Best AI for coding in 2025: 25 developer tools to use (or avoid)
Last updated: March 3, 2025
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Best AI tools for developers in 2025
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S - Recommended
Aider
Cursor
Windsurf
Github Copilot
A - Developing
Amazon Q Developer
CodeMate
OpenHands prev. OpenDavin
Gemini Code Assist
Qodo Gen prev. Codiumate
Work With Apps by ChatGPT
B - Promising
Cline prev. Claude Dev
Gitlab Duo
Jetbains AI Assistant
CodeRabbit
Qodo Merge prev. PR-Agent
C - Ignore
ChatDev
Devin AI
GPT Pilot
MetaGPT
Gorilla
Supermaven
AI Code Review Action
Korbit
⭐ Upcoming ⭐
Github Copilot Workspace
Github Spark
Artificial intelligence has forever changed the way software is developed. Automated tasks and improved teamwork, fueled by AI, are fundamentally rewriting the software lifecycle.
This article explores best AI for coding. We’ll look at their key features, capabilities, benefits, limitations, and areas needing improvement. Understanding these technologies’ current state helps developers and managers decide whether to integrate them into their workflows.
The article covers
Agentic AI assistants
AI agents,
AI assistants,
AI code review assistants,
AI app builders.
Our aim is to provide developers, managers, and tech leaders with insights to make informed tool integration decisions for greater efficiency, productivity, and innovation.
In this guide, we first discuss the upcoming AI coding tools, then review the rest from best to worst (no matter the tool type):
Upcoming
Recommended
Developing
Promising
Ignore
What is the best AI for coding in 2025?
The best AI developer tools in 2025 are Aider, Cursor, Windsurf, and Github Copilot.
Best AI tools for coding: Comparison
Aider
Cursor
Windsurf
GitHub Copilot
AI Models
Aider works best with Claude 3.7 Sonnet, DeepSeek R1 & Chat V3, OpenAI o1, o3-mini & GPT-4o. Aider can connect to almost any LLM, including local models.
15 various models provided by Anthropic, Cursor, Fireworks, OpenAI, xAI. Learn more here.
Codeium supports several AI models across its different features: Codeium Chat Models, Windsurf Cascade Models, and Codeium Command.
Complex projects, AI collaboration, large codebases
IDE integration, code suggestions, chat assistance
Target Audience
Advanced coders, terminal users
Beginners and advanced, graphical users
Advanced coders, complex projects
All levels, depending on IDE familiarity
Supported Languages
Aider should work well with most popular coding languages. It should also work quite well for other languages, even those without repo map or linter support. Learn more here.
Cursor supports a wide range of programming languages, but it excels particularly with Python, JavaScript/TypeScript, Swift, C, and Rust.
Some developer-friendly AI tools are free to use but come with a catch: you need your own API keys, which means you'll still pay for usage depending on the tokens consumed. Examples include:
Windsurf 🏄♂️ – completely free if you bring your own keys.
Cursor 🖋️ – many features are free, but API keys are required for full use.
Aider 🤖 – similar to Windsurf: free tool, but key-dependent.
However, there’s another route: local models. If you have a strong enough machine or use lightweight models, you can run AI tools entirely locally – free of any external costs.
So, technically, the answer depends on your setup. For token-based tools, you'll only pay for API usage. For local models, the cost is your hardware. 🚀
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Copilot Workspace is a Copilot-native development environment designed to facilitate everyday coding tasks by leveraging natural language processing. It enables developers to articulate their intentions, propose plans of action, and implement changes efficiently across their projects.
Key Features
Task-Oriented Development: Unlike traditional code suggestions, Copilot Workspace helps users plan and execute a coordinated set of changes that can span multiple files within a repository. This allows for comprehensive modifications, including adding or removing files, all while maintaining a clear workflow
Iterative Editing: Every aspect of the workspace is editable, allowing developers to refine their specifications, plans, and implementations as needed. Users can regenerate suggestions based on modified inputs without starting from scratch.
Collaborative Environment: The workspace supports real-time collaboration among team members. Developers can share their workspaces instantly, enabling others to iterate on the same code or project seamlessly. The system automatically tracks changes and versions, simplifying the process of creating pull requests (PRs) with a single click
Integrated Tools: Copilot Workspace integrates with existing development tools and processes, including IDEs like Visual Studio Code. It allows for debugging, testing, and running builds directly within the environment, ensuring that code quality is maintained before merging changes into the main codebase
Workflow Stages
Spec/Brainstorming: Users can start by brainstorming ideas or asking questions about their codebase to explore potential solutions.
Planning: The workspace generates a detailed plan outlining which files will be altered and what actions will be taken in each file. This plan is fully editable.
Implementation: Once the plan is finalized, Copilot Workspace streams coding suggestions into the environment. Developers can test these changes using an integrated terminal or Codespace before finalizing their PRs
Getting Started with Copilot Workspace
To use Copilot Workspace:
Prerequisites: A GitHub account and a GitHub Copilot subscription are required.
Session Initiation: You can open an existing issue or PR in GitHub and click “Open in Workspace” to start a new session pre-seeded with that task. Alternatively, you can create a new session from the dashboard
Conclusion
Copilot Workspace represents a significant evolution in how developers interact with AI tools within their coding environments. By focusing on collaborative workflows and iterative development processes, it aims to enhance productivity and streamline project management for software teams.
Introduced at GitHub Universe 2024, GitHub Spark represents a groundbreaking approach to application development. This AI-powered tool enables users to create and share micro apps (called “sparks”) using natural language, eliminating traditional coding and deployment barriers.
