diff --git a/README.md b/README.md
index b6c6128..d3dea87 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,8 @@ LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPytho
 
 The best introduction to the llm.c repo today is reproducing the GPT-2 (124M) model. [Discussion #481](https://github.com/karpathy/llm.c/discussions/481) steps through this in detail. We can reproduce other models from the GPT-2 and GPT-3 series in both llm.c and in the parallel implementation of PyTorch. Have a look at the [scripts README](scripts/README.md).
 
+debugging tip: when you run the `make` command to build the binary, modify it by replacing `-O3` with `-g` so you can step through the code in your favorite IDE (e.g. vscode).
+
 ## quick start (1 GPU, fp32 only)
 
 If you won't be training on multiple nodes, aren't interested in mixed precision, and are interested in learning CUDA, the fp32 (legacy) files might be of interest to you. These are files that were "checkpointed" early in the history of llm.c and frozen in time. They are simpler, more portable, and possibly easier to understand. Run the 1 GPU, fp32 code like this: