According to Andrej Karpathy, the latest Nanochat miniseries v1 demonstrates that optimizing large language models (LLMs) should focus on a family of models, adjustable via compute allocation, rather than a single fixed model. This approach leverages robust scaling laws to ensure predictable, monotonically improving results as more compute is invested, similar to findings in the Chinchilla paper (source: @karpathy, Jan 7, 2026). Karpathy's public release of Nanochat features an end-to-end LLM pipeline, showcasing experiments where model and token scaling adhered closely to theoretical expectations, with a constant relating model size to training horizons. Benchmarking the Nanochat miniseries against GPT-2 and GPT-3 using the CORE score (from the DCLM paper) provides objective validation and demonstrates the potential for cost-effective, compute-optimal model training (source: @karpathy, Jan 7, 2026). This methodology allows AI startups and enterprises to confidently budget for and deploy scalable LLMs, reducing risk and optimizing investment in AI infrastructure.

According to @jimfan_42, the AI community is actively investigating the ability of embodied AI systems to tackle long-horizon, complex, human-centric tasks, highlighting both recent milestones and current limitations. Research focuses on efficiently combining low-level control algorithms with high-level planning to improve task execution in real-world environments. Current models demonstrate notable progress but face generalization limits when exposed to novel or unpredictable scenarios, as cited in recent benchmark studies (source: @jimfan_42). Additionally, there is growing interest in identifying scaling laws for embodied AI, similar to those observed in language models, to predict performance improvements and guide resource allocation in future research and commercial applications. These insights are driving new business opportunities in robotics, autonomous systems, and AI-powered automation.