The error was a classic: TypeError: Cannot read properties of undefined (reading 'map')
.
The weird part? The AI kept insisting the function was there. It would literally write the code, run the build, hit the error, and then respond with "I have fixed the issue" while providing the exact same broken code. I was stuck in a loop. My terminal was screaming at me, and the LLM was gaslighting me into thinking I had deleted a file I never created.
The moment the loop broke
I stopped prompting and looked at the context window. Roo Code had indexed my project, but it was pulling from a stale cached version of a different branch I'd worked on two days prior. It wasn't that the AI was "stupid"—it was that its context was polluted.
I tried a generic "check your files" prompt. Failed. Then I tried a more aggressive approach. I explicitly told it to run ls -R
on the directory and read the actual file content of the utility folder before writing another line of code.
The result was immediate. Roo Code realized the function formatApiResponse
was actually named cleanApiResponse
in the current branch.
The Fix:
-
Forced a directory listing via the terminal tool.
-
Used the
read_file
tool to verify the actual export names.
- Corrected the import statement.
It took 30 seconds to fix once I stopped trusting the AI's "memory" and forced it to use its "eyes" (the terminal tools).
Why Roo Code hits different than Cursor
People keep asking me why I bother with Roo Code when Cursor exists. Here is the raw truth: Cursor is a polished product; Roo Code is a power tool.
Cursor feels like a smart editor. Roo Code feels like a junior engineer who has full access to your shell. The ability to let the agent execute commands, read the output, and pivot based on the error message is where the magic happens. But that power is a double-edged sword. If you don't guide the agent, it will confidently walk you off a cliff.
I put together this quick comparison because the "vibe" is totally different depending on the task:
| Feature | Cursor | Roo Code |
| :--- | :--- | :--- | | Primary Feel | IDE with AI built-in | Agent with IDE access |
| Context Handling | RAG-based indexing | Tool-based exploration (ls, grep, read) | | Autonomy | Suggests changes → You accept | Proposes action → Runs → Validates |
| **Risk Factor** | Low (mostly code suggestions) | Medium (can run `rm -rf`
if you're reckless) |
| Configuration | Simple settings | Heavy prompt customization (Custom Instructions) |
Stop treating it like a chatbot
The biggest mistake I see people make is treating the Roo Code interface like a ChatGPT window. If you just say "Fix my bug," you're gambling.
The secret to a proper Roo Code guide isn't in the settings menu; it's in how you structure your instructions. I've started using a "Constraint-First" approach. Instead of "Build a login page," I use:"Build a login page. Constraints: Use Tailwind for styling, no external UI libraries, ensure the Zod validation handles email edge cases, and do NOT modify the auth-provider.ts file unless I explicitly tell you to."
When you give it boundaries, the hallucinations drop significantly. It stops trying to be "helpful" by rewriting your entire architecture and starts actually solving the ticket.
The struggle with token burn
Let's talk about the cost. Using Claude 3.5 Sonnet via OpenRouter with Roo Code is an absolute dream for logic, but it eats tokens for breakfast. Because Roo Code sends the full context and tool outputs back and forth, a single complex refactor can burn through $2.00 in API credits in twenty minutes.
I found that the "Context Window" is the real bottleneck. When the conversation gets too long, the agent starts forgetting the constraints I set at the beginning.
My workflow now is:
-
Start a task.
-
Once the feature is working, wipe the session.
-
Start a new session and provide the current state of the code.
It feels tedious, but it prevents that "looping" behavior where the AI starts apologizing for mistakes it made ten prompts ago. If you're diving into AI Coding, you'll realize that managing the LLM's attention is just as important as writing the code itself.
Setting up the "God Mode" Custom Instructions
If you want Roo Code to actually be productive, you have to tweak the system prompt. By default, it's too polite. I added a section to my custom instructions that basically tells it: "Be concise. If you see a better way to do something, suggest it briefly, but don't rewrite the file unless it's necessary for the fix. Always verify file existence before assuming a path." This one change reduced my "file not found" errors by about 40%.
Where to actually get help
The wild thing about this space is that the documentation can't keep up. By the time a "guide" is published, the extension has updated three times and the optimal prompt has changed.
This is why I spent more time in the PromptCube community than reading official docs. When I hit a wall with MCP (Model Context Protocol) servers—which Roo Code supports and is honestly a game-changer for connecting your AI to local databases—I didn't find the answer in a manual. I found it in a thread where someone shared the exact JSON config for their Postgres MCP server.
Joining a community like PromptCube isn't about finding "the best prompt." It's about finding the people who are breaking the tools in the same way you are. Whether you're trying to optimize your .clinerules
file or trying to figure out why your agent is stuck in a loop, having a peer group of developers who actually use these tools in production is the only way to stay current.
You can join the community to swap config files, compare model latency, or just rant about why a specific update broke your workflow. It's basically a collective brain for people who are tired of guessing why their AI agent is acting up.
Final thought on the "AI Engineer" workflow
We are moving away from "writing code" and toward "reviewing intent."
With Roo Code, my job has shifted from typing export const...
to auditing the agent's plan. I spend 10% of my time prompting and 90% of my time reading the diffs to make sure the AI hasn't introduced a subtle memory leak or a security flaw.
It's a different kind of mental fatigue, but it's infinitely faster than the old way. Just remember: the agent is only as good as the terminal output it can see. If it's failing, give it a better tool or a clearer path. Don't just keep hitting "Retry."
Next Stop overpaying for ChatGPT Pro if you're just learning to code →
a guide to making money with AI, with plenty of directly applicable cases.
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