What if you could harness AI to help you develop AI? For many developers, this is not just a hypothetical scenario. With the right AI development tools, including Multi-Agent AI, they can leverage AI to automate and streamline various project processes, improving their speed, efficiency, and performance. This blog will unpack the significance of AI development tools and provide the insights you need to achieve your development objectives, like building and deploying high-quality software faster, with fewer errors and less manual effort.
One valuable resource that can help you achieve your development goals is Lamatic’s generative AI tech stack. This AI development tool can streamline your workflows and boost productivity so you can focus on what matters most.
How AI is Used in Software Development
AI assistants, AI art, AI stories—it's not coming; it's already here. These hot new tools, however, aren't just for writers and artists to enjoy. Software developers can reap the benefits of automated assistants right now. From generating and reviewing code to writing documentation and bug tracking, along with lots of stuff in between, there are AI tools for developers to use that match their budgets.
Automated Debugging: Speeding Up the Fixing Process
AI software is helpful in automated debugging. These tools can analyze code, find patterns, and detect errors. They can also identify and categorize error types. With machine learning, AI can learn how to fix bugs.
After enough training, an AI can automatically fix many types of errors. This is similar to how the auto-correct feature on smartphones works. AI debugging tools can even work alongside human coders to speed up debugging.
Smart Assistants: Your New Development Buddy
AI software assistants are gaining popularity daily because they intelligently speed up the development process. They even help novices learn about the development environment more quickly than the trial-and-error method.
AI software assistant tools can act as trainers, offering recommendations and code examples and preventing simple coding mistakes like closing parenthesis. A great example of this is Kite, an assistant developed for Python.
Automate Code Generation: Let AI Write Your Code
Coding from scratch is time-consuming and labor-intensive. To combat the issues of money and time constraints, researchers have built programs that can write code beforehand. These solutions are not very good with ambiguity. In this case, AI software programs came into the picture. They significantly reduced the workload.
Once they master the underlying patterns, more than code completion, AI-based software could generate a program by assembling predefined modules like LEGO pieces. In the future, AI software has the potential to replace the task of junior software programmers. This could be the baby step toward a self-programming machine.
Intelligent Testing: Smarter Tests for Better Software
Testing has been one of the most crucial elements throughout the development cycle in building a quality software product. One of the significant challenges that software programmers face while testing software products is creating a thorough list of use cases and situations that could impact the program’s performance.
To ensure software stability after new changes, regression testing plays a crucial role in maintaining quality across versions. AI software tools can perform this by going through the previous logs and automatically generating a list of test cases to run through the system. They can also predict testing outcomes without even performing the actual tests. It only focuses on the solution, saving a considerable chunk of time.
Designing Strategy: Planning for Development Success
Usually, software developers spend a significant amount of time deciding the features and functionality to be added to a software product. AI software development tools trained on past software development projects and business factors can analyze existing software and find a solution, fulfilling the requirements of both clients and software engineers.
Based on user reviews of similar solutions and client marking, they can produce a hierarchy of the best features for a solution’s success.
The Pros Of AI In Software Development
How does AI do all of this? The coding tools we'll examine work similarly to text-based generation models. They use generative AI models and large language models (LLMs) that have been trained on code datasets. The models suggest how to complete lines when developers write code. Given the proper prompts, they can also create code from scratch, which is what the creator of Plan My Lunch did.
Besides code generation, AI tools have other benefits for developers, such as:
Increased Collaboration
Developers can automate parts of their workflow using AI to free up their time for projects and tasks that require working with other engineers and team members, such as:
- Security reviews
- Planning
- Pair programming
Automated Debugging And Testing
AI tools can find bugs and other coding errors faster than humans. They can also test code and give reports to reduce the time spent on code reviews and bug checking to bring a product online sooner.
Smart Assistants
Developers can benefit from AI tools that suggest ways to complete code or solve problems, whether developing code or handling other tasks. Likewise, they can help developers avoid repeating themselves in their code and offer real-time feedback.
Strategy Design
When AI tools have access to a team's past projects, they can suggest what features will benefit a product best based on developer experience and user reviews.
Team Management
AI tools can help developers coordinate their work to avoid conflicts in code and increase communication to resolve issues more efficiently.
