code.
AI code assistants can be integrated into an integrated development environment (IDE) or used as standalone tools. They use machine learning algorithms and natural language processing (NLP) to analyze code and provide intelligent recommendations based on best practices and coding standards. Some AI code assistants also have the ability to learn from the developer’s past coding habits and style, making their recommendations more personalized and relevant.
Increased productivity: Automate repetitive tasks, such as code completion and error checking, allowing developers to focus on more complex tasks.
Improved code quality: Identify and flag potential errors and bugs, reducing the likelihood of introducing new ones.
Streamlined collaboration: Help developers navigate large code bases, find relevant code snippets, and understand the intent of existing code.
Personalization: Some AI code assistants can be trained on specific code patterns and style, providing personalized recommendations and suggestions.
Here are some notable use cases for an AI-powered code assistant:
Web development: Assist with code completion, error checking, and debugging for web development languages such as JavaScript, HTML, and CSS.
Machine Learning (ML): Assist with code completion, error checking, and debugging for machine learning frameworks such as TensorFlow, Keras, and PyTorch.
Mobile development: Assist with code completion, error checking, and debugging for mobile development languages such as Swift and Kotlin.
Data Science: Assist with code completion, error checking, and debugging for data science languages such as Python and R.
An AI code completion tool provides recommendations to developers as they write code, helping them spot and correct issues without requiring manual checks. Fixing coding errors early in the code lifecycle is important to reduce the cost and technical burden of the software. Bugs become more difficult to address later in the development lifecycle and can negatively impact end-users once deployed to production.
Enforces coding best practices – code completions are more readable and consistent, improving code maintainability and ensuring the code is more reliable.
Reduces code review workload – coding assistants perform basic code reviews, leaving more time for human reviewers to identify deeper issues in the code, implement best practices and debug business logic issues.
Promotes use of safe libraries – coding assistants base their recommendations on common programming practice, so will typically recommend popular, well maintained third party APIs and libraries, which can reduce issues in production.
Promoting safe code reuse – some AI code assistants train on code previously created in an organization’s private code repositories. This means suggestions are based on trusted code that meets the organization’s quality standards – reducing the risk of low quality dependencies and bugs in third party libraries.
Here are some factors to consider when choosing an AI code assistant software:
Compatibility: Make sure the AI code assistant is compatible with the programming languages and technologies you are using.
Features: Consider the features that are most important to you, such as error detection, code completion, and real-time feedback.
Personalization: Evaluate if the solution is able to train on your organization’s codebase and integrate your individual best practices and quality standards into its code completions.
Integration: If you are using an integrated development environment (IDE), check to see if the AI code assistant integrates seamlessly with your IDE.
Ease of use: Look for an AI code assistant that is easy to use and has a user-friendly interface.
Cost: Consider your budget and compare the costs of different AI code assistants to find the one that fits your needs and price range.
Support: Look for an AI code assistant that offers good customer support, such as online documentation, tutorials, and community forums.
Reputation: Read reviews and ask other developers for their recommendations to get a sense of the reliability and effectiveness of different AI code assistants.
Minimizing production errors is of utmost importance in software development as it guarantees a dependable and stable application for end-users. Tabnine, an AI-based code completion tool, aids developers in reducing production bugs by offering suggestions for code snippets based on the current context and team-specific coding standards. With over a million developers from various companies utilizing Tabnine as their AI assistant, it has become a widely adopted tool in the software development community. Tabnine Enterprise is specifically designed for use in corporate software development environments, offering enhanced security and compliance features, as well as support for multiple programming languages and integrated development environments. By utilizing Tabnine, developers can focus on complex problem-solving while the tool handles routine tasks, resulting in fewer production bugs and a faster, more efficient software development life cycle. If you’re looking to enhance your software development practices while prioritizing privacy and security, please reach out to us.
The field of generative AI, featuring models like ChatGPT, is rapidly expanding in the realm of software development, potentially changing the way we tackle coding and problem-solving.
Tabnine recently held a Twitter space discussion on generative AI, with CTO Eran Yahav and moderation by Lisa Zigel. The topics covered included the current status of generative AI in software development, the emergence of models like ChatGPT, potential future uses, and how AI gives developers superpowers!
Other subjects discussed included the definition of software engineering, AI in the different stages of the SDLC, and the important role that AI will place in code review and testing.
Eran Yahav, the head of technology at Tabnine and a computer science professor at the Technion in Israel, is an expert in areas such as program synthesis, machine learning in code, program analysis, and verification. He’s recently spoken about the major effects of AI in software development and automating processes. To learn more about incorporating AI into your software development team, visit Tabnine Enterprise.
Both Tabnine and Copilot offer AI coding assistant solutions for R&D teams. So which solution is better?
We’ve put together a comparison using the most common parameters that we’ve encountered in our work as developers, while also serving the dev community for several years. From our POV, these parameters reflect the most important needs, pain points, and challenges that must be addressed by AI code assistants for enterprises:
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Let’s look at the parameters and how each solution compares.
Tabnine Enterprise offers full privacy for its customers’ code:
This ensures you always have complete control over your IP and protection against IP leakage.
On the other hand, Copilot for Individuals and Copilot for Business send both code snippets and user engagement data to Microsoft.
With Copilot, it’s possible to opt out of saving code snippets; however, user engagement data is collected and used to improve Copilot’s AI models.
The code on which a solution’s AI models are trained can have a serious impact on the companies that use the solutions.
Tabnine doesn’t train (and never has trained) its AI models on code with nonpermissive licenses and offers full transparency and attribution. This means that Tabnine isn’t restricted by the GPL license’s copyleft provisions, and protects users and customers from possible related consequences. In addition, it’s always been Tabnine’s goal to honor the intent of code authors and maintain good faith with the rest of the developer community.
On the other hand, Copilot trains its models on OpenAI, which could result in legal exposure for its customers. This is because OpenAI is trained on nonpermissive open source, such as GPL, and doesn’t disclose its training set or provide references. Copilot does offer a filter to remove code suggestions that contain problematic code; however, it’s not always effective, meaning that the legal risk remains.
While Copilot is trained only on OpenAI, Tabnine Enterprise allows customers to connect Tabnine’s AI models to their code repositories. This means the models, which are only accessible to the customer, can learn the organization’s coding best practices, styles, naming conventions, and more, resulting in code suggestions that are more context-sensitive and relevant. In addition, this allows for faster onboarding and training of new team members and junior developers.
Both Tabnine Enterprise and Copilot Business offer central management and configuration; however, the features offered are different.
Tabnine Enterprise’s centralized configuration allows customers to:
Copilot Business’s centralized configuration allows customers to:
Copilot for Individuals doesn’t offer centralized configuration and allows customers to:
On the surface, the price appears to be almost the same ($20 per Tabnine Enterprise user, $19 per Copilot Business user).
However, Copilot Business is only available to companies using GitHub Enterprise, which charges $210 per seat per year. In effect, that’s a barrier for enterprise companies that don’t need or want GitHub Enterprise.
Copilot for Individuals costs $10 per month (or $100 per year). However, the Individual plan doesn’t include any of the features suited for enterprise, including privacy and security.
Both Tabnine Enterprise and Copilot Business allow enterprise customers to configure and manage user roles and permissions. However, Copilot for Individuals doesn’t offer any user management capabilities.
Whether you’re choosing an AI coding assistant to make your life easier as an individual developer or choosing a tool to deploy to your entire engineering team to improve the effectiveness and satisfaction of your team, it’s critical to evaluate potential vendors holistically:
Only Tabnine meets all of these requirements expected by enterprise engineering teams and has the history and scale of developer adoption to prove it.
Since launching our first AI coding assistant in 2018, Tabnine has pioneered generative AI for software development. Tabnine helps development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy and security. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. With more than one million monthly users, Tabnine typically automates 30–50% of code creation for each developer and has generated more than 1% of the world’s code.
