Exploring and Evaluating Integrated Development Environments (IDEs)Permalink

In this article we will explore the history of IDEs and how they have evolved over time. Apart from this we will see how to evalute the best IDE for your needs and what are IDE in AI era.

A Broad Timeline of IDE DevelopmentPermalink

1. Efficiency (1970s–1980s): Focus on minimalism and raw functionality for basic coding tasks.
2. Enhancement (1980s–1990s): Adding productivity tools and integration for growing complexity.
3. Integration (1990s–2000s): Combining editing, debugging, and building into a cohesive environment.
4. Collaboration (2000s–2010s): Expanding accessibility and teamwork via cloud and networks.
5. Intelligence (2010s–Early 2020s): Leveraging AI for smarter, reactive assistance.
6. Agency (Mid-2020s): Shifting to proactive, context-aware systems that act as co-developers.

Features to Evaluate IDEPermalink

To compare any Integrated Development Environment (IDE) we need a structured set of features that capture both traditional IDE capabilities and the advanced AI-driven functionalities. These features span core IDE functionality, AI-specific enhancements, usability, and integration, allowing for a fair evaluation competitors like windsurf, Cursor, VS Code with Copilot, JetBrains, or others. Currently I am using windsurf heavily so this feature list is influenced by windsurf.

Modern IDE FeaturesPermalink

1. Purpose build models Support, number of AI Models supported
2. Next intent prediction
3. Multi-file multi-edit capability
4. Explaining code
5. Deep contextual awareness
6. LLM-based search tools
7. Terminal Command suggestions
8. Iterative reasoning
9. Implicit reasoning of user actions
10. Rapid application prototyping
11. IDE’s ability to Search code
12. Scalable conversation history
13. Multi-repo codebase awareness
14. Collaborative, agentic chat experience
15. Supercomplete: Suggestions based on next intent
16. High Quality Autocomplete and Speed of Autocomplete
17. Defining your Role (Web Developer using React.JS, Database Developer using PLSQL) in IDE
18. Output Preview and Taking Screenshots
19. Rapid inline code suggestions
20. Ability to Refactor, explain, unit test, document
21. MCP (Model Contex Protocol) integration
22. Command: Inline instruction-based refactoring
23. Terminal Commands: Reading from the terminal, directory, documentation, specific files, websites
24. Ability to attach and analyse images, screenshots
25. Performance: Responsiveness and speed of the IDE.
26. Customization: Ability to modify and extend the environment.
27. Integration: Support for version control, build tools, and other development tools.
28. Community: Availability of plugins, extensions, and community support.
29. Cost: Pricing model and cost of maintenance.
30. Cross-Platform: Support for multiple operating systems.
31. AI Integration: Availability of AI features and tools.

Understanding some of these featuresPermalink

For some people it may be difficult to understand the meaning of the list above, therefore let’s understand this little further with few examples.

1. Core IDE FunctionalityPermalink

• Code Editing
  • Syntax highlighting, code folding, and multi-cursor editing support.
  • Does it offer robust text editing capabilities out of the box?
• Debugging Tools
  • Breakpoints, step-through debugging, and variable inspection.
  • How integrated and intuitive is the debugging experience?
• Version Control Integration
  • Native support for Git, GitHub, or other VCS (e.g., commit, pull, push from within the IDE).
  • Windsurf syncs with GitHub via Cascade—does the IDE match this depth?
• Language Support
  • Number of programming languages supported (e.g., Python, JavaScript, Java, etc.).
  • Does it handle polyglot projects as seamlessly as Windsurf?
• Build and Run Tools
  • Ability to compile, execute, and test code within the IDE.
  • Are these processes streamlined or require external setup?

2. AI-Specific FeaturesPermalink

• Autocompletion
  • Context-aware code suggestions (e.g., single-line, multi-line, or full functions).
  • Windsurf’s Supercomplete feature predicts multi-cursor edits—does the IDE match this?
• Natural Language Coding
  • Ability to generate code from natural language prompts (e.g., “create a REST API”).
  • How well does it interpret intent compared to Windsurf’s agentic approach?
• Codebase Understanding
  • Comprehension of the entire repository for suggestions or refactorings.
  • Windsurf excels at repo-wide context—does the IDE keep pace?
• Agentic Behavior
  • Proactive actions based on inferred developer intent (e.g., suggesting fixes or edits across files).
  • Is the IDE reactive (like Copilot) or anticipatory (like Windsurf)?
• Code Refactoring
  • AI-driven suggestions for improving code structure, performance, or readability.
  • How automated and intelligent is the refactoring compared to Windsurf?
• Error Detection and Fixes
  • Real-time identification of bugs with suggested solutions.
  • Does it match Windsurf’s ability to preemptively address issues?
• Learning Curve for AI Features
  • Ease of use for beginners leveraging AI tools.
  • Windsurf’s Wave 4 update improved beginner-friendliness—how does the IDE compare?

