If you’ve ever tried launching a GameCube game through Dolphin and been greeted with the dreaded “GC IPL file could not be found” error, you’re not alone. This issue can be frustrating, especially when everything else seems to be set up correctly. But don’t worry—there’s a simple fix, and we’ll walk you through it.
🧩 What Causes the GC IPL Error?
The error typically stems from a missing or incorrect BIOS file (also known as the IPL file) required for the GameCube boot animation. While the game itself may be perfectly fine, Dolphin attempts to load the BIOS sequence before launching the game—and if it can’t find the right file, it throws an error.
✅ Fixing the Error in Dolphin (Standalone)
If you’re running Dolphin directly (not through RetroBat), you can bypass the BIOS boot sequence entirely by tweaking a simple setting:
Locate your dolphin.ini configuration file.
Open it in a text editor.
Find the line that says SkipIPL.
Set it to True.
ini
[Core]
SkipIPL = True
This tells Dolphin to skip the BIOS animation and jump straight into the game—no IPL file needed.
🔄 Fixing the Error in Dolphin via RetroBat
If you’re using RetroBat as your frontend, the fix is slightly different. RetroBat tends to overwrite Dolphin’s configuration files each time you launch a game, so editing dolphin.ini manually won’t stick.
Instead, you need to configure RetroBat itself to skip the BIOS:
Launch RetroBat and press Start to open the Main Menu.
Navigate to: Game Settings > Per System Advanced Configuration
Select the console you’re working with (e.g., GameCube).
Go to: Emulation > Skip Bios
Set it to Yes.
This ensures that RetroBat tells Dolphin to skip the IPL sequence every time, avoiding the error altogether.
🎮 Final Thoughts
The GC IPL error might seem like a showstopper, but it’s really just a BIOS boot hiccup. Whether you’re using Dolphin standalone or through RetroBat, skipping the IPL sequence is a quick and effective workaround. Now you can get back to what matters—playing your favorite GameCube classics without interruption.
Got other emulation quirks you’re trying to solve? Drop them in the comments or reach out—I’m always up for a good retro tech fix.
In large projects, subtle anti-patterns can slip through reviews—like importing modules mid-file or conditionally. These non-standard import placements obscure dependencies, make static analysis unreliable, and lead to unpredictable runtime errors. This web article dives into that practice, outlines a broader set of coding bad practices, and even provides a ready-to-use AI coding agent prompt to catch every issue across your codebase.
What Is Non-Standard Import Placement?
Imports or require statements buried inside functions, conditional branches, or midway through a file violate expectations of where dependencies live. Best practices and most style guides mandate that:
All imports sit at the top of the file, immediately after any module docstring or comments.
Conditional or lazy loading only happens with clear justification and documentation.
When imports are scattered:
Static analysis tools can’t reliably determine your project’s dependency graph.
Developers hunting for missing or outdated modules lose time tracing hidden import logic.
You risk circular dependencies, initialization bugs, or runtime surprises.
A Broader List of Coding Bad Practices
Below is a table of widespread anti-patterns—some classic hygiene issues and others that modern AI agents might inject or overlook:
Bad Practice
Description
Spaghetti Code
Code with no clear structure making maintenance difficult.
Hardcoding Values
Embedding constants directly instead of using config or constants.
Magic Numbers/Strings
Using unexplained literals instead of named constants.
Global State Abuse
Overusing global variables causing unpredictable side effects.
Poor Naming Conventions
Using vague or misleading variable and function names.
Lack of Modularity
Writing large monolithic blocks instead of reusable functions.
Copy-Paste Programming
Duplicating code rather than abstracting shared logic.
No Error Handling
Ignoring exceptions or failing to validate inputs.
Overengineering
Adding unnecessary complexity or abstraction.
Under-documentation
Failing to comment or explain non-obvious logic.
Tight Coupling
Making modules overly dependent on each other.
Ignoring Style Guides
Not following language-specific conventions or style guides.
Dead Code
Leaving unused or unreachable code paths in the codebase.
Inconsistent Formatting
Mixing indentation styles or inconsistent code layout.
Placing imports mid-file or conditionally instead of at the top.
Missing Security Checks
Omitting authentication, authorization, or input sanitization.
Inefficient Algorithms
Using suboptimal logic that hurts performance.
Hallucinated Dependencies
Referencing non-existent libraries or methods from AI suggestions.
Incomplete Code Generation
Leaving functions or loops unfinished due to AI cutoffs.
Prompt-biased Solutions
Generating code that only fits the prompt and fails general cases.
Missing Corner Cases
Overlooking edge cases and error conditions in logic.
Incorrect Error Messages
Providing vague or misleading error feedback to users.
Logging Sensitive Data
Writing confidential information to logs without sanitization.
Violating SOLID Principles
Breaking single responsibility or open/closed design rules.
Race Conditions
Failing to handle concurrency leading to unpredictable bugs.
Crafting an AI Coding Agent Prompt
To ensure an AI auditor doesn’t skip files, ignore edge cases, or take shortcuts, use the following prompt. It instructs the agent to comprehensively scan every line, record each finding, and tally occurrences of every bad practice.
## Prompt
You are an expert AI code auditor. Your mission is to exhaustively scan every file and line of the codebase and uncover all instances of known bad practices. Do not skip or shortcut any part of the project, even if the code is large or complex. Report every finding with precise details and clear remediation guidance.
## Scope
- Analyze every source file, configuration, script, and module.
- Treat all code as in-scope; do not assume any file is irrelevant.
## Bad Practices to Detect
- Spaghetti Code
- Hardcoding Values
- Magic Numbers/Strings
- Global State Abuse
- Poor Naming Conventions
- Lack of Modularity
- Copy-Paste Programming
- No Error Handling
- Overengineering
- Under-documentation
- Tight Coupling
- Ignoring Style Guides
- Dead Code
- Inconsistent Formatting
- Improper Version Control Usage
- Non-standard Import Placement
- Missing Security Checks
- Inefficient Algorithms
- Hallucinated Dependencies
- Incomplete Code Generation
- Prompt-biased Solutions
- Missing Corner Cases
- Incorrect Error Messages
- Logging Sensitive Data
- Violating SOLID Principles
- Race Conditions
## Analysis Instructions
1. Traverse the entire directory tree and open every file.
2. Inspect every line—do not skip blank or comment lines.
3. Identify code snippets matching any bad practice.
4. For each instance, document:
- File path
- Line number(s)
- Exact snippet
- Bad practice name
- Explanation of why it’s problematic
- Suggested refactoring
5. Keep a running tally of occurrences per bad practice.
## Output Requirements
- Use Markdown with a section per file.
- Subheadings for each issue.
- End with a summary table listing each bad practice and its total count.
- If the repo is too large, process in ordered batches (e.g., by folder), confirming coverage before proceeding.
- Do not conclude until every file has been reviewed.
Begin the full project audit now, acknowledging you will not take shortcuts.
Next Steps
Integrate this prompt into your AI workflow or CI pipeline.
Pair it with linters and static analyzers (ESLint, Flake8, Prettier) for automated, real-time checks.
Enforce code review policies that catch both human and AI-introduced anti-patterns.
By combining clear style guidelines, automated linting, and an uncompromising AI audit prompt, you’ll dramatically improve code quality, maintainability, and security—project-wide.
Tidbytez 