AI IDEs
The AI IDE Explorer
Jan 15, 2025
I was deep into building a hiring platform with Angular and NestJS when I hit a wall. The task seemed straightforward enough: create a feature that lets recruiters select up to 5 applicants, compare their profiles, and generate a comprehensive PDF report with all the comparison data.
Simple, right? Wrong.
I spent two frustrating hours feeding files to ChatGPT and Claude, explaining my component structure over and over. The AI would generate clean, well-practiced code that looked perfect in isolation. But the moment I tried integrating it into my existing codebase? Chaos. Breaking changes everywhere. My carefully built application flow crumbled with each "helpful" suggestion.
That's when a developer friend mentioned Cursor.
"Just try it," he said. "It's different."
Different was an understatement. When I explained my task to Cursor, it immediately grasped the complexity. No lengthy explanations needed. No copying and pasting components—just drag and drop components to chat windows. It just understood my Angular services, my NestJS controllers, my frontend and backend architecture, and how they all connected.
One and a half hours later, I had my comparison feature working flawlessly. The same task that had me pulling my hair out for hours with traditional AI tools was done.
That moment changed how I think about AI-powered development. And it made me wonder: if Cursor could do this, what else was out there?
Let's explore other IDEs and see what they can do.
I selected these AI-IDE tools based on their visibility in developer communities, GitHub usage, tech blogs, and product rankings.
Then, I reviewed each tool's documentation, feature lists, and user feedback.
Finally, I compared them across key criteria (ease of setup, project context awareness, cross-file editing, debugging assistance, pricing), and ranked them based on how well they perform in real full-stack development tasks.
| # | IDE / Tool | Platform / Type | Free vs Paid | Key Features | Strengths / Advantages | Weaknesses / Trade-offs | Best Use Case / Remarks |
|---|---|---|---|---|---|---|---|
| 1 | Cursor | Desktop, AI-first editor (fork of VS Code) | Paid / freemium | Multi-file edits, project indexing, natural language instructions, "smart rewrite" | Deep context awareness, integrates front + back code, strong dev UX | Subscription cost; potential scaling / performance issues on large codebases | Good for full-stack projects where cross-file changes matter |
| 2 | Windsurf | AI code editor / IDE competitor to Cursor | (Likely paid / freemium) | AI-native editing, context awareness, focused on "vibe coding" (claims) | Designed to rival Cursor in developer flow | Less mature, unknown stability with complex codebases | Worth testing to compare against Cursor |
| 3 | GitHub Copilot | Plugin / extension in VS Code, JetBrains, etc. | Paid (with free trial / GitHub benefits) | Autocomplete, contextual code suggestions, multi-language support | Very mature, wide adoption, many integrations | Occasional incorrect suggestions, cost for heavy users | General purpose coding support in many languages |
| 4 | JetBrains AI Assistant | Inside JetBrains IDEs (IntelliJ, WebStorm, PyCharm, etc.) | Likely paid / subscription model | In-IDE AI features: code generation, refactoring, context awareness | Leverages JetBrains' strong tooling & ecosystem | Cost + plugin / integration overhead | Developers already using JetBrains tools |
| 5 | Replit Ghostwriter / Replit Agent | Browser / cloud IDE | Freemium / tiered pricing | AI chat + code generation in browser, live collaboration, cloud environment | Instant access, no setup, collaborative features | Dependent on internet, limits in heavy custom projects | Quick prototypes, small full-stack/side projects |
| 6 | Tabnine | Plugin / AI assistant added in existing IDEs | Has free forever with limited features; paid for full power | Contextual line & block completions, AI chat in IDE, extensible models | Works inside your favorite IDEs, privacy / customization options | Free mode limited; advanced features behind paywall | Good as "add-on" AI boost rather than full AI IDE replacement |
| 7 | Sourcegraph Cody / Amp | Intelligent assistant tied to codebase context | Freemium / subscription | Deep codebase queries, context-aware suggestions, cross-file insights | Useful for navigating large projects, code search + assist | Might lag on huge repos, cost for full features | Big projects where code navigation + assistance matters |
| 8 | Continue.dev | Extensions, CLI, agent-style architecture | Free / open / modular (you choose model) | Custom agents, chat, modifications, choice of backend models | Flexible, control, no vendor lock-in | Requires more setup, less polished in some parts | Experimenters, people who want to build their own AI workflows |
| 9 | Amazon CodeWhisperer | AWS environment / coding assistant in IDEs | Has free / basic tiers under AWS | Code suggestions, AWS service integrations, security guardrails | Strong if your stack is AWS-heavy | Less suited if you're outside AWS ecosystem | Useful in cloud / serverless / AWS projects |
| 10 | Codeium | AI assistant / IDE integration | Freemium / free tier | Autocomplete, AI code suggestions, chat inside editor | Lightweight, accessible | Feature limits in free tier | Good for developers wanting extra code help without heavy cost |
If you want a different perspective or want to validate these tools against another expert's testing, check out this article: "8 Best AI Coding Tools: Tested & Compared" by n8n. It includes hands-on comparisons and evaluation criteria that complement what I'm doing here.