Natural Language Development
At the heart of GitHub Spark lies its intuitive natural language interface. Users can transform their ideas into functional applications simply by describing them in plain English. The system generates live previews almost instantly, enabling rapid prototyping and iterative development through a conversational AI interface.
Comprehensive Development Environment
GitHub Spark offers a complete ecosystem for app creation:
An NL-based editor that understands plain English instructions
A fully managed runtime environment that handles all technical complexities
A Progressive Web App dashboard accessible from any device
Built-in UI components and a customizable design system
Automatic version control for every revision
Collaborative Innovation
The platform emphasizes community and collaboration through:
Flexible sharing options with customizable permissions
The ability to remix and build upon existing sparks
A built-in version control system that preserves every iteration
An active community of creators sharing their work
AI software development services
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Gain a competitive edge with next-level AI software:
Aider stands out as a powerful AI-powered coding assistant that transforms the way developers interact with their codebase. Here are its standout features:
Seamless Code Integration
Intuitive file management with simple commands: `aider <file1> <file2> …`
Automatic Git integration with intelligent commit messages
Support for multiple files simultaneously, perfect for complex changes
Compatible with most popular languages including Python, JavaScript, TypeScript, PHP, HTML, and CSS
Advanced AI Capabilities
Works optimally with GPT-4 and Claude 3.5 Sonnet
Flexible LLM connectivity options for various AI models
Smart codebase understanding using repository mapping
Real-time synchronization with external editor changes
Innovative Interaction Methods
Voice coding capabilities for hands-free programming
Image support for visual context (with GPT-4 and Claude 3.5 Sonnet)
URL content integration for reference materials
Interactive chat-based development workflow
💡 Aider: Pro Tips
Efficient File Management
Add only the files that need modification to the chat
Avoid cluttering the context with unnecessary files
Utilize `/drop` and `/add` commands to manage files dynamically
2. Strategic Approach
Break complex tasks into manageable steps
Use `/ask` command for planning complex changes
Create new files explicitly using `/add <file>` before modification
Troubleshooting Techniques
Use `/clear` to reset chat history when stuck
Switch between models using `/model` command
Share error outputs using `/run` or `/test` commands
Provide up-to-date documentation through chat, URLs, or the `/read` command
Enhanced Interaction
Use Control-C to interrupt non-productive responses
Leverage multiline input methods: – Direct paste – `{` and `}` delimiters – Meta-ENTER for new lines – `/paste` command for clipboard content
Developer Sentiment
After extensive usage, Aider proves to be a powerful ally in development workflows, though with some notable considerations:
👍 Strengths
Excels in multi-file operations and complex codebase navigation
Voice coding feature offers an innovative approach to programming
Impressive integration with version control systems
Architect mode
🚧 Areas for Improvement
Can occasionally struggle with local variable scope in single-file contexts
Sometimes overwrites its own changes in sequential modifications
Method ordering preservation could be enhanced
Overall, Aider shines brightest when used for multi-file refactoring and feature implementation, making it a valuable tool for modern development workflows, especially when approached with an understanding of its optimal use cases.
Cursor IDE represents a significant leap forward in development tools, combining the familiar Visual Studio Code framework with advanced AI capabilities. This innovative code editor is designed to enhance developer productivity through intelligent assistance and streamlined workflows. It’s also great for vibe coding.
Key Features
Intelligent Code Assistance
AI Code Completion: Features sophisticated autocomplete functionality that predicts and suggests multi-line code changes based on your coding patterns
Natural Language Commands: Translates plain English instructions into functional code, allowing developers to generate functions through natural language descriptions
Contextual Code Support: Offers real-time chat functionality for code-specific questions and debugging assistance, with the ability to reference files and images directly within the IDE
Development Workflow Enhancement
Smart Code Optimization: Automatically identifies optimization opportunities, including unused variables and complex expressions that could be simplified
Terminal Integration: Converts natural language into appropriate terminal commands, making command-line operations more intuitive
Real-Time Error Prevention: Continuously scans code for potential issues, providing immediate feedback to prevent bugs from reaching later development stages
Collaboration and Quality
AI Code Review: Performs automated analysis of code changes, focusing on potential bugs and performance issues before deployment
Documentation Generation: Automates the creation of code documentation, maintaining consistency across projects
Multi-Language Ecosystem: Provides robust support for multiple programming languages, with particular expertise in JavaScript, Python, and TypeScript
Tool Integration: Seamlessly works with Git and supports VS Code extensions, enabling enhanced workflows without disrupting existing processes
💡 Pro Tips
1. Optimize Your Navigation
Use `⌘+K` instead of `⌘+L` for direct file editing. This keeps your focus on coding without context switching.
2. Master Multi-File Editing
Enable the Composer feature in Settings > Cursor Settings > Beta
Use `⌘+Shift+I` to make simultaneous edits across multiple files
It’s particularly powerful for large-scale refactoring tasks.
3. Enhance AI Understanding
Add documentation URLs in Cursor Settings > Docs
Train the AI by providing feedback on incorrect suggestions
Define project-specific rules under Settings > General > Rules for AI
Share AI rules across your team for consistent coding standards
4. Leverage Quick Assistance
Use the “Quick Question” feature for immediate code clarification
Take advantage of context-aware chat functionality while coding
Utilize the “Tab” feature for intelligent multi-line edits and error detection
Windsurf IDE: Revolutionizing Development with AI Integration
In the evolving landscape of development tools, Windsurf IDE by Codeium stands out as a groundbreaking integrated development environment that seamlessly merges AI capabilities with traditional coding workflows. This innovative IDE is reshaping how developers approach their daily coding tasks, offering a unique blend of intelligence and efficiency.
