Top AI Tools for Developers: Expert Insights for All Types of Workflows

We are now living in a world where short time to market does not necessarily mean poor quality. Customers often expect complex projects to be completed in record time, and for a reason. Technologies, especially AI-driven technologies, allow IT teams to automate much of their routine work. For rapid releases, most projects now only require a budget and talented people to manage the tools and perform creative tasks.

As a software development team, we at Flyaps not only use proven AI tools to improve speed and quality but also create such tools for industries like HR or logistics ourselves. In this article, we will not only explain the benefits and potential pitfalls of AI-driven tools for developers but also provide some practical examples. What’s more, we will share some of our own insights into how AI solutions have helped our team deliver the best results for our customers.

For your convenience, we’ve split all the tools we’ll talk about into six groups. Let’s start with the most common one – tools for code completion.

Code completion and prediction AI tools for developers

These AI tools assist you while you are writing code in your Integrated Development Environment (IDE). They analyze the type of project you're working on, the specific programming language, and even your coding style. Based on this context, these tools suggest lines of code you might want to type. For instance, if you're writing a function, they might suggest the next logical step or the completion of a code block. Let’s look at some examples.

Amazon CodeWhisperer

Amazon CodeWhisperer is a spot-on example that works just like we’ve described prior. But the context it analyzes also includes any comments you add in natural language. If the code recommended by the tool isn’t good enough, you can edit it by hand.

Additionally, CodeWhisperer follows secure coding practices. When you use CodeWhisperer to scan your code, the process involves securely uploading the code to Amazon's servers. The uploaded files are encrypted and stored with strict access controls, protecting your code from unauthorized access.

Programming languages the tool supports: Java, JavaScript, Python.

IDEs the tool is accessible through: JetBrains IDEs (IntelliJ, PyCharm, and WebStorm), Visual Studio Code, and Amazon’s AWS Cloud.

Pros of Amazon CodeWhisperer

Cons of Amazon CodeWhisperer

Support for Popular IDEs.

Comment-based suggestions.

Provides secure coding, and scans for vulnerabilities, such as weak hashing algorithms or improper use of AWS APIs, and highlights them.

Optimized to work smoothly with AWS APIs like Amazon EC2, AWS Lambda, and Amazon S3.

Limited language support.

You still need to review and possibly modify the suggestions to ensure they fit your needs.

Tabnine

Just like Amazon CodeWhisperer Tabnine predicts and suggests the next lines of code in real-time based on millions of examples from open-source code. The tool can also learn from your own coding patterns and styles.

Additionally, you can write comments or describe your tasks in plain English, and Tabnine translates this into working code. It is another crucial, but quite common feature for such tools.

Tabnine itself is pretty safe. The solution doesn’t store or share your code and you can choose to run it locally on your machine, on a server within your firewall, or in the cloud. The choice is based on your security and compliance needs.

Programming languages the tool supports: JavaScript, TypeScript, Python, Java, C, C++, C#, Go, PHP, Ruby, Kotlin, Dart, Rust, React, Vue, HTML, CSS, Lua, Cuda, Perl, SQL, Scala, Shell (bash), Swift, R, Julia, VB, Groovy, Matlab, Terraform, ABAP.

IDEs the tool is accessible through: VS Code, JetBrains IDEs (IntelliJ, PyCharm, WebStorm), Eclipse, and Visual Studio 2022.

Pros of Tabnine

Cons of Tabnine

Support for Popular IDEs.

Comment-based suggestions.

Supports over 30 programming languages.

Doesn’t store or share your data.

The free plan is limited and advanced features are only available through the Pro plan.

Running Tabnine locally can consume a lot of memory or CPU resources, particularly for large projects or files.

May not work well with some plugins or extensions that also provide code completion or analysis features in your IDE.

JetBrains AI assistant

JetBrains has long been renowned for its powerful IDEs and productivity tools. Their AI Assistant offers not only code completion and prediction but also a commit message generation feature. The tool automatically generates commit messages describing the changes made in the code for devs to have better control over different code versions. Changes made by the AI are presented in a different view within the editor, making it easy to review and accept modifications.

