25 Nov 25
https://twitter.com/karpathy/status/1993010584175141038
- You will never be able to detect the use of AI in homework. Full stop… You have to assume that any work done outside classroom has used AI.
- Grading has to shift to in-class work… students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later.
- We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don’t want students to be naked in the world without it…. in addition, you understand what it’s doing for you, so should it give you a wrong answer (e.g. you mistyped “prompt”), you should be able to notice it, gut check it, verify it in some other way, etc.
- A lot of the evaluation settings remain at teacher’s discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc
24 Nov 25
Note: This is a personal essay by Matt Ranger, Kagi’s head of ML In 1986, Harry Frankfurt wrote On Bullshit ( https://en.wikipedia.org/wiki/On_Bullshit ).
23 Nov 25
22 Nov 25
Run Qwen LLMs locally in your browser with WebGPU. Zero installation, instant AI chat.
21 Nov 25
Zeli compiles Hacker News and HuggingFace daily AI papers, automatically summarizing and extracting key points to help you quickly select interesting articles to read. Our platform helps you determine if an article is worth reading in-depth by providing concise digests that save your time and boost reading efficiency.
flashcards in a private podcast feed. Knowledge in Audio format.
Build your own ML framework. TinyTorch is organized into four progressive tiers that take you from mathematical foundations to production-ready systems. Each tier builds on the previous one, teaching you not just how to code ML components, but how they work together as a complete system.
Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems.
Papiers helps researchers and engineers explore arXiv papers with AI-generated summaries, wiki-style insights, and interactive viewing tools.
20 Nov 25
Note from me, Frederick: I did not write this, but it reads exactly as if I had wrote it. Holy cow.