Related Reading
- What is Agentic AI
- How to Integrate AI Into an App
- Generative AI Tech Stack
- Application Integration Framework
- Mobile App Development Frameworks
- How to Build an AI app
- How to Build an AI Agent
- Crewai vs Autogen
- Types of AI Agents
24 Best AI Development Tools for Streamlining DevOps & Deployment
1. Lamatic: The Generative AI Tech Stack That Automates Integration
Lamatic offers a managed generative AI tech stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low-Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
2. GitHub Copilot: Your AI Pair Programmer
The developer platform GitHub created Copilot to assist developers in generating code. Recently, it was integrated with OpenAI's GPT-4 to enhance its generative abilities. This tool has received generally positive reviews from users. Developers appreciate its ability to suggest code snippets, complete functions, and provide helpful comments to significantly speed up the coding process, especially when completing repetitive tasks and boilerplate code.
Some reviewers note that Copilot's suggestions are not always accurate or context-appropriate, requiring careful review and occasional adjustments. Concerns about the tool’s reliance on existing codebases and potential copyright issues exist. Despite these limitations, many users report increased productivity and improved code quality when using GitHub Copilot, making it a valuable asset for experienced developers and those learning to code.
3. ChatGPT: The AI Assistant for Everyone
It's for writers, though, isn't it? No! ChatGPT has proven to be a valuable tool for software developers, too. Its capabilities include optimizing code, fixing bugs, generating code snippets, creating custom tests, and assisting with documentation. Developers have found ChatGPT's natural language processing, conversational interface, and AI-driven recommendations particularly helpful in accelerating the development process through code snippets and templates.
Developers note some risks and limitations, such as junior developers' overreliance on ChatGPT's responses without proper validation, which can lead to bugs. They also feel the text formatting capabilities were lacking when using ChatGPT for communication-focused tasks. Some developers found the tool’s information outdated or inaccurate, depending on the version used.
4. Stepsize AI: The Technical Debt Tracker for Developers
While not a code-generating instrument, Stepsize takes over some of the routine “paperwork” in development, leaving more time to code and tackle engineering challenges. It works with Jira and Linear, but it's expected to be integrated with other issue trackers soon. The tool has received positive reviews from developers for its ability to enhance the software development process by tracking, prioritizing, and resolving technical debt directly within their IDE.
Many users find it particularly useful for reducing technical debt, making problem-solving easier, and improving security. Some reviewers note that Stepsize AI requires a learning curve and buy-in from the engineering team to be fully effective. The tool's integration with existing systems may also be more complex than anticipated for some teams.
5. Tabnine: The AI Assistant That Knows Your Coding Style
Tabnine can generate code based on your coding style, helping you review it and recognize any changes you need to make. The tool has received positive reviews from developers for its ability to enhance the software development process. Many users find Tabnine useful for accelerating coding speed, ensuring code consistency, and facilitating code reviews. Tabnine’s context-aware suggestions and ability to adapt to a user's codebase are highlighted as key benefits that improve productivity and code quality.
Some reviewers note that Tabnine requires a learning curve and may be more challenging to integrate with existing systems than anticipated for specific teams. Despite these limitations, most reviews indicate that Tabnine is a valuable asset for individual developers and enterprise teams, helping reduce the burden on senior developers and enabling quicker onboarding of new team members.
6. Mintlify: The Documentation Assistant for Developers
Mintlify has received positive reviews from developers for its ability to streamline the code documentation process. Many users praise Mintlify's auto-documentation feature, which automatically generates clear and concise documentation from code, significantly reducing the time and effort required for this routine task. Reviewers also highlight Mintlify's ability to provide insights into complex code structures, making codebases more accessible, especially for onboarding new developers.
Some reviewers note that Mintlify's starting price may be high for specific teams. They also mention that the quality of the documentation that Mintifly produces depends on the quality of the code and that any texts require manual editing. Despite these drawbacks, most reviews indicate that Mintlify is an invaluable tool for developers, helping to facilitate collaboration and free up engineers for more interesting tasks.