Another thing that makes the AI assistant stand out is that the tool can convert code files between different programming languages, facilitating the migration and integration of projects.

Last but not least, you can create and manage your own library of custom prompts. This lets developers easily automate repetitive tasks and quickly find frequently used patterns.

Programming languages the tool supports: Java, Kotlin, PHP, Python, JavaScript, TypeScript, CSS, PHP, Ruby, Go, C#, and C++.

IDEs the tool is accessible through: IntelliJ IDEA, PyCharm, WebStorm, CLion, Rider, GoLand, PhpStorm, RubyMine, DataGrip, Fleet, ReSharper.

Pros of JetBrains

Cons of JetBrains

Smoothly integrated into JetBrains IDEs.

Supports 12+ programming languages.

Create files from code snippets.

Converts code files between different programming languages.

Integrates securely within the IDE without external data sharing.

Users can create and maintain a library of prompts.

Requires an active JetBrains IDE subscription.

Limited offline functionality, some features may require online connectivity.

Now, let’s move to code generation tools, a pretty similar but still more advanced group of AI tools for developers.

Code generation tools

Code generation tools go beyond mere code completion, enabling programmers to create new code based on their descriptions or requirements. Here are the best examples in our opinion.

GitHub Copilot

Since we have a whole article discussing whether GitHub Copilot is worth it or not, we'll just give a brief comparison to other similar tools here.

GitHub Copilot has all the standard features of tools we already covered. Now to the more interesting feature. When you write a comment describing what you want the code to do, Copilot can generate entire blocks of code that fulfill the described functionality. For example, addressing a comment like “Create a function that calculates the factorial of a number can prompt” Copilot will generate the complete function in the relevant programming language.

“Depending on where you use GitHub Copilot, you may have access to either GPT-4 or GPT-4 Turbo. This could be due to many reasons, including infrastructure capabilities or regional demand. Users in regions with GPT-4 Turbo will experience faster code completion thanks to the model's enhanced capabilities. GPT-4 Turbo is also more cost-effective as the token price is lower than GPT-4 offers.”

- Oleh, developer at Flyaps.

Programming languages the tool supports: Java, JavaScript, TypeScript, Python, Ruby, Rust, Scala, C, C++, C#, Go.

IDEs the tool is accessible through: Visual Studio Code, Visual Studio, Neovim, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.)

Pros of GitHub Copilot

Cons of GitHub Copilot

Excellent at generating repetitive, boilerplate code, reducing manual effort.

Integrates smoothly with popular IDEs.

Acts as an on-the-go mentor, providing syntax hints and best practices for new programming languages.

Different versions of the GPT models are location-restricted.

Since it learns from public code, there's a risk of including sensitive or copyrighted code in its suggestions.

Access to Copilot requires a subscription, which might be a barrier for individual developers or small startups.

Over-reliance on Copilot might diminish problem-solving skills and critical thinking among developers.

ChatGPT

ChatGPT, developed by OpenAI, is not particularly tailored to developers. However, it is still really popular amongst those who work with code. Unlike GitHub Copilot, which integrates directly into coding environments, ChatGPT works as a conversational agent. It goes beyond mere code completion by engaging in natural language dialogues, helping developers brainstorm ideas, explain complex concepts, and even guide software design and architecture decisions.

“One of my favorite features of ChatGPT is its memory of previous conversations. This means I can ask it to tweak something based on earlier interactions without having to repeat all the context, which saves a lot of time.

However, it does tend to struggle with longer more complex prompts - it can be a bit “lazy” in those cases.”

- Oleksii Hlavatskyi, Python engineer at Flyaps.

While ChatGPT is not embedded into IDEs like GitHub Copilot, it is accessible through various platforms and can be integrated into different workflows through its API, third-party platforms like Microsoft Teams and Slack, or directly through OpenAI’s chat interface.

However, in spite of all the good points, performance problems are a common occurrence with this tool, and even if you buy the paid version, you will not be immune to them.