Generative AI models—like GPT, Claude, or others—are trained on massive amounts of data (text, code, etc.). They learn patterns, structures, and relationships in that data so they can predict what comes next or generate new content.
When you ask a prompt like "write a function to parse JSON in TypeScript," the model uses its learned knowledge to generate code that matches patterns it saw during training. You provide the prompt + context, the model responds, and then you integrate that output manually into your project.
Generative AI is reactive — it waits for your prompt, then produces something based on that prompt and whatever context it has. Its "understanding" is statistical and pattern-based, not a true model of your entire project.
When an AI model is integrated with an IDE (or AI-first editor), the model doesn't work in isolation—it becomes part of your development environment. The IDE gives the model access (with safeguards) to your project context: file structure, modules, interdependencies, existing code, and state.
So, rather than you having to paste code + context into a prompt, the IDE can already "see" parts of your project and use that context to guide suggestions, edits, refactorings, or multi-file changes.
In effect, the IDE acts as a middle layer: you give high-level instructions (e.g. "refactor this feature," "apply compare logic across modules"), and the integrated AI carries out actions across relevant files, suggests fixes inline, and maintains coherence with the architecture you already have.
| Aspect | Working with Generative AI (ChatGPT, Claude, etc.) | Working with AI Inside an IDE / AI-IDE |
|---|---|---|
| Context Provision | You must manually paste code, explain architecture, include dependencies | The IDE already has your project context, file structure, module dependencies |
| Integration Effort | You get code output that you must integrate manually (merge, refactor) | The IDE can apply changes directly across files, refactor, propagate modifications |
| Workflow Friction | You switch between chat UI / prompt interface ↔ your code editor | Everything happens inside your coding environment; minimal window switching |
| Error Handling & Validation | You test the output, debug and re-prompt if errors occur | The IDE may run static analysis, catch errors, suggest fixes inline |
| Task Scope | Usually limited to one function, snippet, small module per prompt | Can execute multi-step tasks, cross-file changes, higher-level features |
| User Control & Trust | High manual control — you see prompt, output, you decide | More automation — you need trust and strong undo / rollback options |
| Best Use Cases | Experimentation, prototyping, asking "how to solve X" | Real development work, production features, refactoring, code maintenance |
Cursor's "Agent" lets you issue natural language commands like running terminal actions, creating or modifying files, doing semantic code search, and orchestrating cross-module edits. You don't just get suggestions — you can tell Cursor to do something in your codebase.
Define project-specific AI rules or style preferences inside .cursorrules files or via Notepads. You can bias how Cursor writes, comments, or structures your code.
Cursor can scan your code (or your recent changes) for potential bugs, assign confidence levels, and propose fixes inline.
Use @Web within your prompt to let Cursor fetch information from the web (documentation, patterns) to enhance suggestions.
Ask Cursor to make targeted edits across files with a single natural language request. It can rewrite logic spanning multiple files according to your instructions.
For more info read: Builder.io Cursor Advanced Features | Cursor Features
Cody connects to Sourcegraph's powerful search engine to fetch context not just from your open files, but across your entire repository and even remote repos. That means suggestions can reflect knowledge from all parts of your code.
