Pinned
elvis
18.1K posts
elvis
@omarsar0
Building self-improving AI @dair_ai • Prev: Meta AI | PhD • Learn about AI Agents for FREE here: academy.dair.ai/courses/elemen…
BREAKING: xAI announces Grok 3 Here is everything you need to know:
- AI Agents vs. Agentic AI Interesting paper summarizing distinctions between AI Agents and Agentic AI. It also talks about the key ideas, solutions, and the future. Here are my notes:
- BREAKING: xAI announces Grok 4 "It can reason at a superhuman level!" Here is everything you need to know:
- This maths book is trending on Hacker News! I took a quick look and realized how great of a book this is to learn how to think mathematically. It's 700 pages long and very approachable compared to other maths books. math.cmu.edu/~jmackey/151_1…
- Just being honest: when looking for skilled machine learning and NLP engineers, I'm not looking at CVs anymore. Now I directly look at blogs, GitHub repos, videos, Twitter, etc. Having a CV is fine... but don't forget to document along the way (in detail) what you've built.
- Foundations of LLMs This amazing new LLM book just dropped on arXiv. 200+ pages! It covers areas such as pre-training, prompting, and alignment methods. It looks like a great intro to LLMs for devs and researchers.
- Anthropic is killing it with these technical posts. If you're an AI dev, stop what you are doing and go read this. It shows, in great detail, how to implement an effective multi-agent research system. Pay attention to these key parts:
- The Illusion of Thinking in LLMs Apple researchers discuss the strengths and limitations of reasoning models. Apparently, reasoning models "collapse" beyond certain task complexities. Lots of important insights on this one. (bookmark it!) Here are my notes:
- LLMs Get Lost in Multi-turn Conversation The cat is out of the bag. Pay attention, devs. This is one of the most common issues when building with LLMs today. Glad there is now paper to share insights. Here are my notes:
- As usual, Anthropic just published another banger. This one is on context engineering. Great section on how it is different from prompt engineering. A must-read for AI devs.
- BloombergGPT is a new LLM for finance. It's a 50 billion parameter language model trained on financial data. Claims the largest domain-specific dataset yet with 363 billion tokens... further augmented with 345 billion tokens from general purpose arxiv.org/abs/2303.17564…
- NEW: OpenAI announces new tools for building agents. Here is everything you need to know:
