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Can AI Write Code as Well as Humans & 24 AI Tools to Help

In the fast-paced world of software development, the concept of AI Copilots is making waves. Can AI write code? The question is not just a theoretical pondering but a practical consideration for many in the tech industry. As AI continues to evolve at a rapid pace, the idea of AI Copilots is becoming more viable. Developing software can be time-consuming and challenging, and AI Copilots could potentially save developers time and effort, allowing them to focus on the more creative and challenging aspects of coding. If AI Copilots can reach the point where they can write code, what impact will it have on the industry? Are AI Copilots the future of software development? Let's explore.


What is AI-Generated Code?

Creating AI-generated code involves utilizing advanced machine learning models to generate and manipulate code. These models are trained to understand programming languages and functions, allowing them to assist programmers in writing, editing, and improving their code efficiently. 


The AI tools used to generate code work by processing large amounts of data, which enables them to predict accurate code based on input from the user. By training these models with high-quality data related to programming, the AI software can produce reliable code and perform a variety of coding tasks.


Tasks Performed with AI-assisted Coding

AI-assisted coding tools are capable of performing a range of tasks to help programmers develop code more effectively. These tasks include code analysis, code completion and generation, error detection and correction, and integration with development environments. 


Code analysis allows AI models to understand the structure, patterns, and semantics of the code, enabling them to analyze and interpret code accurately. Code completion and generation features help generate code snippets and predict likely code sequences based on learned patterns. Error detection and correction are achieved by identifying syntax and semantic errors in the code and making suggestions for corrections or optimizations. Integrating these AI models with development environments allows them to provide real-time assistance to developers, offering suggestions and corrections as they code.


Ethical considerations and Guidelines for AI-generated code

As AI-generated code is a relatively new concept, ethical guidelines and considerations are still being developed to address the use of AI in coding. AI-generated content raises questions about the ethical use of AI code alongside human-written code, considering factors like accuracy, reliability, and best coding practices. As the field of AI and coding continues to evolve, guidelines and ethical considerations will play a crucial role in determining how AI-generated code is used in practice.


Can AI Write Code as Well as Humans?

AI struggles with variations of coding as it lacks judgment or basic sense, making it challenging for AI to find the best ways to write code, although it can understand the problem statement. AI can process syntax and detect grammatical errors but may fall short when it comes to reasoning or making assumptions like humans. 


AI can recognize what the program needs to do but lacks the ability to identify the best ways to achieve the goal. Researchers are optimistic that AI will be able to write code as well as humans by the end of 2023, but it still has a long way to go in writing complex code. 


AI Faces Challenges in Writing and Analyzing Programs Compared to Humans

Although AI has progressed significantly and shows promise, it will take time before AI can code as proficiently as humans. Coding requires some human intervention, making it questionable whether AI can write programs as effectively as humans. AI may eventually reach a level where it can understand and learn codes similar to humans, paving the way for writing new programs. 


The technology is still in its early stages, requiring further advancements to reach that level. Some AI tools can write code in multiple programming languages, but their accuracy remains uncertain and may require manual testing and correction at times.


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Try our free AI copilot today — just head to omnipilot.ai and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.


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24 AI Tools That Can Generate Code To Help Programmers

1. Omnipilot

Imagine an AI that can type anywhere on macOS, with full context of what's on your screen. Imagine if, your email wrote itself, your terminal was fluent in Bash, your team updates became effortless. You don’t have to imagine any of this anymore. You can download Omnipilot’s MacOS AI Copilot for free right now and use AI anywhere on MacOS! 


Try our free AI copilot today — just head to omnipilot.ai and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.


2. OpenAI Codex

The most well-known AI coding tool currently accessible is OpenAI Codex, a powerful AI code generator. It was trained using vast code and built on the LLMs (GPT-3 and GPT-4) from OpenAI, naturally incorporating the ability to understand and generate code. OpenAI Codex promises to write programs in over 12 languages, including Go, TypeScript, Perl, PHP, BASH, Ruby, JavaScript, and Swift. The algorithm has been developed on trillions of lines of openly accessible code from sites such as GitHub repositories, making it a versatile and robust tool for developers and programmers.


3. Tabnine

Tabnine is an efficient AI code generator online created by Codota. This code generator uses machine learning approaches to predict and recommend completion, making coding faster, more effective, and less error-prone. Since it uses deep learning abilities to offer highly accurate code recommendations, it is an essential resource in every developer’s toolbox.