Key Features
Windsurf IDE distinguishes itself through several cutting-edge capabilities:
AI-Enhanced Development
Intelligent Code Suggestions: The IDE leverages advanced AI to provide contextually aware code completions, anticipating developers’ needs based on their coding patterns and project context.
Cascade Technology: A revolutionary feature that maintains continuous awareness of developer actions, delivering intuitive and timely contextual support that feels like a natural extension of your thinking process.
Deep Contextual Understanding: The system demonstrates remarkable comprehension of complex codebases, enabling it to offer highly relevant suggestions and insights tailored to your specific project environment.
Collaborative Intelligence
Real-Time AI Collaboration: Experience seamless interaction between developers and AI, with instant feedback and assistance during active coding sessions.
Intelligent Collaboration Mode: The system functions both as a real-time copilot and an autonomous agent, capable of handling complex tasks independently while maintaining natural interaction with developers.
Enhanced Productivity Tools
Multi-File Smart Editing: Efficiently manage large projects with the ability to make coherent edits across multiple files simultaneously.
Command Integration: Execute commands directly within the IDE through AI suggestions, streamlining your workflow by reducing tool switching.
Rapid Prototyping Capabilities: Quickly transform ideas into functional prototypes using AI-generated frameworks, significantly accelerating the initial development phase.
💡 Pro Tips for Maximizing Windsurf IDE
Essential Workflows
Master the Flows Feature – Activate “Flows” to enhance AI-developer collaboration – Let the AI understand your coding context for more accurate assistance
Leverage Cascade Technology – Utilize deep contextual awareness for superior code suggestions – Enable automated issue detection and debugging assistance
Productivity Boosters
Natural Language Integration – Use `Cmd + I` for natural language code generation and refactoring – Communicate complex requirements efficiently through conversational inputs
Advanced Features – Employ codelenses for quick insights and refactoring options – Utilize Supercomplete for advanced action prediction – Configure AI settings via the status bar for a personalized experience
Developer Sentiment
👍 Strengths
The Cascade tool demonstrates impressive capabilities, successfully generating complete applications from single prompts
Seamless terminal command execution directly from the chat window
Innovative checkpoint system allowing easy reversion of changes
Flexible AI continuation of development work from any point
🚧 Areas for Improvement
Limited selection of supported models
Some features may require familiarization time for optimal use
Through its innovative approach to AI-assisted development, Windsurf IDE is setting new standards for what developers can expect from their development environment. While there’s room for expansion in model support, the existing features, particularly the Cascade technology, demonstrate the powerful potential of AI-integrated development tools.
GitHub Copilot stands as one of the pioneering AI coding assistants, revolutionizing how developers approach their daily coding tasks. This tool seamlessly integrates artificial intelligence into the development workflow, offering real-time coding assistance and automation capabilities.
Key Features
Intelligent Code Generation
Advanced code autocompletion that suggests entire code blocks rather than just single variables or methods
Context-aware suggestions that adapt to your coding style and project requirements
Support for multiple programming languages including Python, JavaScript, Ruby, and C++
Developer Experience Enhancements
Interactive chat interface for natural language coding queries
Automated code documentation generation
Built-in test case generation with coverage for edge cases
AI-driven code review suggestions for quality improvement
Collaborative Development Support
Pull request summarization
Change description generation
Context-aware test suggestions
Seamless integration with popular IDEs (Visual Studio Code, JetBrains)
💡 Pro Tips for Maximum Efficiency
Optimize Your Prompts
Be specific and clear in your requests
Break complex tasks into smaller, manageable components
Use descriptive function and variable names to improve suggestions
Leverage Context
Keep relevant files open in your IDE
Add top-level comments to guide Copilot’s understanding
Structure your code logically to improve suggestion accuracy
Review and Iterate
Always validate suggested code for accuracy and security
Use the feedback mechanism to improve future suggestions
Don’t hesitate to rephrase prompts if initial results aren’t satisfactory
Developer Sentiment
As an early player in the AI coding assistant space, GitHub Copilot maintains a strong market position and offers a solid entry point for developers new to AI-assisted coding. However, the landscape is evolving rapidly, and newer tools are emerging with more advanced capabilities.
👍 Strengths
Robust integration with GitHub ecosystem
Well-established user community
Reliable for common coding tasks
Excellent for beginners in AI-assisted development
🚧 Areas for Growth
Competition offering more advanced features
Room for improvement in complex code generation
Limited customization options compared to newer alternatives
Probably limited to only models from Gemini family without the access to the best coding model Claude Sonnet 3.5 right now
Amazon Q Developer brings a suite of AI-powered capabilities designed to enhance the developer experience within the AWS ecosystem. Here’s what makes it stand out.
Key Features
Intelligent Assistance
Conversational Development Support: Leverage natural language interactions to get instant answers about AWS services, coding practices, and troubleshooting steps. The contextual awareness of responses helps maintain development flow without constant context-switching.
Code Enhancement Tools
Smart Code Completion: Experience real-time code completion with context-aware suggestions, streamlining the development process and reducing common coding errors.
Security-First Development: Benefit from automated vulnerability scanning and receive actionable recommendations to strengthen code security during development.
Legacy Code Modernization: Efficiently transform outdated code to modern standards, such as upgrading from Java 8 to Java 17, ensuring your codebase stays current and maintainable.
Development Workflow Integration
Seamless IDE Integration: Work with Amazon Q directly within popular IDEs like Visual Studio Code and JetBrains, maintaining your preferred development environment.
