Choosing Your AI Co-Pilot: A Deep Dive into Cursor, Cline, LLM Quirks, and Environment Setups
Selecting the right AI development environment and understanding the nuances of the Large Language Models (LLMs) powering them are critical for a productive partnership. My journey, documented in posts about AI’s evolving landscape, crafting rules, and task management, has involved extensive experimentation with tools like Cursor and various LLMs. Here’s a deeper dive into those experiences.
Cursor: A Powerful Editor with Its Own Set of Puzzles
Cursor has been a central part of my recent workflow, but it’s not without its specific behaviors and limitations:
- Task and Mode Management: A significant hurdle is Cursor’s current lack of support for initiating subtasks in different modes or a
/new_tasktool akin to what some other platforms offer. This can lead to context becoming cluttered in longer, multifaceted interactions. - Context Features: While features like
@docs(which often relies on a JSONL file of crawled documentation) exist, their utility can diminish with newer, more context-aware LLMs that can often find information more dynamically. Similarly, for@lint-errors, I often find git hooks a more robust solution for managing linting, especially since not all critical linters (like API key leakage detectors) are supported by default. - Configuration Management: Keeping
.cursor/mcp.jsonversion-controlled became easier with tools like envmcp, which helps resolve local environment variables. However, the dream of a “Keep It Simple, Stupid” (KISS) approach tomcp.jsonfiles often failed, as linked files sometimes required editor restarts after changes. Furthermore, environment loading differs between agents;envmcpworks well for Cursor, while a tool likemcp-safe-runmight be needed for Roo. This means mixing configurations isn’t straightforward. - Rule System Adaptation: Converting memory bank concepts from systems like Cline or Roo to Cursor’s specific rule types was a learning curve. Initially, feeding file paths or rule names to the AI, hoping it would fetch them, proved ineffective. The most reliable method I found was setting internal variables within the rules themselves.
- Date and Time in Rules: Cursor rules don’t natively support dynamic date/time interpolation (a known forum topic). Workarounds involve feeding it via system prompt (not always controllable), using a terminal command, or an MCP call—the latter adding overhead.
As tools like Roo advanced, my reliance on Cline diminished, eventually leading to its removal from my active setup.
The LLM Lottery: Performance and Peculiarities
The underlying LLM plays a massive role:
- The Gemini 2.5 Pro Impact: The release of Gemini 2.5 Pro (UPD: specifically the
05-06version I’ve been using) was a game-changer. Its ability to generate decent code for a Pacman MVP with reasonable decisions and clean output was impressive, far surpassing earlier Gemini versions. This leap in capability also meant that some tools and approaches, like the original design ofcursor-memory-bank, felt somewhat outdated or less necessary. - Claude’s Curious Case: Working with Claude models (like 3.7 Sonnet via Cursor) revealed some… interesting quirks:
- Cutoff Date Confusion: Officially, Claude 3.7 Sonnet’s cutoff was stated as October 2024. However, when directly queried in Cursor without tool use, it would claim April 2023! This discrepancy also manifested in inconsistent date generation when asked to format timestamps.
- The Numbered List Nemesis: I discovered (the hard way, after my Cursor subscription briefly lapsed at a critical point!) that Claude models seem to have a peculiar interaction with numbered lists. They appear to recognize the structure and then sometimes “cut corners” or jump ahead, even when explicitly instructed not to. The AI might even apologize for skipping steps it was told to follow! This was a stark lesson, reminiscent of why projects like Plandex heavily prohibit numbered lists in their system prompts.
These experiences highlight that even with sophisticated tools, understanding the specific LLM’s strengths, weaknesses, and downright odd behaviors is crucial for effective prompt engineering and reliable outcomes. The journey is one of constant adaptation and learning the “personality” of your AI co-pilot.