3. Usability and PerformancePermalink

• User Interface (UI)
  • Cleanliness, customizability, and intuitiveness of the layout.
  • Does it feel as polished as Windsurf’s AI-native design?
• Performance
  • Speed of loading, responsiveness, and resource usage (CPU/memory).
  • Can it handle large projects as efficiently as Windsurf?
• Cross-Platform Support
  • Availability on Mac, Windows, Linux (Windsurf supports all three).
  • Is it as versatile in deployment?
• Customization
  • Extensibility via plugins, themes, or settings.
  • How flexible is it compared to Windsurf’s tailored experience?

4. Integration and EcosystemPermalink

• Toolchain Integration
  • Sync with external tools like Slack, GitHub, or CI/CD pipelines (e.g., Windsurf’s Cascade).
  • Does it offer a cohesive workflow across development tools?
• Cloud Collaboration
  • Real-time collaboration features for teams.
  • Windsurf emphasizes solo productivity—does the IDE extend to team use?
• Marketplace/Extensions
  • Availability of additional plugins or AI models.
  • Can it expand functionality like Windsurf’s focused but growing ecosystem?
• Security Features
  • Code scanning for vulnerabilities or compliance (e.g., AWS CodeWhisperer’s strength).
  • Does it prioritize security as part of the AI toolkit?

5. Cost and AccessibilityPermalink

• Pricing Model
  • Free tier, subscription cost, or one-time purchase.
  • Windsurf is noted as cheaper than Cursor—how does the IDE stack up?
• Offline Functionality
  • Ability to work without an internet connection.
  • Does it match Windsurf’s presumed local capabilities (assuming standard IDE norms)?
• Community Support
  • Size and activity of user community (forums, X buzz, tutorials).
  • Is there as much enthusiasm as Windsurf’s growing X hype?

6. Unique DifferentiatorsPermalink

• Special Features
  • Any standout capabilities unique to the IDE (e.g., Windsurf’s “mind-meld” intent prediction).
  • What sets it apart from Windsurf’s agentic focus?
• Target Audience
  • Designed for beginners, professionals, or specific domains (e.g., AWS for CodeWhisperer).
  • How does its focus align with Windsurf’s broad appeal?

Evolution of IDE CapabilitiesPermalink

We can trace the evolution of the IDE ecosystem by grouping capabilities into distinct phases of progress.

1. Early Text Editors (1970s–1980s)Permalink

The foundation of coding tools began with minimalistic editors on Unix/Linux systems, like vi (released 1976), designed for efficiency on resource-constrained hardware.

• Basic Text Editing
  • Insert, delete, and modify text in a file (e.g., vi’s command and insert modes).
  • Line-based navigation (e.g., j, k, h, l in vi).
• File Management
  • Open, save, and close files (e.g., :w, :q in vi).
• Search and Replace
  • Simple pattern matching for finding and substituting text (e.g., :%s/old/new/g in vi).
• Minimal Resource Usage
  • Lightweight design to run on terminals with limited memory and processing power.
• Keyboard-Driven Workflow
  • Command-line interface with no graphical elements, optimized for speed.

2. Enhanced Editors and Early IDEs (1980s–1990s)Permalink

As hardware improved and programming complexity grew, editors like Emacs and early IDEs (e.g., Turbo Pascal) added functionality beyond basic editing.

• Syntax Highlighting
  • Color-coded text to distinguish keywords, variables, and comments (introduced in editors like Emacs).
• Customizable Keybindings
  • User-defined shortcuts for efficiency (e.g., Emacs’ extensibility via Lisp).
• Integrated Compilation
  • Ability to compile code within the editor (e.g., Turbo Pascal’s built-in compiler).
• Basic Debugging
  • Step-through execution and error checking (e.g., Borland IDEs’ rudimentary debuggers).
• Project Management
  • Grouping files into projects for multi-file development (e.g., early IDEs like Borland C++).
• Extensibility
  • Support for macros or plugins to extend functionality (e.g., Emacs’ ecosystem).

3. Mature IDEs (1990s–2000s)Permalink

The rise of graphical user interfaces (GUIs) and object-oriented programming spurred fully integrated environments like Eclipse, Visual Studio, and JetBrains IntelliJ.

