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# Introduction
AI is advancing so rapidly that traditional news outlets and even academic journals often struggle to keep up. LLM, in particular, sees advances in reasoning, efficiency, and agentic abilities so frequently that social media is constantly filled with them. X (formerly Twitter) remains a central hub for the AI ​​research community, where developers, engineers, and researchers can share and exchange ideas in real time.
However, finding high-quality information in the age of algorithmic feeds can be challenging. To truly benefit from the platform, one must filter through the hype to find contributors who provide deep technical expertise and actionable insights for the best results. There are some big, obvious names that everyone probably already follows, so I won’t repeat them here. Instead, this article focuses on accounts that consistently share useful LLM updates, papers, tools, or thoughtful commentary. If you want signal over noise, these are solid follows.
# 10 Best X (Twitter) Accounts for LLM Updates
// 1. DAIR.AI (@dair_ai)
DAIR.AI Regularly posts paper threads and short research explainers that are technical but still readable and easy to read. When people ask how to keep up with it it is usually recommended as a trustworthy feed for AI and LLM research indicators. i loved them personally “Machine Learning Papers of the Week” The series and the previous year followed it closely.
// 2. Andrzej Karpathi (@karpathi)
Andrej Karpathy Still one of the best for deep learning and clear thinking about LLM. When he posts, it’s usually worth a read. He shares intuition, learning advice, and perspective on where the field is going. If you care about the basics, this is a must-follow.
// 3. Sebastian Raschka (@rasbt)
Sebastian Rashka Focuses on implementation and learning by doing. You’ll see tutorials, architecture breakdowns, and practical machine learning and LLM insights. If you really make (or want to make) models, his posts are consistently useful.
// 4. AlphaXiv (@askalphaxiv)
alphaXiv arXiv is built around discovery and discussion of papers, with a social layer for research. This lets you browse, discuss, and see what other people are engaging with in recent papers, so you’ll quickly know what’s insightful or impactful. I’ve personally changed it up over the past month to keep up with the trends.
// 5. The Rundown AI (@TheRundownAI)
Rundown AI There’s a high-volume AI news stream that’s best used like a wire service: look at the headlines, click only what’s important, and ignore the rest. they have their own positioning “The largest AI newsletter,” Which matches how it feels on the X – i.e. fast, comprehensive and constantly updated. If you want to keep up with product launches, funding news, and model releases, this works.
// 6. AK (@_akhaliq)
AK arXiv is one of the most referenced accounts for new papers, model releases, and open-source tools. If something new drops, it often appears here immediately. The feed can sometimes get mixed up in viral content, but for discovery, it’s hard to ignore.
// 7. Ahmed Osman (@TheAhmadOsman)
Ahmed Usman Instead of relying solely on application programming interfaces (APIs), AI focuses on systems, infrastructure, and hardware, particularly running LLMs locally. He shares practical insights on graphics processing units (GPUs), inference performance, and self-hosted setups. Honestly, their posts almost convince you to buy a GPU and build your own local LLM setup.
// 8. Matt Wolfe (@mreflow)
matt wolfe Shares daily AI updates and tool roundups. Very builder-friendly. If you like to know what new AI products have launched this week (without looking for them yourself), this account keeps you updated.
// 9. Simon Willison (@simonw)
simon willison Excellent for practical LLM use. He shares experiments, real hints, tooling breakdowns, and honest thoughts on what works and what doesn’t. If you really care about building with LLMs and not just reading about them, this is one of the best follows.
// 10. Ethan Mollick (@emollick)
ethan mollick Talks about the LLM in the context of work, education and impact on the real world. Less about model internals, more about “What does this change?” more about. If you want thoughtful and original commentary on how AI impacts jobs and organizations, he is a strong voice.
# conclusion
You don’t need to follow hundreds of AI accounts to stay informed. A short, well-researched list is usually better. If you care:
- Research: DAIR.AI, AlphaXiv.
- Deep Intuition: Andrzej Karpathy.
- Practical Building: Sebastian Raska, Simon Willison.
- News and Tools: The Rundown AI, Matt Wolfe.
- Systems and infrastructure: Ahmed Osman.
- Function and Effects: Ethan Mollick.
Choose based on what you really want to learn. That alone will cut out most of the noise.
Kanwal Mehreen He is a machine learning engineer and a technical writer with a deep passion for the intersection of AI with data science and medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is an advocate for diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitex GlobalLink Research Scholar, and a Harvard VCode Scholar. Kanwal is a strong advocate for change, having founded FEMCodes to empower women in STEM fields.