“Recently, I upgraded to the paid version running on GPT-4.0. It’s definitely faster and more accurate, which is great. However, I hit a snag when I tried to do some research with a lot of queries at once. The system couldn't keep up and stopped responding, which was a bit frustrating.”

- Oleh, developer at Flyaps.

Programming languages the tool supports: Python, JavaScript, Java, C++, HTML/CSS, SQL.

Pros of ChatGPT

Cons of ChatGPT

Helps with tasks beyond coding, such as brainstorming, explaining concepts, and offering design guidance.

Can be integrated into various platforms and custom applications through its API.

Good for generating documentation.

Has a large community and plenty of resources to help leverage it.

Often provides more generalized responses rather than specific coding solutions.

The quality of suggestions heavily depends on the clarity and specificity of the queries.

Unlike GitHub Copilot, it doesn’t provide real-time, in-IDE code suggestions.

The time taken to generate responses can be slower compared to real-time in-IDE suggestions.

May not have access to the latest coding trends or specific niche programming knowledge.

Developers need to be cautious about sharing sensitive or proprietary code, as interactions are processed and stored by OpenAI.

OpenAI’s CodeX

CodeX and ChatGPT share the same DNA as they come from one family. But they’ve been trained for different tasks.

Just like ChatGPT, CodeX takes user input in natural language and transforms it into code. However, ChatGPT has broader functionality and code generation isn’t its main focus. Therefore, the quality of code generated by CodeX is much higher as it is specifically designed for such tasks and was trained with countless examples of programming languages. Whether it's Python, JavaScript, or even something more niche like Rust, CodeX can generate code snippets, solve programming problems, and even understand complex coding structures. Moreover, users can refine their input, and CodeX will adapt, generating revised or additional code as needed.

When team members “speak” different coding languages, CodeX acts as a translator. It helps everyone understand each team member’s work and can collaborate more effectively. For instance, a Python developer can write some code, and CodeX will translate it into JavaScript, making it easy for a JavaScript developer to pick up and continue working on the same project.

CodeX is a great tool for game development. Open AI even created a space game to demonstrate how good this tool is at building, editing, and animating digital objects. Moreover, it can do this by using voice commands.

Programming languages the tool supports: the list includes but is not limited to Python, JavaScript, Java, C++, Ruby, PHP, SQL, Bash/Shell.

IDEs the tool is accessible through: Visual Studio Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, and others).

Pros of CodeX

Cons of CodeX

Great at interpreting natural language instructions.

Supports a broad range of programming languages.

Can take a block of code written in one programming language and translate it into another.

Allows users to generate and modify code using simple spoken instructions.

Users can see immediate visual feedback and apply changes to 3D objects and environments using voice commands.

Voice commands can sometimes be misinterpreted, leading to incorrect code generation.

Currently more applicable in specialized fields like game development and AR/VR, less so in standard software development.

May not support all programming languages equally well, leading to potential gaps in translation capabilities.

DeepMind’s AlphaCode

AlphaCode is built on the same transformer architecture as OpenAI’s code-generation tools we’ve already discussed. However, it has its unique strengths and focuses. What sets it apart is that AlphaCode is designed to handle complex programming problems often found in competitive coding environments. By competitive coding environments, we mean platforms where programmers solve challenging problems under time constraints. In Codeforces, for example, participants are given a problem to solve (for example, finding the shortest path in a graph), for which they must write a working program within a limited time to get a high score.

As both ChatGPT and CodeX, AlphaCode understands the problem statement written in natural language.

Programming languages the tool supports: Python and C++ are its primary focus given their prevalence in coding competitions.

IDEs the tool is accessible through: Currently, AlphaCode is an internal project at DeepMind and is not available as a public tool or plugin for IDEs.

Pros of AlphaCode

Cons of AlphaCode

Understands and solves complex problems.

Hepls with writing the foundation for unit tests, as it can test and refine solutions against provided test cases.

Can generate diverse solutions for a single problem.

Perfect for competitive coding environments.

Might be overkill for simpler or more routine coding tasks.

As of now, AlphaCode is primarily a research tool and not widely available for general programming use.

Requires substantial computational power and time.