7. Bugasura: The Bug Tracking Tool That Helps You Get Organized
Bugasura consolidates your bugs into one place while linking similar bugs to ensure you don't miss anything that could slow your project down or waste time on duplicate reports. Many users praise Bugasura's intuitive interface, collaborative features, and intelligent capabilities that automate tasks like the following:
- Capturing device and browser information
- Taking screenshots
- Suggesting issue descriptions
Balancing AI Efficiency and Usability: Overcoming Bugasura's Learning Curve
Reviewers highlight how Bugasura's AI-powered features, such as impact detection and similar issue recommendations, can save developers significant time and effort in prioritizing and resolving bugs. Some reviewers note that Bugasura may require a learning curve, especially for teams transitioning from other issue-tracking tools.
Users also pointed out that the tool can be slow and that filtering bugs through additional customization options is difficult. Despite these minor limitations, most reviews indicate that Bugasura is an invaluable tool that improves productivity, collaboration, and overall software quality for development teams of all sizes.
8. WhatTheDiff: The AI Code Review Assistant
WhatTheDiff, an AI-powered code review assistant, has received very positive reviews from developers for its ability to streamline the pull request process. Many users praise the tool's automated generation of detailed descriptions for code changes, which saves time and improves clarity during the review process.
Maximizing WhatTheDiff’s Impact: Weekly Reports, API Dependence, and Language Support
Reviewers also highlight WhatTheDiff's ability to provide weekly reports summarising the most significant pull requests and activities, helping all team members and stakeholders stay informed and track progress. Developers mention some drawbacks, including:
- A lack of support for less common coding languages
- The sizeable token consumption of requests
- Dependence on the GitHub/GitLab API
Considering these drawbacks, most reviews indicate that WhatTheDiff is an invaluable AI assistant significantly improves software development teams’ efficiency, collaboration, and code quality.
9. Cody: The AI Assistant for Code Understanding
Cody uses optimized search and codebase context to help you understand, write, and fix code. Many users praise Cody's ability to integrate seamlessly as a GitHub Action or GitLab pipeline, serving as a helpful interface for automating code reviews and summarization.
Enhancing Development Efficiency with Cody: Customization, Context Awareness, and AI-Driven Code Reviews
Reviewers highlight how Cody's AI-driven features, such as providing detailed descriptions of code changes and generating weekly progress reports, can significantly improve efficiency, collaboration, and code quality during software development. Some reviewers find that Cody may require a learning curve, especially for teams transitioning from other tools, especially in customizing the instrument to your needs and preferences.
Regarding customization, some users requested more features and improvements to the tool's context awareness to ensure more accurate suggestions. Despite these minor limitations, developers indicate that Cody is an invaluable AI assistant that streamlines various aspects of software development, from code reviews to progress tracking, making it a valuable asset for development teams of all sizes.
10. Codium: The Low-Code Development Tool for Rapid Prototyping
Codium is a low-code platform that allows developers to design applications visually without extensive coding. Many users praise Codium's drag-and-drop interface and point-and-click tools, which enable rapid prototyping and application building.
Maximizing Team Efficiency with Codium: Collaboration, Customization, and Low-Code Development
Reviewers highlight how Codium's collaborative features, such as project planning, team communication, and progress monitoring, can streamline the software development process and improve coordination among team members. Regarding drawbacks, some reviewers note that Codium may require a learning curve, especially for developers accustomed to traditional coding methods.
A few users also requested more customization options and the ability to export generated code for further refinement. Despite these minor limitations, most reviews indicate that Codium is an invaluable low-code tool that significantly boosts productivity, reduces development costs, and enables teams to quickly adapt to changing requirements and technologies.
11. Core ML: Apple’s Machine Learning Framework
Core ML is Apple’s framework for integrating machine learning models into apps running on their operating systems. The key advantage of Core ML is how easy it is to use – it has a simple drag-and-drop interface for ease of use. You can use it to create custom AI models trained on your data and import models from third-party libraries. And that’s why it’s a good choice.
Important features
- Core ML Tools: If you’re importing a third-party model, you can easily convert it into the Core ML format with Core ML Tools.
- Model customization: With Core ML, you can expand and modify your AI model with new layers to improve accuracy and performance.
- Model personalization: In Core ML, you can update your model to adapt to new data and give your users a better experience.
12. ML Kit: Google’s SDK for Mobile Machine Learning
ML Kit is Google’s software development kit (SDK) that allows you to integrate machine learning into your mobile app. With ML Kit, you can add production-ready AI models optimized for mobile with only a few lines of code. And the best part? It works on both Android and iOS devices. And that’s why you should go with it.
