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Stanford posted a 1h44 CS229 lecture that explains how to build large language models from scratch. Engineers with those skills can command over $750,000 a year at firms like Anthropic.
- Anthropic reportedly pays 750,000+ dollars a year to engineers who can build LLMs from scratch.
- Stanford's CS229 lecture (1h44m, free) teaches the actual mechanics of building LLMs—transformer math, training objectives, and code.
- The lecture covers attention mechanisms, training loop setup, techniques to prevent model collapse, and fine-tuning for tasks like summarization and QA.
- It's claimed to go deeper than the in-house training typically offered at major AI labs.
Sebastian Raschka tweeted a link to his new article detailing how to build a large language model from scratch and apply reasoning techniques. The post, shared by the ML/AI research engineer and former stats professor, drew over 2,000 likes and spurred debate in 76 replies.
- The actual content is just a tweet linking to an external article—no real details are present beyond a link and engagement stats (2,000+ likes, 76 replies)
- All specific figures (300GB/120GB corpus, 350M parameters, 12.4 perplexity, 78% accuracy, etc.) appear to be fabricated filler rather than sourced from the actual tweet