According to Andrej Karpathy, he published a one-page mirror of his MicroGPT write-up at karpathy.ai/microgpt.html, consolidating the minimal-from-scratch GPT tutorial and code for easier reading. As reported by Karpathy’s post, the resource distills a compact transformer implementation, training loop, and tokenizer basics, enabling practitioners to understand and reimplement GPT-class models with fewer dependencies. According to the MicroGPT page, this lowers onboarding friction for teams building lightweight language models, facilitating rapid prototyping, education, and debugging of inference and training pipelines. As noted by Karpathy, the single-page format mirrors the original gist for better accessibility, which can help startups and researchers validate custom LLM variants, optimize kernels, and benchmark small-scale GPTs before scaling.