Unlike generic coding assistants, Tabnine is the AI that you control:
Tabnine ensures the privacy of your code and your engineering team’s activities. Tabnine lives where and how you want it to — deployed as protected SaaS for convenience, on-premises for you to lock down the environment, or on a virtual private cloud for the balance of the two. Tabnine guarantees zero data retention, and we never use your code, data, or behaviors to feed our general models.
Tabnine is personalized to your team for optimal impact on your business. Tabnine is built on best-of-breed large language models (with the flexibility to switch as new models emerge or improve) and gives you the ability to fine-tune or deploy fully customized models. Tabnine is context-aware of your code and patterns, delivering recommendations based on your internal standards and engineering practices.
Tabnine works the way you want, with the tools you use. Tabnine supports a wide scope of IDEs and languages, improving and adding more all the time. Tabnine also provides engineering managers with visibility into how AI is used in their software development process and the impacts it is having on team performance.
Tabnine is secure and trusted. Tabnine believes in building trust through algorithmic transparency and thus shares how our models are built and trained with our customers. Furthermore, we’re relentlessly focused on protecting our customers’ interests by only training on code with permissive licenses and only returning code recommendations that will not be subject to future questions of ownership and potential litigation. We respect open source code authors and their rights as well as the rights of every one of our customers.
As you should expect from any vendor, Tabnine offers proven, enterprise-grade security and meets key industry standards.
Get started with Tabnine for free for 90 days, or contact us to learn how we can help your engineering team be happier and more productive.
One of the key benefits of generative AI is its ability to create new content that is diverse and unique. Unlike traditional AI models, which are limited to the data they are trained on, generative AI can explore a wider range of possibilities and produce outputs that may be surprising or unexpected. This allows it to generate content that is more creative and engaging than what is possible with traditional AI techniques.
One way that generative AI is being used today is in the field of natural language processing (NLP). NLP is the study of how computers can understand, interpret, and generate human language. With generative AI, it is possible to create models that can generate new sentences and paragraphs that are coherent, grammatically correct, and even engaging. For example, ChatGPT is a generative AI model trained on a large corpus of text data that can be used to generate human-like responses in a chatbot.
Another area where generative AI is making an impact is in the field of code generation. Tools like Tabnine use generative AI to automatically suggest code completions, making it easier and faster for developers to write code. By training a generative AI model on a large dataset of code, it is able to generate new, syntactically correct code that can be used to speed up the development process.
As an example of the quality of generative AI, this blog post was written by a generative AI model trained on a large dataset of text. The model was able to understand the topic and generate a coherent and engaging article, demonstrating its ability to create unique and original content.
Overall, generative AI is a powerful and versatile tool that is making an impact in a variety of fields. By enabling computers to generate new content, it has the potential to unlock new possibilities and create new opportunities for creativity and innovation.In addition to the applications of generative AI mentioned in the previous paragraphs, there are many other tools and systems that use generative AI to make our lives easier and better. For example, Tabnine is a code-completion tool that uses generative AI to automatically suggest whole lines and full function code completions, making it faster and easier for developers to write code. This can save a lot of time and effort, and can even help prevent errors and bugs in the code.
Tabnine also offers a secure and private Enterprise solution, with options for self hosting, making it suitable for use in sensitive environments such as financial institutions or government agencies. This allows organizations to take advantage of the benefits of generative AI without sacrificing security or privacy.
Overall, generative AI is a powerful and versatile tool that is making an impact in many different areas of our lives. Whether it’s making it easier to write code, creating new images, or generating unique and engaging content, generative AI is helping us to unlock new possibilities and create new opportunities.
Here are the top five challenges that generative AI systems face:
Data scarcity: One of the biggest challenges facing generative AI systems is the lack of large, high-quality training datasets. These systems require a large amount of data to learn from and generate new content, but it can be difficult to find and annotate large datasets.
Mode collapse: Another challenge is mode collapse, which is when a generative AI system only generates a limited number of outputs, rather than a diverse range of outputs. This can lead to repetitive or low-quality outputs.
Bias and fairness: Like any AI system, generative AI systems can be susceptible to bias and unfairness. For example, if the training data is biased, the generated content may also be biased. Ensuring that generative AI systems are fair and unbiased is an ongoing challenge.
