GoKawiilAdvice column urges teens to master LLM fundamentals from first principles
An opinion piece argues that a 17-year-old today should focus on building large language models from scratch rather than just using them, in order to understand their capabilities and limits. The author notes LLMs excel at tasks like writing essays but fail at physical, real-world tasks like cleaning a room, and suggests pursuing college and graduate study aimed at closing that gap. The piece frames this as a path toward developing new architectures beyond current LLMs for physical-world learning.
GoKawiil's interpretation of the reporting above, not reported fact.
The advice reflects a growing recognition that language fluency and real-world competence are separate problems, with robotics and embodied AI lagging far behind text generation. Encouraging young people to study foundational AI architecture, rather than just prompt engineering, points to where the next wave of research talent may be needed most.
- LLMs remain far better at language tasks than physical, embodied tasks.
- The author recommends deep technical study of AI architecture over surface-level AI usage.
- Future breakthroughs may require new methods beyond current LLM designs for real-world learning.
Build a Large Language Model (From Scratch)" by Sebastian Raschka — This hands-on book walks you through coding a working LLM step by step, from tokenization and attention to pretraining and fine-tuning. It's exactly the kind of foundation a curious 17-year-old (or anyone) needs before pushing past LLMs toward robotics and physical intelligence.
See Build a Large Language Model (From Scratch)" by Sebastian Raschka on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: twitter.com, 2026-08-23
Published there as: “I were 17, I'd learn how to build LLMs from scratch”
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