• Graphical Interface
  • Mouse-driven navigation, toolbars, and windows for a visual workflow.
• Advanced Code Completion
  • Context-sensitive suggestions for variables, methods, and classes (e.g., IntelliJ’s IntelliSense-like features).
• Robust Debugging
  • Breakpoints, watch variables, stack traces, and interactive debugging (e.g., Eclipse’s debugger).
• Version Control Integration
  • Built-in support for Git, SVN, etc., with commit and diff views (e.g., Visual Studio’s VCS tools).
• Refactoring Tools
  • Automated renaming, method extraction, and code restructuring (e.g., JetBrains’ refactoring suite).
• Language-Specific Tooling
  • Deep support for specific languages (e.g., PyCharm for Python, JBuilder for Java).
• Build Automation
  • Integration with build systems like Make, Ant, or Maven for compiling and deploying code.
• GUI Designers
  • Visual tools for designing interfaces (e.g., Visual Basic’s form designer).

4. Collaborative and Cloud-Based IDEs (2000s–2010s)Permalink

The internet era brought collaboration and accessibility, with tools like NetBeans and cloud platforms like Replit.

• Real-Time Collaboration
  • Multiple developers editing the same codebase simultaneously (e.g., Replit’s multiplayer mode).
• Cloud Execution
  • Running and testing code in the browser without local setup (e.g., Replit’s cloud runtime).
• Package Management
  • Integrated tools for installing libraries (e.g., npm, pip in NetBeans or VS Code).
• Cross-Platform Support
  • Consistent experience across Linux, Windows, and Mac (e.g., Eclipse’s Java-based portability).
• Unit Testing Frameworks
  • Built-in support for writing and running tests (e.g., JUnit integration in Eclipse).
• Code Formatting
  • Automatic indentation and style enforcement (e.g., VS Code’s Prettier integration).

5. AI-Enhanced IDEs (2010s–Early 2020s)Permalink

The integration of artificial intelligence, starting with tools like GitHub Copilot, added predictive and assistive capabilities.

• AI-Powered Autocompletion
  • Predictive suggestions for entire lines or functions based on context (e.g., Copilot’s GPT-based completions).
• Natural Language Coding
  • Generating code from plain English prompts (e.g., “write a sorting algorithm” in Copilot).
• Error Detection and Fixes
  • Real-time identification of bugs with suggested solutions (e.g., VS Code with AI extensions).
• Code Explanation
  • Inline documentation or explanations of complex code (e.g., Replit Ghostwriter’s features).
• Security Scanning
  • AI-driven detection of vulnerabilities (e.g., Amazon CodeWhisperer’s scans).
• Multi-File Awareness
  • Suggestions based on understanding multiple files in a project (e.g., early AI tools like Tabnine).

6. Modern Agentic IDEs (Mid-2020s–Present)Permalink

Tools like Windsurf (2025) represent the latest evolution, where AI doesn’t just assist but proactively shapes the development process.

• Agentic Behavior
  • Anticipating developer intent and performing repo-wide actions (e.g., Windsurf’s predictive edits).
• Repo-Wide Context
  • Deep understanding of the entire codebase for coherent suggestions (e.g., Windsurf’s project awareness).
• Multi-Cursor Supercompletion
  • Simultaneous edits across multiple locations with AI guidance (e.g., Windsurf’s Supercomplete).
• Workflow Integration
  • Syncing with external tools like GitHub or Slack for a seamless pipeline (e.g., Windsurf’s Cascade).
• Proactive Refactoring
  • Suggesting and applying structural improvements without manual prompting.
• Beginner-Friendly AI
  • Simplified interfaces and smarter assistance for new developers (e.g., Windsurf’s Wave 4 updates).
• Performance Optimization
  • AI-driven suggestions for faster, more efficient code execution.

Some Linux Based IDEPermalink

These are IDEs that either originated on Linux, are optimized for it, or have strong native support, often open-source and tied to the Linux ecosystem.

