Writing Fast, Reproducible, Attractive Code (For AI Research) (akshit2003.substack.com)

A researcher lays out a practical playbook for making AI research code fast to start, easy to reproduce, and attractive to other developers—arguing that readable, runnable repos are now essential as papers go viral and AI coding agents make others likely to clone and reuse your work. The piece stresses that lasting impact requires more than novel ideas: minimal setup, one-line experiment runs, and clear modular structure turn papers into usable baselines that the community can build on. Key technical recommendations: replace fragile conda/venv workflows with the uv package manager to auto-install exact package versions and eliminate “works on my laptop” issues; use jsonargparse with dataclasses for hierarchical, reproducible command-line config and run multi‑experiment sweeps with shell loops/TMUX; prefer a single orchestration entrypoint (main.py) with well-separated utilities, class-based models, and base-class APIs (e.g., unified unlearn()/train()/test() methods) so new methods plug in easily. Use Python logging (not print) and WandB for persistent metrics/tracking, test thoroughly before release, and leverage AI coding agents to scaffold infrastructure. These practices reduce onboarding friction, improve reproducibility, and increase the likelihood your code becomes a community standard or baseline.