Feature Implementation Support: Transform natural language descriptions into structured implementation plans and functional code, significantly reducing time spent on boilerplate coding.
Team Collaboration Tools: Foster team communication with built-in sharing capabilities and project management features, ensuring everyone stays aligned.
CodeMate transforms the development experience through its comprehensive suite of AI-driven capabilities:
Integrated Development Environment
CodeMate seamlessly integrates with Visual Studio Code through a dedicated extension, supporting major programming languages including C++, Java, Python, and JavaScript. This native integration ensures developers can access all features without leaving their preferred coding environment.
Intelligent Code Analysis
Real-time Error Detection: Instantly identifies and resolves syntax, compile-time, and runtime errors
Quality Assessment: Analyzes code against industry standards, providing contextual improvement suggestions
Performance Metrics: Generates detailed reports with programming scores based on time and space complexity
Advanced AI Capabilities
The platform leverages custom-trained Large Language Models (LLMs) to deliver:
Context-aware code suggestions based on your existing codebase
Intelligent refactoring recommendations
Automated documentation generation for improved maintainability
Developer Workflow Integration
Seamless Git repository integration for enhanced version control
Enterprise-grade security with industry-standard encryption
Flexible subscription plans (Free, Premium, and Enterprise) catering to both individual developers and teams
💡 Tips for Optimal Usage
Keep your VS Code extension updated to access the latest features
Utilize Git integration for better team collaboration
Consider the Premium plan for advanced features if working with larger codebases
Regularly review the automated documentation to ensure it aligns with your team’s standards
Developer Sentiment
After extensive use, developers have reported mixed experiences with CodeMate:
👍 Strengths
Powerful VS Code integration enhances existing workflows
Comprehensive code analysis and error detection capabilities
Strong potential for team collaboration through the web interface
🚧 Areas for improvement
Web application currently supports only single file analysis in chat mode
Web interface can disrupt established development workflows
Some features may require workflow adjustments for optimal use
OpenHands stands out in the AI-assisted development landscape with its comprehensive feature set designed to streamline the software development process:
Immediate Deployment & Security
Zero-wait access with instant platform availability
Enterprise-grade secure sandbox environment for safe code execution
Isolated workspaces enabling parallel development and testing
Intelligent Development Interface
Natural language communication for intuitive coding assistance
Seamless VS Code integration for enhanced developer experience
Real-time code preview and modification capabilities
Dynamic workspace management for multiple development sandboxes
Advanced AI Integration
Support for multiple language models via litellm library
Claude Sonnet 3.5 as the default model, with flexible configuration options
Autonomous complex application generation from backend to frontend
Extensible plugin architecture for enhanced functionality
💡 Pro Tips
Setup & Configuration
Quick Installation – Follow the official Development documentation – Use Mamba for accelerated dependency installation – Ensure Python and Node.js are properly configured
Model Configuration – Execute `make setup-config` for model customization – Configure API keys and model preferences – Enable debugging with `DEBUG=1` for troubleshooting
Development Best Practices
Sandbox Development – Utilize isolated environments for risk-free experimentation – Leverage pre-built agents for specialized tasks – Take advantage of real-time code generation capabilities
Tool Integration – Integrate with VS Code for streamlined development – Use `make help` to access comprehensive command documentation – Employ logging features for effective debugging
User Sentiment
👍 Strengths
Versatile model support, including image processing capabilities
Rich ecosystem of predefined concrete agents
Docker compatibility for containerized development
Strong community support with active contributors
🚧 Areas for Improvement
Docker setup process could be streamlined
Some configuration steps require additional documentation
Despite minor setup challenges, OpenHands maintains its position as a leading agent platform, backed by robust features and an active development community
Gemini Code Assist, developed by Google, is a sophisticated AI coding assistant that seamlessly integrates with popular IDEs to enhance developer productivity. Here are its standout features:
Intelligent Code Generation and Completion
Generates contextually relevant code snippets based on natural language descriptions
Provides real-time code completions as you type, reducing repetitive coding tasks
Adapts to your coding style over time through adaptive learning
Comprehensive Code Understanding
Offers clear explanations of complex code segments in plain language
Assists with debugging by analyzing logs and suggesting potential fixes
Includes source citations from documentation and open-source repositories
Development Workflow Integration
Supports multiple programming languages including Python, JavaScript, and Java
Seamlessly integrates with popular IDEs like VS Code, IntelliJ IDEA, and PyCharm
Provides code refactoring suggestions to improve efficiency and maintainability
💡 Pro Tips
1. Maximize Code Generation
– Write descriptive comments to generate specific code blocks – Use direct, clear prompts in the chat feature (e.g., “Generate unit tests for this function”) – Accept inline suggestions using the Tab key to speed up coding
2. Optimize Your Workflow
– Utilize smart actions for quick access to common tasks like refactoring and testing – Customize keyboard shortcuts to match your preferences – Reset chat history regularly to maintain focused, relevant interactions
3. Leverage Learning Features
– Experiment with different prompt styles to understand the tool’s capabilities – Use the code explanation feature to understand complex codebases – Take advantage of the multi-language support for cross-project development
Developer Sentiment
After extensive use, here are the key takeaways about Gemini Code Assist:
👍 Strengths
Seamless IDE integration
Strong code generation capabilities
Adaptive learning that improves over time
Comprehensive language support
March update: Gemini Code Assist is now free. Learn more here.
🚧 Areas for Improvement
Limited to Gemini model family
Preview status with some features still in development
OpenAI’s “Work with Apps” feature represents a significant advancement in ChatGPT’s capabilities, particularly for macOS users. This integration enables ChatGPT to seamlessly interact with desktop applications, providing context-aware assistance that transforms how users interact with their development environment.