Important features
- Pre-trained models: ML Kit offers a range of Google’s pre-trained AI models, focusing on computer vision and natural language processing models.
- Custom model support: If you need a more complex model, you can replace some of their pre-trained models with custom models.
- On-device processing: ML Kit models process data on-device, meaning they can work in real time without an internet connection.
13. PyTorch: The Flexible Machine Learning Framework
PyTorch is a machine learning framework that creates deep learning models. It’s widely used for training computer vision and natural language processing AI models. The main advantage of PyTorch is that it’s flexible and easy to use compared to other machine learning frameworks. And that’s why it should be your top choice.
Important features
- Dynamic computation graph: PyTorch’s dynamic computation graph is a standout feature that allows flexible model definition and modification.
- Deep learning support: With PyTorch, you can build neural networks and deep learning models for advanced AI features and applications.
- Extensive ecosystem of tools and libraries: PyTorch supports a large ecosystem of tools and libraries you can use to improve and optimize the AI models you build.
14. Scribe: The Documentation Tool That Uses AI to Save You Time
Scribe is an AI tool that automatically generating step-by-step guides and documentation for any process. In seconds, you can create:
- Step-by-step guides
- SOPs
- Training manuals
Using Scribe is the easiest way to save time and effort when writing documentation without sacrificing quality. And that’s why it’s a good investment.
Important features
- Automatic documentation: Scribe can automatically generate visual, step-by-step guides for any process, which saves you a lot of time.
- Customization options: You can customize documents by adding titles and descriptions and redacting sensitive information, ensuring your guides are comprehensive and tailored to your needs.
- Custom branding: Scribe supports adding company logos and colors to create guides that align with your brand identity.
15. Snyk: The AI Tool for Finding and Fixing Vulnerabilities in Code
Snyk is an AI SAST tool similar to Amazon CodeGuru. Think of it like Grammarly for your code. It can scan your code, find vulnerabilities, and suggest fixes that won’t cause new issues. Snyk works with most significant IDEs and repositories and can analyze code in most common programming languages. And that’s why it’s a good pick.
Important features
- Automated vulnerability detection: Snyk continuously scans your code and automatically detects vulnerabilities and issues so you can fix them as soon as they happen.
- Comprehensive security solutions: Snyk scans your code, dependencies, Docker containers, and infrastructure as code (IaC) so you have full coverage.
- Direct integration: As part of its developer-first approach, Snyk seamlessly integrates into your IDEs, repos, and workflows.
16. TensorFlow: The Popular Open-Source Machine Learning Framework
TensorFlow is an open-source machine learning framework that builds and deploys machine learning models. TensorFlow is especially useful if you want to build and train deep learning models. Also, TensorFlow has specialized tools like TensorFlow Lite (for mobile devices) and TensorFlow.js (written in JavaScript) to make deployment and integration easier. That’s why it’s one of the most popular machine learning frameworks.
Important features
- Flexibility: TensorFlow allows for different types of data definition and manipulation, which gives you flexibility and more control over your AI model.
- TensorFlow Lite: TensorFlow Lite is a mobile library for deploying AI models in mobile apps and edge devices.
- TensorFlow.js: With TensorFlow.js, you can develop AI models in JavaScript and use them directly in the browser or in Node.js.
17. Uizard: The AI Tool for Designing App Interfaces
Uizard is an AI-powered UI design tool that quickly creates UI designs and prototypes. With Uizard, you can make:
- Wireframes
- Mockups
- Prototypes
- Landing pages
You can take one of Uizard’s UI templates and adjust it to your needs or create a new design from scratch using their pre-made design elements. And the best part? Your whole team can work together on the design in real time. And that’s what makes Uizard stand out from the crowd.
Important features
- Real-time collaboration: Uizard allows your whole team to collaborate in real time to create the design so everyone is aligned from the start.
- Drag-and-drop interface: Uizard has an easy-to-use, intuitive interface so even non-designers can easily create UI designs and prototypes.
- Autodesigner: The stand-out feature, Autodesigner, can generate editable prototypes, screens, and images from natural language prompts.