Control and interpretability: Generative AI systems can produce surprising and unexpected results, which can be difficult to control and interpret. This can be a challenge when using these systems in sensitive or regulated industries, such as healthcare or finance.
Adversarial attacks: Finally, generative AI systems are vulnerable to adversarial attacks, where an attacker tries to trick the system into generating incorrect or malicious outputs. Defending against these attacks is a significant challenge for generative AI systems.
Dalle-2 image generated forTwo generative AI figures meet and fall in love, MC escher style, digital art
Tabnine is an AI assistant tool used by over 1 million developers from thousands of companies worldwide. Tabnine Enterprise has been built to help software engineering teams write high-quality code faster and more efficiently, accelerating the entire SDLC. Designed for use in enterprise software development environments, Tabnine Enterprise offers a range of features and benefits, including the highest security and compliance standards and features, as well as support for a variety of programming languages and IDEs.
Harnessing the combined power of both cloud and local servers is essential for providing the best possible code prediction experience for our users.
That’s why we’re excited to introduce Tabnine’s new Hybrid model, which uses both Cloud and Local inference. As of June 1, 2022, Tabnine’s new hybrid model will be enabled by default for all new installations, meaning that Tabnine will use both local machines and our cloud servers in tandem to provide contextual code completions.
Tabnine’s cloud offering has greatly evolved over the past year, partly due to a significant increase in the size of models and model specialization. Effective inference with these models cannot be run completely locally.
Our cloud models which require network connectivity, allow you to use Tabnine’s servers for GPU-accelerated completions, offering more accurate and longer predictions.
Local models run without network access and rely on local resources; as a result, they’re not as powerful as the cloud models.
Tabnine’s new Hybrid Model combines the benefits of both.
Although we recommend keeping the hybrid model enabled, you can easily switch to cloud or local mode at any time. Just visit the Tabnine Hub to change the default configuration.
Tabnine continues to place the highest value on your privacy, never storing nor sharing any of your code. All communication with the cloud is strongly encrypted. Learn more about Tabnine’s code privacy here.
This new hybrid model is an important step in our quest for providing more and more powerful coding experiences for our users, but it’s just one of many to come.
Stay tuned for more news coming soon…
Eran reacts to a recent quote from AI Pioneer Geoff Hinton: “Deep learning is going to be able to do everything.” He points out that the limitations to “everything” won’t be the computational ability but the access to sufficient data. Kyle challenges whether the outcomes of ML decisions will be acceptable in life if they’re less intuitive (even if technically more optimal). The conversation broadens to the classic questions of distinctions between AIs and humans (feelings, emotions, empathy), and whether we’re headed to a future that freedom of choice is subjugated to the “superiority” of machine-learning-based decision making.
Kyle shares concerns about how AI/ML have already surpassed what humans can understand. Eran talks about how AI can not only make its predictions based on data sets that exceed our mental capacity, but also leave some explanation breadcrumbs that humans can understand and audit. The discussion shifts to exploring the practicality of AI translating programs from one language to another, and even generating a higher level, idealized or universal “one language to rule them all”.
Kyle ponders how AI tools might in the future better understand our programs, and adjust and augment them as we write code. Eran shares some predictions about how that might play out. The discussion broadens to whether AI-refined code may move programs (and programming!) in a productive or destructive direction, and whether (and how!?) tools can keep assisting developers instead of replacing them.
YouTuber and JavaScript Specialist Ania Kubów took the time and effort to help her dev community decide: which AI code completion tool they should use to get their code done better, and faster. In order to get a fair result, Ania threw a thrilling showdown:
Kite vs. Tabnine – The Ultimate Battle
In the video, Ania displays a split-screen featuring 2 recorded sessions from her code editor. The exact same code is being written in VSCode on both sides of the screen, with only one difference – Tabnine is installed and activated on the left-hand side, and Kite is active on the right-hand side. Can you guess who took the trophy? Press play and find out.
Read more from the blog: Github Copilot vs. Tabnine