Tools for rapid prototyping

These tools help quickly create and test early versions of software or applications. They allow you to build a basic, functional model for your project in a short time, so you can experiment, get feedback, and refine your ideas before fully developing the final product.

Gradio

Gradio is an open-source Python library that is particularly valuable for creating demos and visualizing model outputs.

One of its popular features is the ability to rapidly prototype interactive user interfaces for machine learning models. Whether you're dealing with text, images, or audio, Gradio supports a broad spectrum of input and output types, allowing you to create engaging and functional demos with ease.

You don’t need to have prior experience with web development to use Gradio. You can stay focused on your Python code while Gradio takes care of all the complexities related to building web interfaces.

Sharing your work is also straightforward with Gradio. Once you've built your demo, you can generate a public link that lets others interact with your model remotely. This feature is perfect for getting quick feedback.

Last but not least, Gradio integrates seamlessly with Hugging Face Spaces. This means you can host your demos online permanently, without worrying about setting up or maintaining web hosting services.

Programming languages the tool supports: Python.

IDEs and platforms the tool can be integrated with: Jupyter Notebooks, Google Colab, VS Code, PyCharm.

Pros of Gradio

Cons of Gradio

Minimal setup and no web development skills are required.

Lets you quickly create and iterate on interactive demos.

Allows to share demos easily with public links or host on Hugging Face.

Handles a wide variety of data types.

May not handle large models or datasets as efficiently due to browser-based execution.

Doesn’t support any other languages except Python.

Best suited for demos and prototypes rather than full-scale web applications.

May require additional setup for high-traffic applications or deployment at scale.

Streamlit

Imagine being able to showcase your machine learning models or data visualizations through a sleek web interface without needing to learn HTML, CSS, or JavaScript. Streamlit allows you to do exactly that. When you create a Streamlit app, you write it in Python. Streamlit then automatically converts your Python code into a web app, handling all the necessary HTML, CSS, and JavaScript code behind the scenes. As a result, you can focus on the functionality and data of your app, while Streamlit takes care of the interface and user interaction.

Streamlit supports a variety of widgets that allow users to interact with your app. These include text inputs, sliders, buttons, and file uploaders. It also supports rendering complex data types like images, audio, and even interactive plots from libraries like Matplotlib and Plotly.

Unlike other similar tools, Streamlit has a reactive design. Each time an input widget's state changes, Streamlit re-runs the entire script from top to bottom to update the app’s output, ensuring that the displayed information is always current. Streamlit’s session state feature ensures that each user’s session is isolated, maintaining separate states for each user and making the application thread-safe.

“For data engineers, Streamlit is a lifesaver. It makes it super easy to create basic graphics and visualize how a model is performing. This is crucial because having a clear visual understanding of the model's behavior helps in making better decisions.

One of the great things about Streamlit is how it speeds up the development of interfaces for data and AI applications. You can throw together a prototype, deploy it, and test your ideas quickly. It’s ideal for prototyping because you can see your changes in real-time and get instant feedback.”

- Alexander Arhipenko, CTO at Flyaps.

Programming languages the tool supports: Python.

IDEs and platforms the tool can be integrated with: Can be used with any Python-compatible IDE, including Jupyter Notebook and JupyterLab, PyCharm, VSCode, Spyder.

Pros of Streamlit

Cons of Streamlit

No web development skills are required. Just Python.

Allows to quickly create interactive UIs with little amount of code.

Includes a variety of input and output widgets.

Reflects changes in real-time without the need for manual refreshes.

Frequent state changes can lead to performance issues because the app needs to reprocess everything repeatedly.

Provides basic layout tools such as sidebars, columns, and expanders, but it doesn’t support more sophisticated layout techniques like nesting containers within containers.

Best suited for simple key-value pairs.

When there is a need for comparing or visualizing multiple complex objects, Streamlit’s current design necessitates defining separate keys or states for each item.

Does not include built-in support for user authentication, which is a significant limitation for applications that require secure access or user-specific data handling.

Model development and training tools

When developing AI, such tools allow developers to design AI models using frameworks and libraries, making the process more efficient. We can include a lot of tools in this group, but in our opinion, it’s better to focus on the most prominent and proven tool we have tried ourselves – TensorFlow.

