正在加载视频...

视频加载失败

Claude 3.7 reasoning and coding capabilities are no joke! Watch how I use it to one-shot a quick simulator of how attention mechanisms work. I think it could be awesome for each of us to have access to a personal Andrej Karpathy-like tutor to explain complex stuff to us. 😀

31,669 次观看 • 1 年前 •via X (Twitter)

8 条评论

elvis 的头像
elvis1 年前

This is not perfect by any means and I didn't check for mistakes but just wanted to quickly share this to inspire more of these use cases.

elvis 的头像
elvis1 年前

I also like this one (Precision & Recall). Claude 3.7 Sonnet (with extended thinking) also did this oneshot. The quality of the code and dashboards is on another level.

elvis 的头像
elvis1 年前

Prompts for first example: "Can you help me explain attention mechanisms in Transformers to college students? Think deeply about clever ways to explain the concepts without focusing too much on maths." "Now create a simulator that could students understand it better."

Jay Rodge 的头像
Jay Rodge1 年前

@karpathy This is a cool use case! Can you share the prompt in this thread?

elvis 的头像
elvis1 年前

@karpathy Sure, let me do that.

Tariq Gadgetman 的头像
Tariq Gadgetman1 年前

@karpathy Claude 3.7's capabilities are indeed impressive! A tutor like Karpathy would make complex concepts much easier to grasp.

Jilong | We provide AI marketer - 24/7 marketing 的头像
Jilong | We provide AI marketer - 24/7 marketing1 年前

@karpathy personal tutors could boost learning, for sure.

Yang 的头像
Yang1 年前

Want to learn how practical AI skills and automations for your business and work? Check out our step-by-step video tutorials 100% FREE 🥳

相关视频

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,661 次观看 • 1 年前