 Your writing habits are used to generate personalized suggestions, and your code is automatically indexed. Its sophisticated AI capabilities offer features like automatic code completion, auto-generated tests, syntax highlighting, and interface with well-known IDEs.


4. CodeT5

CodeT5 is a platform that enables developers to produce AI-generated code for their particular projects. It is a free AI code generator online which is easily accessible to developers around the world. The tool analyzes project requirements using natural language processing (NLP) and machine learning approaches to provide the best code attainable using pre-trained models. It can complete incomplete code, summarize, and convert between several programming languages.


5. Polycoder

Polycoder is one of the initial open-source AI-powered code generators that focuses on writing code for specific programming tasks. PolyCode is a potent AI code generator that is freely accessible on GitHub. It uses complex algorithms for natural language processing and code creation. The datasets now available on GitHub are more extensive than 200GB, and the models are continuously updated with the help of solid hardware.


6. GitHub Copilot

GitHub Copilot, created by GitHub and OpenAI, is the newest advancement in AI-powered programming support. This tool swiftly helps developers write better code by acting as a virtual duo programmer. You can use it to help complete code, code clarification, debugging, and other functions. Compared to Codex, Copilot generates more comprehensive code that covers more programming language models.


7. GhostWriter Replit

Replit GhostWriter, created by Replit, is a powerful AI code generator to help programmers create effective, high-quality code. GhostWriter distinguishes for its capability to finish producing boilerplate code as the developer writes automatically. This saves time wasted looking for syntax problems and writing boilerplate code. It is well-known for its browser-based IDE, which supports native hosting and co-coding within documents


8. Seek

Seek is an AI code generator that provides developers with various code designs. It works on the concept of “code snippets,” which lets developers use pre-existing templates and customize them to fulfill the requirements of their project. It is an ideal AI code writer for programmers and data analysts who want to instantly create SQL queries without manually writing code. Seek’s approach provides more adaptability and customization when creating SQL code for training data.


9. AI2SQL

The AI2SQL code generator, a powerful tool in the realm of artificial intelligence, was specifically designed to translate naturally posed natural language inquiries into SQL statements. AI2sql minimizes the requirement for a comprehensive understanding of SQL syntax by effortlessly translating natural language into SQL, thereby making database interactions more approachable to a broader audience. 


Python programmers, data scientists, and company executives who are required to explore databases for insights can all benefit from the capabilities of this innovative AI code generator


10. Cody by Sourcegraph

Cody, a built-in AI coding helper, is designed to facilitate “ai code generation naturally.” It excels at locating, interpreting, and generating code by harnessing a combination of Large Language Models (LLMs), Sourcegraph search, and Sourcegraph code expertise. 


By leveraging these tools, Cody significantly reduces the need for manual labor, allowing human programmers to stay focused and productive. One of Cody’s standout features is its innate ability to understand the context in which the code is being generated, empowering it to provide insightful and relevant comments and feedback


11. MutableAI

According to their official website, Mutable is an AI code generator online that employs NLP and machine learning to boost software developers’ efficiency and contentment. It provides auto-complete features for several programming languages, including Java, Python, Typescript, Rust, and Solidity. It is explicitly made to produce usable front-end code from unfinished design files.


12. Amazon CodeWhisperer

CodeWhisperer by Amazon elevates real-time code generation, suggesting snippets to entire functions based on your comments and existing code. Embracing natural language commands, it extends support to CLI completions and effortlessly translates natural language into bash commands. The free CodeWhisperer Individual Tier includes code suggestions, reference tracking, security scans, and Amazon Q conversational coding.


13. Codeium

Codeium, an AI-powered code completion tool, redefines the coding landscape, enhancing speed and accessibility. Integrated with popular IDEs and supporting over 20 languages, Codeium enables developers to navigate unfamiliar codebases effortlessly, eliminating the need to delve into syntactical intricacies.


14. Cogram

Cogram is a startup from Berlin’s Y-Combinator incubator that creates code for data scientists and Python programmers using Jupyter Notebooks and SQL queries. English-language queries may be written by data scientists and converted by the tool into sophisticated SQL queries with joins and grouping. It works with MySQL, SQLite, PostgreSQL, and Amazon Redshift. 


Cogram may be integrated with Jupyter Notebooks by Python and Julia developers to automatically produce code. The tool may provide contextual code for a particular job based on the remarks. Even common Python modules like Matplotlib, Plotly, or Seaborn may be used by data scientists to create visualizations.


