According to @karpathy, the nanochat d32 language model was recently taught to count occurrences of the letter 'r' in words like 'strawberry' using a new synthetic task called SpellingBee (source: github.com/karpathy/nanochat/discussions/164). This process involved generating diverse user queries and ideal assistant responses, then applying supervised fine-tuning (SFT) and reinforcement learning (RL) to instill this capability in the AI. Special attention was given to model-specific challenges such as prompt diversity, tokenization, and reasoning breakdown, especially for small models. The guide demonstrates how practical skills can be incrementally added to lightweight LLMs, highlighting opportunities for rapid capability expansion and custom task training in compact AI systems (source: @karpathy on Twitter).