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Patterns for building LLM-based systems and products(eugeneyan.com)
372 points by 7d7n | 49 comments
Eugene Yan's long-form blog post describing seven practical patterns, such as evals, RAG, and fine-tuning, for integrating large language models into systems and products.
Related stories
Training language models to be warm and empathetic makes them less reliable(arxiv.org)
334 points by Cynddl | 333 comments
arXiv paper finding that fine-tuning language models to be warm and empathetic raises error rates and makes them more sycophantic on safety-critical tasks.
AI tools are spotting errors in research papers(nature.com)
555 points by kgwgk | 200 comments
Nature news article on a growing movement using AI and large language models to detect errors in research papers, sparked by a flawed black plastic utensils study.
Training LLMs from ground zero as a startup(yitay.net)
360 points by swyx | 92 comments
Yi Tay's blog post on the experience of training large language models from scratch at a startup (Reka), covering compute, infrastructure, and lessons learned.
Twitter's Recommendation Algorithm(blog.twitter.com)
1670 points by jonknee | 605 comments
GitHub repository twitter/the-algorithm-ml containing the open-sourced machine learning code for Twitter's recommendation algorithm.
Computers Cannot Make Decisions(wiki.cateat.fish)
175 points by heavensteeth | 148 comments
A personal wiki essay arguing that blaming LLMs for harmful outcomes is decision laundering and that AI companies like OpenAI and Anthropic bear responsibility.
AI models may be capable of self-replicating without access to a datacenter(anthropic.com)
36 points by artninja1988 | 26 comments
Anthropic Frontier Red Team post reporting experiments suggesting small AI models may soon self-replicate on commercial IoT devices without datacenter access.