15. Kite

For Python, Kite is a free AI-powered code completion tool. You will get real-time code completions thanks to machine learning. For a fee, Kite also provides access to premium services, including sophisticated code analysis and refactoring tools. Kite stands out from the competitors since it supports more than 16 languages and 16 code editors. The regular updates to Kite make this machine-learning code aid more dependable and economical than the competition.


16. DeepCode

DeepCode is a code review tool powered by AI that examines your code and makes suggestions for improving it. Code completion, refactoring, and lining are among its many capabilities. For open-source projects, DeepCode is free, while a premium membership is available for private enterprises.


17. CodeWP

The WordPress code generator CodeWP was created by Isotropic, which is who we are. This platform offers JS and PHP support and settings tailored to well-known plugins like WooCommerce and major page builders. It is particularly designed and optimized for those who construct WordPress websites. CodeWP makes it simple to write sophisticated queries, produce shortcodes, and finish activities that would normally take hundreds of minutes to hours to precisely accomplish. It is aimed at both developers and “nontechnical” WordPress designers.


18. AskCodi

AskCodi is a comprehensive toolkit that uses AI to assist programmers and developers in creating more quickly. It has a variety of settings, ranging from producing code in several languages to making documentation using already written code. It covers every development process, from creating documentation to writing code! You may create SQL queries from scratch and create several tests quickly. These various feature sets aim to improve workflow efficiency by using AI to assist all developers in iterating and shipping quicker.


19. Visual Studio IntelliCode

Microsoft’s IntelliCode is a product that allows AI-assisted coding. It is pre-integrated with Visual Studio, a Microsoft IDE. It is compatible with Java, Python, JavaScript, and TypeScript in Visual Studio Code, while it supports C# and XAML in Visual Studio. This AI code completion tool, was trained on the codes of 500,000 open-source projects on GitHub that have at least 100 stars. As a result, it can provide you with wiser recommendations while considering the context and present code.


20. PyCharm

Want to work more efficiently while creating the most complex software? Utilize PyCharm, an integrated programming environment designed specifically for Python programmers. It allows you to further develop the AI code completion capability. With PyCharm’s keyboard-centric design, you may complete your coding tasks faster and concentrate on more essential tasks. It enables rapid repairs, error checks while moving, smooth project navigation, and intelligent code completion.


21. AIXcoder

AI technology handles your codes so you may utilize your brains to address more difficult and important issues. An intelligent programming pair for your development team is called AIXcoder. It employs deep learning models trained using millions of lines of open-source code. As a result, it may provide principles suited to your situation. 


Use the offline version to keep your code secure on your computer if you are worried about data security and privacy. You may accomplish more throughout the development process with the help of this code completion engine’s support for individualized deep customization for your team or company. It compiles coding statistics to provide you with custom code inspection criteria.


22. Ponicode

Do you want to create software that is free of errors and deliver it on time? Then, it’s time to use Ponicode to empower your team. With this method, you can develop code more quickly while still keeping the highest level of quality with the least amount of work. After less than a minute of installation, you may start the app creation process, which will be easier than it was. 


It will assist you in getting rid of tedious operations by balancing creativity and quickness with high-quality coding. You may now create industrial-grade software free of bugs and faults and with future-proof code. This solution is free to use with Java, Python, JavaScript, and TypeScript programming languages.


23. Jedi

Another open-source option on this list of AI code completion solutions is Jedi. It is mostly a plugin for Python static analysis tools for IDEs and editors. It uses its other capabilities, like goto functionality, search and find, refactoring, and so on, even though it concentrates on code auto-completion. With its straightforward API, you may use it with various editors and projects, including Visual Studio Code, Sublime Text, Vim, Emacs, Kate version 4.13 and above, Atom, GNOME Builder, Gedit, and IPython.


24. Wing Pro

You should use the clever editor Wing Pro to quickly code in Python. This editor examines static and dynamic code to provide excellent, context-relevant recommendations.It offers you a better editing experience with a clever error-checking tool. The editor’s auto-completion functionality and built-in Python shells are both available. 


This tool includes a Source Assistant that constantly updates to provide inline documentation, type information, and call suggestions. As you code, it also automatically inputs function and method parameters. Wing Pro also allows you to browse the invocation and appropriately put your parameters.


7 Benefits of AI-Assisted Coding

1. Elevated Efficiency and Productivity

AI-assisted coding tools can help speed up the coding process, reduce errors, and minimize debugging. This allows developers to focus on more complex aspects of software design, leading to better results.


