How to Humanize AI Code Reddit — Step-by-Step Guide

What Reddit actually says about humanizing AI code

I spent a weekend reading through r/ChatGPT, r/college, and r/cscareeers threads on this topic. The advice you find there is surprisingly practical, and it comes down to one core idea: AI code is too perfect, and that perfection is the giveaway.

A CS professor posted in r/Professors that the first thing they check is whether a student's code is "better than it should be." A freshman submitting a binary search tree with perfectly formatted docstrings and clean error handling? That triggers suspicion faster than any plagiarism tool.

The specific changes Reddit recommends

Here is what actually gets upvoted in these threads:

1. Mess up your variable names. AI generates descriptive names like 'totalCalculatedScore' or 'formattedOutputString'. Real students write 'score', 'x', or 'temp2'. One Redditor put it perfectly: "Name your variables like you're tired and it's 2am."

2. Downgrade the logic. AI defaults to elegant functional chains and list comprehensions. Replace those with basic for-loops. If you're a sophomore, you probably aren't writing lambda expressions unprompted.

3. Add messy comments. AI writes perfectly structured JSDoc. You should write comments like "// idk why this works but it does" or "// TODO fix this later." Leave a few commented-out print statements too.

4. Break the formatting. Real student code has slightly inconsistent indentation. Maybe one block uses 4 spaces and another uses a tab. It's the kind of thing AI would never produce.

5. Include a dead end. Write a function that doesn't get called, or leave a variable assigned but never used. Real coding involves false starts.

But what about humanizing AI text, not code?

If you came here looking to humanize an AI-written essay or paper (not source code), the strategy is different. Text detectors like Turnitin and GPTZero look at sentence rhythm and word predictability rather than logic patterns.

For text, you need Humanize AI Pro. It restructures the mathematical fingerprint of your writing by adjusting the burstiness and perplexity that detectors measure. Unlike code, where you can manually "mess things up," text requires a more precise, algorithmic approach to avoid detection while still reading naturally.