AI IDEs vs. Chat Apps: Developers Weigh In on Coding Efficiency and Context Management

The Hacker News discussion revolves around a common dilemma for developers leveraging AI: are dedicated AI IDEs significantly better than the established workflow of copy-pasting code into powerful chat applications like ChatGPT or Gemini? The original poster highlights the tedium of manually providing context to chat apps for large projects, a key selling point for AI IDEs that promise to understand the entire codebase. However, concerns about the intelligence of embedded models (like early Copilot versions) versus standalone chat models, and the often higher or pay-per-use cost of AI IDEs, fuel the debate.

The Context Conundrum

A central theme is context management. Users find that AI IDEs like Cursor, Windsurf, or integrated solutions in VSCode/JetBrains aim to solve the context problem by indexing the codebase. However, experiences vary. Some find these tools adept at multi-file operations and refactoring, while others note they still need to manually guide the AI (e.g., using @mentions in Cursor) or that the automated context isn't always perfect. As one user put it, for gptel in Emacs, "Context management is really central to my workflow, I manage it like a hawk."

Conversely, manually copy-pasting into chat apps gives precise control over context but is time-consuming. Several users shared workarounds, from command-line tricks (cat file.js | xclip -sel clip) to helper websites (files2prompt.com) and custom plugins to streamline this process.

Cost vs. Capability

Cost is a major factor. Chat app subscriptions (e.g., ChatGPT Plus) offer effectively unlimited prompts with powerful models. AI IDEs or specialized tools like Claude Code can become expensive, with some users reporting costs like "$3-5 per session" for Claude Code. However, others argue that even premium AI IDE subscriptions (e.g., Cursor at ~$20/month) are "very affordable compared to professional developer wages."

Many seek a balance: * Using free tiers or models (like Gemini 2.5 Pro in Google AI Studio). * Opting for tools that allow bringing your own API key (e.g., Roo Code, Cline), thus paying directly for model usage, which can be cheaper for some workloads or provide access to preferred models. * Strategically using different models within tools like Cursor, reserving expensive, powerful models for complex tasks and unmetered or cheaper models for boilerplate or simpler requests.

Workflow and Tooling Landscape

The discussion highlights a diverse tooling landscape and varied workflows:

• Dedicated AI IDEs (e.g., Cursor, Windsurf): Praised for features like multi-file edits, codebase indexing, and sometimes superior autocomplete (Cursor's Supermaven). Criticized for occasional 'dumbness', UI glitches (Windsurf), or disruptive autocomplete.
• IDE Plugins (e.g., GitHub Copilot, JetBrains AI, gptel for Emacs, various VSCode extensions): Offer integration within familiar environments. Copilot's paid tier is noted for providing access to stronger models and better context awareness. gptel users appreciate its in-editor chat and context control.
• CLI Tools (e.g., aider-chat, Claude Code, Cline, Roo Code): Favored by developers comfortable with the command line. Aider-chat is lauded for its effectiveness once its workflow is learned, particularly with Python. Claude Code is seen as powerful for whole-codebase tasks. Cline and Roo Code offer flexibility with model choice and cost via API keys.
• Hybrid Approaches: Many developers use a combination. For example, using ChatGPT for high-level problem-solving or generating precise prompts, then using Cursor or an IDE plugin for implementation and refactoring. One user described using ChatGPT-4o for tough problems and Cursor for "glorified multi-file autocomplete."
• Manual Context + Chat Apps: Still a strong contender for those prioritizing model intelligence and control, despite the copy-paste overhead. Tools like Simon Willison's llm or files2prompt.com aid this.

Key User Strategies and Insights

Several productive tips and arguments emerged:

• Iterate in Small Chunks: Especially with tools like aider-chat, breaking down tasks makes AI assistance more effective.
• Prompt Engineering: Even with context-aware IDEs, the quality of prompts matters. Some use one AI (e.g., ChatGPT-4o) to generate precise prompts for another (e.g., Cursor).
• Understand the Tool's Strengths: Different tools excel at different tasks. Agentic features might be good for exploration or well-defined tasks but can fail on complex or novel problems. Autocomplete is good for boilerplate but can hinder deep thinking or learning.
• Verify and Control: Many emphasize the need to review AI-generated code. JetBrains' "compare with clipboard" feature was highlighted as essential for verifying changes from an LLM.
• Security and Privacy: A user raised concerns about feeding proprietary code to third-party AI services, a critical consideration for many businesses.
• Learning Curve: Some tools, like aider-chat, have a learning curve but offer significant benefits once mastered.
• Managing Long Conversations: One user suggested a "commit-like" workflow for ChatGPT: get final code, summarize, then start a new chat with the summary and code to maintain performance.

Ultimately, the discussion shows that while AI IDEs offer compelling advantages in terms of workflow integration and context awareness, they are not yet a definitive replacement for chat-based interactions for all tasks or all developers. The field is rapidly evolving, and many users are finding value in experimenting with a mix of tools and techniques to best suit their individual needs and coding styles.