TensorFlow

TensorFlow is a powerful open-source ML library developed by the Google Brain team. It’s designed to support the creation, training, and deployment of ML models.

At the core of TensorFlow is something called a “dataflow graph.” Picture a flowchart where each box represents a math operation, like adding or multiplying numbers. The lines connecting these boxes carry the data from one operation to the next. In TensorFlow, this data is often complex and multi-layered, much like a spreadsheet with many dimensions. This setup allows TensorFlow to handle large and complex calculations very efficiently.

TensorFlow proved itself in this graph-based approach because it organizes and manages the flow of data through various operations smoothly. This is especially useful for big tasks, where breaking down a massive problem into smaller, connected steps makes it easier and faster to solve.

But TensorFlow doesn’t just stop at doing math. It also includes a popular toolkit called TensorBoard, which provides visual insights into the training process of the TensorFlow model.

“We've integrated TensorBoard into one of the projects we’ve helped to build, UrbanSim, and it's been a game changer for visualizing the model training process. During what's called the calibration phase, where the model automatically fine-tunes its parameters, TensorBoard lets us see exactly what's going on. We can see in real time how the model is learning and decide whether we need to keep going or stop early if things aren't progressing as expected.”

- Alexander Arhipenko, CTO at Flyaps

Programming languages the tool supports: Python (the primary language for TensorFlow), C++, JavaScript, Java, Swift and Go.

IDEs and platforms the tool can be integrated with: PyCharm, Jupyter Notebooks, and VS Code, Google Cloud, AWS, and Azure.

Pros of TensorFlow

Cons of TensorFlow

Offers excellent tools like TensorBoard for visualizing model performance and data flow graphs.

Backed by Google, which ensures regular updates and a strong ecosystem of tools and extensions.

Can train models efficiently on various hardware configurations, including CPUs, GPUs, and TPUs, and it supports distributed training across multiple devices.

Once trained, models can be deployed across different platforms, from servers to mobile devices

Some features and optimizations are better supported on Linux and macOS than on Windows.

Requires significant computational power, particularly for training deep learning models.

Can be complex for beginners to grasp, especially without prior experience in machine learning.

Tools for enhancing developer’s productivity and collaboration

The following AI-driven tools speed up the coding process, help spot and fix errors quickly, and ensure everyone on the team is on the same page. We will focus here on our favorite ones – Stepsize AI and Pieces.

Stepsize AI

Stepsize AI is designed to integrate directly into your code editor, providing developers with a real-time interface to flag, track, and manage technical debt. It allows you to mark sections of code that might need fixing or improvement as you work. These annotations are not just for the present – they're tracked over time, letting you see how issues evolve and ensuring they get the attention they need. Stepsize integrates seamlessly with popular version control systems like Git, so you can keep a detailed history of changes and understand how technical debt impacts your project's progress.

Platforms the tool can be integrated with: the list includes but is not limited to VS Code, Jira and Linear.

Pros of Stepsize AI

Cons of Stepsize AI

Lets devs proactively manage technical debt, avoiding unexpected slowdowns.

Developers can focus more on new features rather than fixing old problems, thanks to efficient tracking.

Keeps everyone informed and aligned on technical debt, fostering better team communication.

The tool's effectiveness relies on consistent use and buy-in from the development team.

Integrating with existing systems can sometimes be more complex than anticipated, especially in large organizations.

Pieces

Pieces is an AI-driven assistant tailored to developers, which operates across their workflow. The tool saves and organizes code snippets directly from your desktop application or through extensions for your browser and IDEs. Additionally, Pieces automatically enriches these snippets with metadata and context, making them easily searchable and reusable.

When it comes to workflow activity, the tool tracks your actions across different solutions and maintains a continuous log of activities, helping you pick up where you left off.

Basically, Pieces acts as a bridge connecting research, coding, and collaboration by embedding itself across your primary tools, such as browsers, IDEs, and communication platforms.