2. Enhanced Coding Precision

AI tools can improve coding accuracy by offering real-time suggestions based on best practices and historical data. They can also help identify and prevent common errors, making the code more secure and reliable.


3. Diminished Learning Curve

AI coding tools can provide contextual guidance and suggestions, making it easier for developers at all levels to learn and write code efficiently.


4. Optimized Code Quality and Dependability

By reducing errors and improving accuracy, AI tools can help developers create more reliable and efficient software.


5. Enhanced Collaboration and Standardization

AI tools can help ensure consistent coding standards and foster efficient teamwork among developers.


6. Accelerated Deployment and Iteration

AI tools can speed up the deployment and iteration process, allowing for faster code reviews and updates, giving companies a competitive edge in introducing new features to the market.


7. Strategic Competitive Advantages

AI tools offer various strategic advantages like code optimization, automatic bug detection, smart debugging, and more, leading to time savings and more efficient development processes.


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5 Challenges and Limitations of AI-Generated Code

1. Code Quality and Correctness

The first limitation of AI-generated code is the concern over quality and correctness. Like any code written by humans, code generated by AI may contain errors, bugs, or inefficiencies. While the code may be syntactically correct, there could be logical errors that lead to incorrect or suboptimal solutions. AI models may not fully understand the problem domain or requirements, which can result in code that does not align with the intended purpose or scope.


2. Security Vulnerabilities

The second limitation involves security vulnerabilities. AI models may inadvertently introduce vulnerabilities into the generated code due to a lack of awareness of security best practices or potential attack vectors. Blind spots in the training data could lead to the reproduction of insecure code patterns, putting systems at risk of exploitation.


3. Lack of Maintainability and Scalability

Another limitation to consider is the lack of maintainability and scalability of AI-generated code. Without proper documentation and comments, the code becomes challenging to maintain and extend. As the codebase grows, AI-generated code may become increasingly difficult to integrate and refactor. This lack of adherence to software design principles and architectural patterns can hinder long-term scalability.


4. Bias and Ethical Concerns

The potential for bias and ethical concerns is also a significant limitation of relying on AI for coding. AI models can inherit biases present in their training data, which can lead to biased or discriminatory code. The lack of transparency and accountability in AI-generated code can result in unintended consequences or misuse.


5. Limited Generalization and Domain Specificity

AI models are typically trained on specific domains or programming languages, limiting their ability to generalize across different domains. Specialized domains or novel problems may require significant fine-tuning or retraining of the AI model to achieve satisfactory results. This domain specificity can pose challenges when tackling diverse coding tasks beyond the trained scope.


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5 Best Practices for Using AI-Generated Code

1. Sequential Thinking

When generating code or seeking solutions from AI, it is recommended to think sequentially, defining and executing one step at a time to ensure correctness before proceeding. Sequential thinking helps in breaking down complex problems into manageable parts, leading to logical and structured solutions.


2. Test-Driven Development (TDD)

Utilizing TDD can be beneficial when working with AI-generated code. Test-Driven Development involves creating conditions for success and failure, documenting constraints, and then writing code to match those constraints. This approach is crucial for ensuring code quality, security, and avoiding copyright infringement.


3. Housekeeping and Documentation

It is advised to mark files with partially AI-generated content with comments to track where AI assistance was used. Housekeeping and documentation practices help in understanding and managing AI-generated code within a project, maintaining clarity and transparency during the development process.


4. Continuous Learning and Adaptation

AI-assisted coding tools continuously learn and adapt based on new coding patterns and user interactions. This feature ensures that the tools stay updated with the latest coding trends and developer preferences, offering ever-improving support to developers in their coding tasks.


5. Integration with IDEs

AI-assisted coding tools should seamlessly integrate with Integrated Development Environments (IDEs) to provide real-time assistance to developers as they write code. Integration with IDEs enhances productivity and coding experience by offering features and capabilities directly within the preferred coding environment, streamlining the development process.


Try Our MacOS AI Copilot for Free Today

Omnipilot is a revolutionary AI tool designed to enhance the MacOS experience by providing an AI copilot that can assist users in various tasks, such as writing emails, coding in the terminal, or managing team updates. This AI copilot is designed to provide full context of what is on the screen, making it easier to write code with precision and efficiency. 


By incorporating artificial intelligence into the MacOS environment, Omnipilot aims to streamline workflows and improve productivity. Users can download the MacOS app for free from omnipilot.ai, and experience the benefits of having an AI copilot to assist them in their daily tasks. 


Let Omnipilot make your life easier by offering a seamless and efficient AI-powered experience on MacOS.