18. H20.AI: An Open-Source Deep Learning Platform
H20.AI is an open-source deep learning platform. Business-oriented AI software helps users make decisions based on data and draws insights. There are two open versions of it:
- Standard H20
- The paid version is known as Sparkling Water
H20.AI is used for:
- Predictive modeling
- Risk and fraud analysis
- Insurance analytics
- Advertising technology
- Healthcare
- Customer intelligence
19. Cortana: The Voice Search Assistant for Everyone
Cortana is an AI-enabled voice-search assistant that performs varied tasks such as answering queries, setting reminders, etc. Its voice recognition feature is a time-saver and supports operating systems such as:
- Windows
- iOS
- Xbox
- OS
It supports different features, such as:
- Supporting a wide range of tasks like switching on the light to placing an order.
- Takes voice inputs.
- Uses the Bing Search Engine.
- It supports multiple languages, such as French, German, English, Japanese, and many more.
20. IBM WATSON: The Next-Generation AI Tool for Business
IBM Watson is a question-answering software. It supports SUSE Linux Enterprise Server 11 OS using the Apache Hadoop framework. After training your model with Watson, it profoundly understands the core notions. IBM Watson is a robust system that assists in making business processes more intelligent.
Some of the predominant features of IBM Watson are:
- Supports distributed computing
- It can operate with existing tools
- Provides an API for app development
21. Infosys Nia: The AI Tool for Smarter Business Processes
It accumulates organizational data on the legacy system and piles it into a self-learning knowledge base. This is one that business analysts and software developers in your team can leverage to create high-performing, scalable ML models. And the team could do all of this even if they don’t have data science expertise.
Some of the predominant features of Infosys Nia are:
- Extensibility: for data preparation, visualizations, and machine learning methods.
- Self-Service Provisioning: Elastic Cloud Deployments. GUI-based features enable your AI solution team to build precise ML models.
- Integrated Enterprise Framework: for data preparation, deployment, and modeling.
- Streaming Fast Predictions: Infosys Nia Prediction Server.
22. BigML: The AI Tool for Creating Predictive Models
This is one of the best Artificial Intelligence tools that allows you to create and train a model using data. You can generate or use new predictive insights based on that same model in your business. One of its biggest strengths is how easy it is to train a big dataset quickly.
Even though BigML is a paid service, there’s no limit on data or features, and dataset training time can be completed within minutes. It’s perfect for companies that don’t have expertise in ML (Machine Learning) but need to try out AI solutions, develop prototypes and understand large datasets at scale.
Some of the key features of BigML are:
- Built machine learning algorithms
- Flexible deployment options
- Visualizations and Insights
- Integrations with popular programming languages
- Low-code interface for ease of use
23. Microsoft Azure AI Platform: Microsoft’s Comprehensive AI Platform
Microsoft’s artificial intelligence (AI) platform is the Microsoft Azure AI Platform. It encompasses an array of tools and services, including a set of pre-trained neural networks that can be utilized in a wide range of use cases. These include:
- Recognizing objects and patterns in video streams
- Translating between languages, text analysis, sentiment analysis
- Image tagging
The data layer contains services for both structured and unstructured data. The former uses SQL, while Spark supports batch processing large volumes of unstructured data from document management systems or other sources like IoT devices.
The Key Features Of the Microsoft Azure AI Platform:
- Built machine learning algorithms
- Data labeling services
- Pre-trained models
- Custom model training
- Model management and deployment Integrated development environment (IDE) support
- Real-time predictions
24. Salesforce Einstein: AI for Salesforce Apps
Salesforce’s Einstein is a set of artificial intelligence and machine learning tools designed to offer developers an easy way to add these capabilities to their Salesforce apps. The primary tool, Einstein Vision, analyzes images and recognizes objects within them.
The platform is designed to understand individual objects and the relationships between them. For example, it can tell you it sees a picture of a cat sitting beside food on a table. Then, it can classify images and objects, so you know exactly what you’re looking at from image A (cat) or image B (food on the table). It sounds easy enough, but there’s quite a bit of power behind these tools, and they work incredibly well.
Key Features of Salesforce Einstein:
- Pre-trained models
- Custom model training
- Easy to use interface
- Supports a wide range of programming languages.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed generative AI tech stack.
Our solution provides:
- Managed GenAI middleware
- Custom GenAI API (GraphQL)
- A low code Agent builder
- Automated GenAI workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated vector database (Weaviate)
Lamatic empowers teams to implement GenAI solutions rapidly without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
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