X postCopyRebeldiaResource
Deep technical understanding of model mechanics beneath user prompts remains in high demand. Open course recordings from top institutions provide broad access to foundational AI theory, though social media attributions often require fact-checking.
The post uses tech compensation figures to generate interest in university-level machine learning fundamentals. The fact-checking note added by readers underscores how viral technical summaries on social platforms frequently mix up course numbers and lecturers.
CopyRebeldia
@CopyRebeldia · 22 August 2026
Así se ve un sueldo de 750.000 dólares al año: un tipo en camiseta blanca, un pizarrón y 2 horas y media. Stanford, CS336. Percy Liang construye un LLM desde cero. Lo que hay debajo de Claude y ChatGPT, y arranca por la parte que todos saltan: el modelo no lee tu texto, lee t.co/VTJ5XGdldC
22.7k likes185 replies41.9k bookmarks2.8m views
View on XSummary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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Link:Tatsunori Hashimoto, Percy Liang
Stanford University's CS336 course teaches the end-to-end development of language models from the ground up, inspired by traditional operating systems courses. Students build every component: data pipelines, Transformer architectures, Triton GPU kernels, scaling laws, and reasoning-focused alignment.
VideoVideo:Stanford Online
In the inaugural lecture of Stanford's CS329A course, Aakanksha Chowdhery and Azalia Mirhoseini outline the evolution of large language models towards autonomous, self-improving agents. The session explores foundational scaling laws, the emergence of chain-of-thought reasoning, post-training alignment via RLHF, inference-time scaling techniques, and the architectural principles behind agentic workflows in software engineering and research.
X post:Sizer
A viral post by Sizer promotes a two-hour lecture by Andrej Karpathy, claiming it covers agents, loops, graphs, and self-improving systems. However, commenters point out the video is actually an older presentation on basic daily LLM usage. The post also links to an article on orchestrating hundreds of sub-agents using context graphs.
1.7kbookmarks127.1kviews
Link:Percy Liang
The academic homepage of Stanford computer science professor Percy Liang outlines his core research directions, teaching portfolio, and academic initiatives. Liang focuses on foundational questions in machine learning, radically open model creation through the Marin project, and reproducible research via CodaLab Worksheets.
Link:Tengyu Ma
The official academic homepage of Tengyu Ma, Assistant Professor of Computer Science at Stanford University, detailing his machine learning research, current lab members, publications, and teaching.
X post:lumxss
In a viral post, lumxss shares a curated list of ten freely available textbooks covering machine learning, deep learning theory, and mathematical foundations.
9.1kbookmarks258.6kviews
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