Qodo AntiSlop scan found 8 issues across 7 recent PRs
Hey team,
A user recently scanned this repo using Qodo's AntiSlop Scanner. The analysis reviewed 7 recent PRs and found 8 issues, all confirmed to still exist on main.
Here's one example:
Per-batch memmap remapping adds overhead in training loop
Severity: action_required | Category: performance
train.py now opens and creates a new np.memmap inside get_batch() on every call. Since get_batch() is called in the inner training micro-step loop and during evaluation, this adds repeated file open/mmap syscalls per iteration.
How to validate: Profile get_batch() calls during training and observe the repeated np.memmap constructor overhead per batch.
Agent prompt to fix:
Move the
np.memmapcreation outside ofget_batch()so the memory-mapped arrays for train and val splits are opened once and reused across all batch fetches. Pass them as arguments or store them as module-level variables.
Other confirmed issues
| # | Title | Category | PR |
|---|---|---|---|
| 1 | CUDA backend flags set on non-CUDA devices (MPS, CPU) | reliability | #309 |
| 2 | Wrong device bf16 check before configurator override | correctness | #277 |
| 3 | Outdated memmap comment misleads on file paths | maintainability | #73 |
| 4 | shakespeare_char still uses platform default encoding | correctness | #429 |
3 additional findings (including suspected issues) are available in the full report.
Enjoy!
P.S. Qodo offers free tooling for open-source maintainers: https://www.qodo.ai/solutions/open-source/
cc @karpathy
Source: karpathy/nanoGPT