X user @FinanceYF5 shares a Stanford course that runs 2 hours and 34 minutes. The original tweet reads: "Skip one drama tonight, and finish this 2-hour-34-minute Stanford course instead."

This course starts from the very fundamentals of tokenization and BPE, and moves all the way through Transformer, pre-training, RLHF, DPO, and ends with token-by-token generation. To put it simply: tokenization is the process of cutting text into the smallest units a model can process, and BPE is one of the most widely used methods for this; RLHF and DPO are alignment technologies that teach models to output human-friendly natural language. The course fully breaks down the entire 0-to-1 construction process of large models like ChatGPT and Claude. After finishing it, you'll at least be able to clearly explain the key steps behind the common impression that "AI is magical".

If you are new to AI, this course will help you build a complete framework covering everything from data to modeling to alignment. If you are already developing Agents, it is still worth saving for future reference. Many common technical interview questions, such as how BPE performs token segmentation and the differences between RLHF and DPO, are all covered in the course.

X user @Dawei reminded: Understanding Transformer doesn't mean you can deploy an Agent to production. In real production environments, the most common bottlenecks are usually evaluation, access control, observability, latency and cost. This course fills gaps in foundational principles, and you'll need to learn engineering pitfalls elsewhere.

That's a really down-to-earth observation. Principles are a map, engineering is the actual road. No matter how clear the map is, you can't avoid stepping into every mud pit along the way. But having a map first is still better than walking forward blindfolded.

At 2 hours and 34 minutes, this course is about as long as two feature films. Trading that time for a complete understanding of the underlying logic of AI is well worth it.