Contextual Understanding
ChatGPT demonstrates remarkable ability to read and interpret content from compatible applications, including major development tools like Xcode, Visual Studio Code, Terminal, and iTerm2. This deep integration ensures that assistance is always relevant to your current work context, eliminating the traditional barriers between AI assistance and actual development work.
Smart Content Integration
The system automatically incorporates relevant content from your active application, with the capability to process up to 200 lines of code or the entire content of your active window. This automatic inclusion eliminates the tedious process of copying and pasting code snippets, streamlining the workflow significantly.
Intuitive Interface Controls
Users enjoy a streamlined experience with:
Direct application selection within the chat interface
Clear visual indicators showing the active application
Seamless switching between different contexts
Real-time feedback on connected applications
Precision Through Selection
The feature’s ability to focus on highlighted text while maintaining awareness of the surrounding context ensures precise, targeted assistance. This selective focus capability makes it particularly valuable for debugging specific code segments or analyzing particular sections of larger files.
Enhanced VS Code Integration
The dedicated Visual Studio Code extension takes the integration to another level, offering:
Direct code snippet selection for analysis
Contextual suggestions within your development environment
Seamless switching between coding and AI assistance
💡 Tips for Optimal Use
Leverage Multiple Contexts: Switch between different applications to get comprehensive assistance across your development stack.
Use Text Selection Strategically: Highlight specific code sections when you need focused help while keeping the broader context available.
Combine with VS Code Extension: For development work, use the dedicated extension to maximize the integration benefits.
User Sentiment
After extensive use, the feature presents a mixed bag of impressions:
👍 Strengths
Seamless integration with existing GPTs
Advanced voice mode compatibility
Excellent for code review and testing tasks
Intuitive interface and context switching
🚧 Limitations
File attachment functionality is disabled in this mode
Limited selection of supported applications
Best suited for review and testing rather than extensive development work
Overall, “Work with Apps” demonstrates significant potential in streamlining development workflows, particularly excelling in code review and testing scenarios, despite some current limitations in its application support and file handling capabilities.
Cline, formerly known as Claude Dev, revolutionizes the software development workflow through several innovative capabilities.
Key Features
Intelligent Task Processing
The assistant understands natural language requests and executes complex coding tasks by analyzing project context and structure. This enables seamless integration into existing development workflows while maintaining high accuracy in code generation and modifications.
Real-Time Development Support
Streams responses directly into popular IDEs like VS Code, eliminating waiting times
Provides immediate feedback during coding sessions
Supports file creation, editing, and command execution within the development environment
Advanced Project Management
Implements a human-in-the-loop approach through diff view previews
Enables review and approval of proposed changes before implementation
Reduces the risk of unintended code modifications
Browser Automation & Integration
Performs automated web interactions for testing and debugging
Executes real-time browser actions like clicking and typing
Streamlines web development workflows
Cost-Effective Architecture
Integrates with OpenRouter to access various AI models, including free options
Reduces API calls by up to 40% compared to traditional coding assistants
Maintains performance efficiency while optimizing resource usage
Tips
Model Selection Strategy
Utilize Qwen2.5 models for routine tasks to optimize costs
Consider task complexity when choosing between different AI models
Leverage free model options through OpenRouter for appropriate use cases
Workflow Optimization
Implement XML-based prompt engineering for precise interactions
Use the cancel button to maintain control over ongoing tasks
Review changes in diff view before applying modifications
Manage context size limitations by configuring provider blacklists
Developer Sentiment
The experience of using Cline reveals both its strengths and areas for improvement:
👍 Strengths
Innovative implementation of Anthropic’s technology
Excellent cost tracking capabilities
Comprehensive integration with development environments
🚧 Areas for Improvement
Initial interaction can feel rushed as it attempts to solve problems immediately
Interactive chat interface for instant help with configurations and Git commands
Intelligent root cause analysis for CI/CD pipeline failures, complete with suggested solutions
AI Impact Dashboard for tracking development metrics and productivity improvements
Enterprise Additions
GitLab Duo Enterprise extends these capabilities with:
Discussion summarization for efficient stakeholder communication
Enhanced chat capabilities for complex troubleshooting scenarios
Advanced project management features powered by AI
💡 Pro Tips
Maximize Code Suggestions: Enable code suggestions in your most frequently used languages first to build familiarity with the system
Pipeline Optimization: Use the root cause analysis feature proactively by reviewing suggested fixes even when pipelines succeed to identify potential improvements
Security Integration: Configure vulnerability scanning to run automatically in your CI/CD pipeline for continuous security monitoring
Developer Sentiment
After extensive hands-on experience with GitLab Duo, here’s what stands out:
👍 Strengths
Comprehensive AI integration across the DevSecOps pipeline
Strong security-focused features
Valuable productivity enhancements for teams
🚧 Areas for Improvement
IDE integration lags behind competitor offerings
Initial setup process needs simplification
Several key features remain in beta, indicating room for maturity
JetBrains AI Assistant seamlessly integrates into JetBrains IDEs, offering a comprehensive suite of AI-powered features to enhance developer productivity:
Code Intelligence
Smart Code Generation: Create code snippets from natural language descriptions, reducing time spent on boilerplate code
Context-Aware Completion: Get intelligent suggestions for variables, methods, and documentation, with enhanced support for Java, Kotlin, and Python
Proactive Bug Detection: Identify and fix potential issues early in the development cycle through AI-powered code analysis
Development Workflow
Automated Testing: Generate comprehensive unit tests based on specified functionality requirements
Documentation Assistant: Automatically produce well-structured markdown documentation by analyzing code structure and comments
Intelligent Refactoring: Receive suggestions for code optimization and better naming conventions based on project context
Developer Experience
In-Line Code Generation: Create code snippets directly within the editor using natural language prompts
Interactive Chat Interface: Access a dedicated chat window for project-specific questions and coding guidance
Seamless IDE Integration: Experience smooth workflow integration across all JetBrains development environments
💡 Tips
Optimize Your Workflow – Use keyboard shortcuts for quick access to the AI chat window – Save frequently used prompts for repetitive tasks – Leverage in-line code generation for uninterrupted coding
Maximize Context – Include relevant code snippets when asking for help – Tailor queries to your specific project needs – Manage chat context settings for more accurate responses
Leverage Advanced Features – Utilize automated documentation generation for better project maintenance – Take advantage of refactoring suggestions to improve code quality – Use the AI assistant for learning complex algorithms and code patterns
CodeRabbit distinguishes itself through several powerful capabilities:
Intelligent Review System
The platform’s AI-driven code review system provides contextual, line-by-line feedback on pull requests, significantly reducing manual review time. What sets it apart is its ability to learn from user interactions, delivering increasingly refined and relevant suggestions over time.
Interactive Development Environment
Real-time chat functionality enables dynamic discussions within the review context
Direct code generation capabilities during review sessions
Seamless integration with GitHub and GitLab workflows
Continuous and incremental review processes as new commits are made
Advanced Analysis Tools
Automated bug detection and documentation generation
Comprehensive pull request summaries with high-level insights
Integration with project management tools like Jira
Customizable review parameters for team-specific requirements
Collaborative Features
Incremental review system for managing large codebases
Real-time collaboration tools for distributed teams
Automated documentation of review discussions
Direct commit capabilities from the GitHub interface
💡 Tips for Maximizing CodeRabbit’s Potential
Review Process Optimization
Implement a structured review checklist incorporating coding standards, security practices, and error handling
Utilize the incremental review feature for early issue detection
Configure custom review parameters aligned with team standards
Document all feedback systematically for future reference
Team Collaboration
Engage actively in real-time chat discussions for deeper technical insights
Involve multiple reviewers while using CodeRabbit as an additional perspective
Maintain regular review schedules to foster continuous improvement
Focus on constructive, actionable feedback delivery
Technical Integration
Leverage the line-by-line feedback feature for detailed code analysis
Stay updated with new features and platform capabilities
Utilize integration capabilities with existing development tools
Set up automated documentation generation for consistent record-keeping
Developer Sentiment
The reduction in manual review overhead
Enhanced code quality through AI-driven insights
Improved team collaboration through real-time features
Streamlined workflow integration with existing tools
The tool proves especially valuable for:
Large development teams managing high PR volumes
Open source projects requiring consistent code quality
Startups needing to maintain velocity without compromising standards
Qodo Merge (formerly PR-Agent) revolutionizes the pull request workflow by bringing AI-powered assistance directly into your development process. Here’s what makes it stand out:
Intelligent PR Analysis
The core strength of Qodo Merge lies in its automated analysis capabilities. It thoroughly examines pull requests, evaluating code quality, identifying potential security vulnerabilities, and ensuring proper test coverage. This proactive approach helps catch issues before they make it into your main branch.
Interactive Development Experience
Context-Aware PR-Chat: Engage in private, contextual conversations about your code right within GitHub. The chat feature understands your PR context, including descriptions and commit messages, to provide relevant responses.
Smart Code Suggestions: Receive committable code improvements with auto-expanding code blocks for better visibility. The intuitive “quote-and-reply” feature streamlines specific comment discussions.
Command-Based Interface: Simplify your workflow with straightforward commands like `/review`, `/describe`, `/improve`, and `/ask` for quick PR interactions.
Seamless Integration
Chrome Extension: Access all tools directly from your PR interface through a convenient toolbar extension
Repository Flexibility: Works with both open-source and private repositories (Pro version required for private repos)
Customizable Filtering: Sort and filter conversation messages to focus on specific types of feedback
Community-Driven Development
As an open-source project, Qodo Merge benefits from community contributions, allowing for continuous improvement and customization to meet diverse development needs.
💡 Pro Tips
Use the filtering options strategically during complex PR discussions to maintain clarity
Leverage the PR-Chat feature for quick clarifications without cluttering the main PR thread
Combine multiple commands to get comprehensive PR feedback in one go
Consider contributing to the open-source codebase to add features that match your team’s workflow
Developer Sentiment
👍 Strengths
Open-source nature provides transparency and customization options
Comprehensive suite of PR tools in one package
Self-hosting capability for teams requiring full control
🚧 Areas of Consideration
Private repository support requires Pro version
Learning curve for effective command usage
Setup process for self-hosting requires technical expertise
Supermaven serves as a coding assistant that integrates with popular code editors like VS Code and JetBrains IDEs. It utilizes an AI model named Babble, designed to provide context-aware code completions and suggestions. Babble boasts a context window capable of processing up to 1 million tokens, allowing it to understand extensive codebases effectively
The project is closed as it merged with Cursor IDE :
We’re excited to announce that Supermaven is joining Cursor!
Devin is a tireless, skilled teammate, equally ready to build alongside you or independently complete tasks for you to review. With Devin, engineers can focus on more interesting problems and engineering teams can strive for more ambitious goals.
ChatDev that leverages large language models (LLMs) to streamline the entire software development process through natural language communication.
