https://claude.ai/chat/16c21a60-fb14-42a1-8582-f231af213fcc

Your Debugging Approach Assessment

What You're Doing Well ✓

Print/Log Debugging: This is actually one of the most practical debugging techniques! You're directly observing program state, which is fundamental.

Using AI Assistance: Smart move - AI models can spot patterns, suggest hypotheses, and save time on syntax issues.

Potential Gaps to Consider

1. Systematic Problem Isolation

Before adding logs everywhere, do you:

• Reproduce the bug reliably?
• Identify the smallest input that triggers it?
• Know what the expected vs. actual behavior is?

2. Strategic Log Placement

Random logging can be noisy. Consider:

• Binary search approach: add logs at the midpoint of suspected code
• Log at boundaries: function entry/exit, before/after key operations
• Log the right data: inputs, outputs, state changes, assumptions

3. Using Debuggers

Print statements are great, but debuggers let you:

• Pause execution and inspect everything
• Step through code line by line
• Set conditional breakpoints
• Avoid cluttering code with temporary prints

4. Reading Error Messages Carefully

Stack traces tell you exactly where things failed. Do you:

• Read the full error message (not just the type)?
• Trace back through the stack to find your code?
• Look up unfamiliar error types?

5. Hypothesis-Driven Debugging

Instead of random logging, try:

1. Form a hypothesis about what's wrong
2. Design a test to confirm/reject it
3. Update your mental model based on results
4. Repeat

How to Better Use AI Models

When asking DeepSeek/Doubao for help, give them:

• The error message (complete stack trace)
• Minimal reproducible code (not your whole codebase)
• What you've already tried
• Your hypothesis about the cause

Instead of: "Why doesn't this work?" Try: "I'm getting X error at line Y. I think it might be because Z. Here's the relevant code: ..."

Quick Debugging Checklist

Before diving into logs:

•  Can I reproduce it consistently?
•  What's the simplest case that breaks?
•  What's the error message telling me?
•  Where was it last working?
•  What changed since then?

Tools Worth Learning

• Python: pdb, breakpoint(), logging module
• JavaScript: browser DevTools, console methods beyond log
• IDE debuggers: VS Code, PyCharm have excellent visual debuggers
• Git bisect: find which commit introduced the bug

Next Steps

Pick ONE thing to try next time you debug:

1. Use an actual debugger instead of print statements
2. Write down your hypothesis before adding logs
3. Try binary search logging (midpoint method)
4. Read the full stack trace carefully before asking AI

Your current approach isn't wrong - it's just the beginning. These additions will make you much more efficient!