Platforms the tool can be integrated with: the list includes but is not limited to Visual Studio Code, Obsidian, and Notion.

Pros of Pieces

Cons of Pieces

Saves time by managing code snippets.

Processes data on-device to increase privacy and security.

Supports multiple LLMs both locally and in the cloud.

Operates at the OS level to provide suggestions and insights fully understanding a developer's workflow.

Delay in capturing context from files and folders, slowing down the workflow.

The Live Context feature may not always provide the desired results and can take time to respond.

Lack of project-specific filtering in VS Code, making it harder to find relevant snippets.

The relevance and quality of AI suggestions can vary depending on the context and chosen LLM.

Specialized tools for specific languages and frameworks

In this final block of AI tools that we recommend, we will focus on two powerful solutions designed for Python developers – PyCharm and Wing Python IDE Pro. Both of these specialized IDEs elevate your coding experience, offering unique capabilities that general-purpose editors often can’t match.

PyCharm

Whether you're working on web development, data science, or general Python projects, this tool, developed by JetBrains IDE, provides you with intelligent suggestions, seamless debugging, and extensive integrations.

PyCharm supports Python 2 and 3 and is available across major operating systems: Windows, macOS, and Linux. It's known for its smart code editor, which provides syntax highlighting, code completion, and real-time error detection. The solution also includes a graphical debugger, integrated testing tools, and version control system (VCS) support, which simplifies the development process.

PyCharm offers three versions: two free versions for basic Python development and beginners learning Python, and a paid one with advanced features for web development, scientific computing, and more.

Platforms the tool can be integrated with: Django, Google App Engine, IPython, WakaTime, Anaconda, Kite, Matplotlib, SciPy, Django, Flask, SSH, Docker, Git, Mercurial, and Perforce.

Pros of PyCharm

Cons of PyCharm

Offers a wide selection of tools for code editing, debugging, and testing.

Backed by a large community and robust support system.

Many plugins are available to extend functionality.

Manages code with Git, Mercurial, and other systems seamlessly.

Works on Windows, macOS, and Linux.

Full support for Django and other frameworks.

Highly customizable to fit personal and project needs.

Supports HTML, CSS, JavaScript alongside Python.

Consumes a lot of memory and CPU, especially with large projects.

Takes time to launch, which can be frustrating for quick edits.

Merging and version control features can be complex.

Some tools, like the package checker, can be unreliable.

Setting up proxies is not straightforward.

Wing Python IDE

Why do you need another IDE for Python? Though both these tools are tailored for Python development, they cater to different needs and preferences, and Wing stands out in several key areas.

Wing's latest feature, for example, is the integration of generative AI that assists in coding tasks such as implementing missing code, refactoring, and creating new functions or modules based on textual descriptions. Meanwhile, though PyCharm has also incorporated AI-driven code suggestions and completion with plugins or integrations, it does not offer the extensive, conversational AI capabilities found in Wing for now.

Another of Wing's famous features is its debugger. If you’ve spent countless hours troubleshooting code, you know how crucial a reliable debugger is. Wing’s debugger goes beyond the basics with capabilities like live code inspection and modification, conditional breakpoints, and an interactive probe that lets you test changes on the fly without stopping the running program.

Pros of Wing Python IDE

Cons of Wing Python IDE

The IDE is intuitive, making it accessible for both beginners and experienced developers.

Wing’s support team is proactive, often implementing user feedback quickly.

Excellent support for test-driven development across multiple frameworks

From intelligent editing to powerful debugging and remote development, Wing covers all essential aspects of Python development.

Smaller community and less extensive documentation compared to larger IDEs like PyCharm.

Advanced project management features may require some initial setup and familiarization.

Final thoughts

It's just a small part of the great AI tools developers commonly use. Jupyter Notebook, Hugging Face and many others will also be a great addition to your workflow, depending on the project. In some cases, you will find TensorBoard to be the perfect assistant for visualizing ML experiments. In others, Neptune will do the job just as efficiently. In any case, many tools have free trials so you can try them out and build your own personal AI toolbox.

Looking for a team with experience in both using and building AI tools? Drop us a line - we're here to help!