Key points
ChatDev is a virtual chat-powered software development company that mirrors the waterfall model, dividing the process into four stages: designing, coding, testing, and documenting.
At each stage, ChatDev recruits “software agents” with different roles, such as programmers, reviewers, and testers, who engage in collaborative dialogue to propose and validate solutions.
The chat chain breaks down each stage into atomic subtasks, enabling dual roles to discuss and resolve specific issues through context-aware communication.
To address code hallucination challenges, ChatDev introduces a “thought instruction” mechanism where an instructor explicitly provides guidance to the assistant programmer on code modifications.
Experiments show ChatDev’s efficiency and cost-effectiveness, with the ability to complete the entire software development process in under 7 minutes and at a cost of less than $1.
The framework demonstrates the potential of integrating LLMs into software development, streamlining key processes and promoting effective collaboration among diverse roles.
Here’s how GPT Pilot builds apps, according to a quote from a project’s GitHub README:
You enter the app name and the description.
Product Owner agent like in real life, does nothing. 🙂
Specification Writer agent asks a couple of questions to understand the requirements better if project description is not good enough.
Architect agent writes up technologies that will be used for the app and checks if all technologies are installed on the machine and installs them if not.
Tech Lead agent writes up development tasks that the Developer must implement.
Developer agent takes each task and writes up what needs to be done to implement it. The description is in human-readable form.
Code Monkey agent takes the Developer’s description and the existing file and implements the changes.
Reviewer agent reviews every step of the task and if something is done wrong Reviewer sends it back to Code Monkey.
Troubleshooter agent helps you to give good feedback to GPT Pilot when something is wrong.
Debugger agent hate to see him, but he is your best friend when things go south.
Technical Writer agent writes documentation for the project.
Tests
Result
Comment
Simple TODO KTOR crud application
Failure
Quite promising. It took GPT Pilot 2 hours, some assistance, and manual intervention to complete a basic app with only one endpoint. Despite this, the overall process shows potential. Most issues stemmed from dependency management, import errors, and missing code sections. The total cost of this experiment was around $15.
MetaGPT is a multi-agent framework based on Large Language Models (LLMs) that aims to redefine the paradigms of task execution, collaboration, and decision-making in the workplace. It consists of two primary layers:
Foundational components layer: Provides the essential building blocks for individual agent operations, including environment, roles, tools, and actions.
Collaboration layer: Breaks down complex tasks, assigns them to appropriate agents, and ensures adherence to guidelines while fostering data sharing and a shared knowledge base.
Key features of MetaGPT include role definitions, quick learning, knowledge sharing, and a human-centric approach. It offers benefits such as automation, integration of human SOPs, creative program generation, and enhanced performance through multiple AI agents.
Benefits
Compared to other LLM-based frameworks, MetaGPT stands out in terms of scalability, customizability, and consistent performance across diverse benchmarks. Its development philosophy emphasizes adaptability, user-centricity, and a collaborative ecosystem.
Limitations
However, MetaGPT is still under development and may not be ideal for highly intricate projects. Its capabilities are also restricted to its training data, necessitating frequent updates for accuracy.
Key Points
Concept of MetaGPT: MetaGPT is designed to address the limitations of existing LLM-based multi-agent systems which often produce inconsistent logic due to cascading errors. It incorporates human-like workflows to streamline and standardize the development process, thus reducing errors and improving efficiency.
Standardized Operating Procedures (SOPs): The framework utilizes SOPs to guide the interactions and responsibilities among agents. SOPs help in breaking down complex tasks into simpler subtasks and defining clear roles for each agent.
Role-based system: MetaGPT assigns specific roles and responsibilities to different agents, such as Product Manager, Architect, Engineer, etc. Each role has defined inputs and outputs, which are strictly adhered to, ensuring a coherent workflow.
Communication protocols: To avoid miscommunications that commonly occur in unstructured natural language interactions, MetaGPT employs structured communication interfaces. Agents communicate through specific, structured outputs like flowcharts, design artifacts, and documented requirements, reducing the risk of information loss or distortion.
Executable feedback mechanism: An innovative aspect of MetaGPT is its executable feedback mechanism, which allows continuous code verification and debugging during runtime, thereby enhancing the quality of the generated code.
Empirical validation: The article reports that MetaGPT has been tested against benchmarks like HumanEval and MBPP, showing superior performance in terms of task completion rates and code quality compared to existing systems.
Collaborative software engineering: MetaGPT has proven particularly effective in collaborative software engineering scenarios, showing its capability to manage complex software development tasks with multiple agents involved.
Tests
Prompt
Result
Comment
Simple TODO KTOR crud application – basic prompt (5, 10 and 15 round attempts)
create simple todo crud application in Ktor with jwt authentication, and serialization
Failure
– Missing classes, build files, authentication or content negotiation, some classes generated in another language
+ Proper dependencies used
Simple TODO KTOR crud application – advanced prompt (30 rounds)
Create a simple TODO CRUD application in Ktor with JWT authentication and serialization.
**Requirements:**
– Use Ktor for building the server-side application
– Implement a CRUD functionality for managing TODO items (Create, Read, Update, Delete)
– Include JWT authentication for securing the endpoints
– Use Kotlin serialization for handling JSON data
– Include a `build.gradle` file for managing dependencies
Feel free to ask if you need any help or further clarification.
Failure
– Missing classes, build files, authentication or content negotiation, some classes generated in another language
Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well.
This tool is integrated into the GitHub Actions workflow.