After interacting in chat, you can convert suggestions into diffs and apply them directly inline — no manual copy-paste.
Cody can suggest generating unit tests for methods or components, anticipating edge cases.
Use Cody inside VS Code, JetBrains IDEs, and more. The assistant works in your preferred environment without you losing features.
Tabnine adapts to your own code patterns — not just generic training data — so its suggestions feel more in line with your style or your team's code conventions.
For teams concerned about code confidentiality, Tabnine allows using private deployment or local instances so your code never leaves your environment.
It supports a broad range of languages and integrates into many editor / IDE setups (VS Code, IntelliJ, etc.), making it versatile in hybrid tech stacks.
Because it lives inside JetBrains IDEs (IntelliJ, PyCharm, WebStorm), the AI assistant can tap into the full power of the IDE: refactoring tools, code inspections, project structure awareness, and built-in diagnostics.
The assistant can generate code comments, docstrings, and explanations for code blocks, helping maintain readability.
Some versions include options to run models locally or restrict external usage, improving privacy for enterprise projects.
CodeWhisperer checks the suggestions it gives against security best practices, flagging potential issues or disallowed code patterns.
It highlights if generated code overlaps with open-source code, showing references or license sources to help you avoid copyright violations.
Since it's built by Amazon, it often understands AWS services deeply and can suggest architecture patterns, API calls, or Lambda / IAM configurations intelligently.
You can opt out of telemetry or data collection to limit what's shared back to Amazon's servers.
When you adopt AI-powered IDEs, there's huge upside — but also risks. Keep these in mind:
If you let AI do too much, you might lose deep understanding of your codebase, making debugging and maintenance harder.
AI suggestions can introduce flaws: insecure defaults, missing input validation, outdated dependencies. A study found many AI-generated snippets had serious security weaknesses.
Generated code may inadvertently mirror open-source or proprietary code with restrictive licenses. That can bring legal trouble.
Even AI inside IDEs can misunderstand your architecture, prompt wrong edits, or break existing flow. Context is hard, especially in large or legacy codebases.
If the AI tool allows user input in prompts (or reads from web/external sources), malicious or misleading prompts could change behavior unintentionally.
The AI may do things "magically" that you don't fully understand or control. Without undo / audit options, wrong changes can be hard to reverse.
Running complex models or large context may slow down your IDE. Also, AI features might be disabled for very big projects or files.
Letting the AI access project files, proprietary code, credentials, etc., carries risk. Telemetry, access control, and data governance are important.
This generation is calling it vibe coding — you speak your intention, and the AI + IDE combo brings it to life. Now, imagine that not just with prompts, but inside your coding environment.
With AI seamlessly integrated into your IDE, vibe coding becomes more than a buzzword — it becomes your workflow. Instead of writing lines by hand, you guide, steer, and let the tools handle the plumbing:
You say, "Build the applicant comparison module," and the IDE + AI tool maps that into your project: creating service files, adjusting modules, wiring up APIs.
You ask, "Refactor this logic across all files to use a common utility," and the IDE applies it across controllers, models, and views — all with minimal fuss.
You prompt, "Add caching layer with fallback strategy," and it injects code, integrates configurations, and keeps your structure intact.
In this mode, your job becomes more creative direction and reviewer of ideas than typing every function. You watch the AI grow your feature, then you tweak, refine, and polish.
It's fluid — you stay in your IDE, navigate your code, ask for features by name, and the tool responds. Productivity flows. Mental friction drops. You build fast, experiment boldly, and shape architecture with commands instead of keystrokes.
This is vibe coding with an IDE — where intention becomes implementation, in your environment, without switching contexts.
We've explored how generative AI differs from AI embedded in IDEs, the real benefits of coding inside those environments, and uncovered hidden features and pitfalls — all through the lens of evolving vibe coding.
Choose one AI IDE and test a small feature yourself
Always read and understand every line of code it generates
Rigorously test the generated code in your environment
Don't let AI write your future — treat it as a powerful assistant, not a replacement