1. Geany
   • Description: A lightweight, fast IDE built for Linux, supporting over 50 programming languages (C, Python, Java, etc.). It’s designed to work with minimal dependencies, avoiding heavy desktop environment reliance (e.g., GNOME or KDE).
   • Linux Roots: Native to Linux, requiring only GTK libraries. Available on most distros via package managers (e.g., sudo apt install geany on Ubuntu).
   • Features: Syntax highlighting, code folding, basic autocompletion, plugin support.
   • Status: Actively maintained, popular for its simplicity and speed on Linux.
2. KDevelop
   • Description: A powerful, open-source IDE from the KDE community, tailored for C, C++, Python, QML/JavaScript, and PHP development on Linux.
   • Linux Roots: Built on KDE and Qt libraries, deeply integrated with Unix-like systems. Installable via sudo apt install kdevelop or similar.
   • Features: Advanced code completion, debugging (with GDB), Git integration, extensible via plugins.
   • Status: Still active, widely used by Linux developers for system-level programming.
3. Code::Blocks
   • Description: An open-source, cross-platform IDE with strong Linux support, primarily for C, C++, and Fortran.
   • Linux Roots: While cross-platform, it’s a staple in Linux communities, installable via sudo apt install codeblocks.
   • Features: Customizable build system, debugging, plugin support for additional languages.
   • Status: Maintained, though less flashy than modern AI IDEs, it’s a reliable Linux classic.
4. Eclipse
   • Description: A versatile, open-source IDE initially focused on Java but extensible to many languages via plugins (e.g., PyDev for Python).
   • Linux Roots: Fully supported on Linux (e.g., via Snap: sudo snap install eclipse --classic), with a strong presence in open-source development.
   • Features: Robust debugging, refactoring, version control integration, vast plugin ecosystem.
   • Status: Still widely used, though considered heavier than newer tools.
5. GNAT Studio
   • Description: An IDE by AdaCore for Ada and SPARK programming, with native Linux support.
   • Linux Roots: Designed for Linux and other Unix-like systems, often used in embedded and systems programming.
   • Features: Context-aware completion, integrated debugger, Git support, documentation generation.
   • Status: Niche but actively maintained for Ada developers on Linux.
6. Anjuta DevStudio
   • Description: A GNOME-based IDE for C, C++, and other languages, built for Linux developers.
   • Linux Roots: Tied to the GNOME ecosystem, installable via sudo apt install anjuta.
   • Features: Project management, GUI designer, interactive debugger, version control.
   • Status: Less active today (last major updates pre-2020), but still functional on older distros.

Older IDEsPermalink

These are legacy IDEs, many of which predate the AI era and were influential in their time. Some have Linux support, while others were more tied to other platforms but shaped IDE history.

1. Turbo Pascal
   • Description: A classic IDE from Borland for Pascal programming, popular in the 1980s and early 1990s.
   • Linux Relevance: Not natively Linux-based (ran on DOS), but emulated on Linux via DOSBox. Influenced modern IDE design.
   • Features: Integrated editor, compiler, and debugger in a single interface.
   • Status: Obsolete, but a historical benchmark for simplicity and speed.
2. Borland C++
   • Description: An early IDE for C++ development, released in the early 1990s by Borland.
   • Linux Relevance: DOS/Windows-centric, but runnable on Linux via emulation. A precursor to modern C++ IDEs like Code::Blocks.
   • Features: Syntax highlighting, built-in compiler, basic debugging.
   • Status: Discontinued, replaced by successors like Embarcadero C++ Builder.
3. JBuilder
   • Description: A Java IDE by Borland, prominent in the late 1990s and early 2000s.
   • Linux Relevance: Later versions had Linux support, though it was primarily Windows-focused.
   • Features: Visual design tools, code completion, project management.
   • Status: Faded after Borland’s decline; Eclipse and IntelliJ took its place.
4. Visual Basic (Pre-.NET)
   • Description: Microsoft’s IDE for BASIC programming, hugely popular in the 1990s.
   • Linux Relevance: Windows-only, but its drag-and-drop GUI design influenced cross-platform tools. Runnable on Linux via Wine in some cases.
   • Features: Rapid application development (RAD), form designer, integrated debugging.
   • Status: Superseded by Visual Studio, no native Linux version.
5. Dev-C++
   • Description: A lightweight C/C++ IDE for Windows, popular in the early 2000s, with a Linux port via wxWidgets.
   • Linux Relevance: Linux version (via SourceForge) was less polished but usable.
   • Features: Simple interface, GCC integration, basic project management.
   • Status: Original project stalled; revived as Orwell Dev-C++, but overshadowed by modern tools.
6. NetBeans (Early Versions)
   • Description: An open-source Java IDE launched in 1996, later acquired by Sun Microsystems.
   • Linux Relevance: Gained Linux support early on, growing with the open-source community.
   • Features: Java-focused initially, with code completion, debugging, and GUI builders.
   • Status: Evolved into Apache NetBeans, still active but early versions feel dated.