Similar to Korbit, it generates a substantial number of comments, many of which may be considered redundant or unhelpful. (also there are duplications between this tool and Korbit so it seems they use similar prompting strategy)
However, it struggles with larger code diffs, potentially limiting its effectiveness in complex projects.
AI Code Review Action on GitHub utilizes the publicly available GPT-3.5 Turbo model.
Tests
Result
Comment
200 line MR
Failure
27 comments, focused on changed lines, useless in the context of the whole project.
Most comments focus on test naming, but these are invalid.
Usefulness of comments: While both Korbit and AI Code Review Action on GitHub generate numerous comments, the sheer volume can make it challenging to identify genuinely useful feedback. On the other hand, CodeRabbit’s approach of providing comprehensive overviews and patch notes may be more effective in conveying meaningful insights.
Scope of review: CodeRabbit stands out by not only reviewing code but also suggesting best practices, which can be invaluable for maintaining high-quality code and adhering to industry standards.
Data privacy: While data privacy is a critical aspect of any code review tool, CodeRabbit explicitly prioritizes the protection of sensitive information, giving it an advantage in security-conscious environments
Honest opinion
I currently see 3 use cases for these tools:
Private projects
Projects with a single developer
Low-commercial-experience developers
AI-powered code assistants: Comparative analysis
Code completion accuracy: GitHub Copilot and Cloud Code provides accurate and context-aware code completions, thanks to its ability to understand the project’s codebase.
Data privacy: GitHub Copilot offers configurable data privacy settings, allowing users to control the level of information shared with the tool. On the other hand, Google Cloud Code lacks transparency regarding its data privacy practices.
AI agents: Comparative analysis
MetaGPT incorporates human-like workflows and standardized operating procedures (SOPs) to address the limitations of existing LLM-based approaches. It assigns specific roles and responsibilities to different agents, promoting a coherent and structured development process. MetaGPT’s features include an executable feedback mechanism for continuous code verification and debugging, as well as a focus on knowledge sharing and collaboration.
Similarly, GPT Pilot takes a step-by-step approach, with each agent (e.g., specification writer, architect, developer) playing a distinct role in the software development process. This structured workflow helps to mitigate the risk of cascading errors and inconsistencies.
While these AI agent-based frameworks demonstrate the potential of integrating LLMs into software development, they are still in their early stages of development and not yet ready for widespread production use. In our tests, they were unable to generate a complete and functional TODO application with Ktor, JWT authentication, and serialization, highlighting the need for further refinement and maturation before they can be reliably used for complex software projects.
Contents
AI developer tools FAQ
Frequently asked questions about AI tools used in software development.
What are AI developer tools?
AI developer tools are a collection of software applications and libraries that assist developers in building, testing, and deploying artificial intelligence functionalities within their software. These tools can streamline workflows and improve the efficiency of AI development.
What is generative AI and how does it benefit software developers?
Generative AI, such as models developed by OpenAI, can create code snippets, automate repetitive tasks, and assist in debugging, significantly enhancing productivity and efficiency for software developers.
How are AI models used in coding tools?
AI models are integrated into coding tools and IDEs to provide intelligent code suggestions, automate error detection, and generate documentation, streamlining the development process.
Who can benefit from AI developer tools?
AI developer tools are beneficial for various developers, including those with experience in machine learning, data science, and traditional software development. Even beginners can leverage user-friendly tools to integrate basic AI features.
What are the common use cases for AI developer tools?
Training and deploying machine learning models for tasks like image or speech recognition, natural language processing, and anomaly detection.
Automating repetitive coding tasks and generating code snippets based on developer intent.
Optimizing software performance and identifying potential bugs through AI-powered analysis.
What types of AI developer tools are available?
I developer tools come in many flavors, designed to assist programmers in various stages of the development workflow. Here's a breakdown of some common types:
Code completion and assistants: These tools use AI to predict the next line of code, suggest code snippets, or even generate entire functions. Examples include Tabnine, JetBrains AI assistant, and aiXcoder.
Code review and debugging: Tools in this category can analyze code for errors, suggest improvements, and even help with debugging complex problems. Some examples include Codium, Stepsize AI, and Sourcery.
Documentation generation: These AI-powered tools can automatically generate documentation from your code, saving developers time and effort. Rewind.ai is a popular example.
General AI assistants: Some development environments like Replit include built-in chatbots powered by AI that can answer questions, provide suggestions, and even help with debugging.
UI/UX Design assistants: There are AI tools that can help with designing user interfaces by generating mockups or suggesting layouts based on user data. While these aren't strictly code-focused, they can be valuable for developers involved in the entire application creation process.
How do you use AI in your development process?
At Pragmatic Coders, we use AI tools to generate code, brainstorm, or streamline daily tasks.
Why do you need to integrate AI developer tools into your product lifecycle ASAP?
Artificial intelligence is crucial to do things faster: experiment, make mistakes, and learn from then.
Joe Justice, ex-Tesla employee and Agile coach shared with us his observations on AI implementation:
I think companies that aren’t using AI are behind, and those that aren’t using their own AI have missed the opportunity to start training it.
Once you start training your own AI, you see which types of data and datasets are most useful. This realization starts to change how you gather information and even change how you work to make it easier to gather information. They haven’t even started that learning curve yet.
Most importantly, AI is crucial to innovate, which you most probably want to do if you're building digital products.
AI-powered IDEs, like Visual Studio Code with Copilot, offer intelligent features that assist developers in writing, optimizing, and debugging code more efficiently.
What is the role of OpenAI in advancing AI tools for developers?
OpenAI has developed powerful AI models like GPT-4 that are integrated into various development tools, providing advanced assistance in coding, debugging, and project management.
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