According to @karpathy, nanochat now carries a defined identity and can state its capabilities, including that it is nanochat d32 built by him with a reported $800 cost and weaker non-English proficiency, achieved via synthetic data generation, source: x.com/karpathy/status/1980508380860150038. He released an example script that demonstrates generating diverse synthetic conversations and mixing them into mid-training or SFT, stressing the importance of entropy to avoid repetitive datasets, source: x.com/karpathy/status/1980508380860150038. He adds that base LLMs lack inherent personality or self-knowledge and require explicitly bolted-on traits via curated synthetic data, source: x.com/karpathy/status/1980508380860150038. For traders, the disclosed $800 customization benchmark and open-source workflow provide concrete cost and process reference points for evaluating open-source AI agent development and adoption paths across AI-linked tokens and AI-exposed equities, source: twitter.com/karpathy/status/1980665134415802554.