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# Introduction
AI is moving so fast that traditional news outlets and even academic journals often struggle to keep up. LLMs, in particular, see breakthroughs in reasoning, performance, and agentic skills so often that social media is flooded 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 difficult. To truly benefit from the platform, one must filter through the hype to find partners who offer deep technical expertise and actionable insights for the greatest results. There are some big, obvious names that everyone is already following, 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 more signal than noise, these are the solid next.
# 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 skim. It is usually recommended as a reliable feed for AI and LLM research pointers when people ask how to maintain it. I personally loved them. “Machine Learning Papers of the Week” series and followed it closely last year.
// 2. Andrej Karpathy (@karpathy)
Andrej Karpathi Still one of the best for deep learning and clear thinking about LLM. When he posts, it’s usually worth reading. He shares insights, learning tips, and perspectives on where the field is headed. If you care about the basics, it’s worth following.
// 3. Sebastian Raschka (@rasbt)
Sebastian Raschka Focuses on implementation and learning. You’ll see tutorials, architecture breakdowns, and practical machine learning and LLM insights. If you really model (or want to), her posts are consistently useful.
// 4. alphaXiv (@askalphaxiv)
alphaXiv With a social layer to research, arXiv is built around discovering and discussing papers. It allows you to browse, discuss and see what other people are engaging with recent papers, so you can quickly get an idea of ​​what is practical or impactful. I have personally moved into it over the past month to keep up with the trends.
// 5. The Rundown AI (@TheRundownAI)
The Rundown AI A high-volume AI news stream is best used like a wire service: skim the headlines, click on only what’s important, and ignore the rest. They have their own positioning. “The Biggest AI Newsletter,” Which matches how it feels on the X — that is, fast, extensive, and constantly updated. If you want to keep up with product launches, funding news and model releases, this works.
// 6. AK (@_akhaliq)
A.K One of the most cited accounts for new arXiv papers, model releases, and open source tools. If something new drops, it often appears here quickly. The feed can sometimes be mixed with viral content, but for discovery, it’s hard to ignore.
// 7. Ahmad Osman (@TheAhmadOsman)
Ahmad Usman Instead of relying solely on application programming interfaces (APIs), AI focuses on systems, infrastructure and hardware, especially around running LLMs natively. He shares practical insights on graphics processing units (GPUs), inference performance, and self-hosted setups. Honestly, his posts almost convince you to buy a GPU and build your own native LLM setup.
// 8. Matt Wolfe (@mreflow)
Matt Wolff Shares daily AI updates and tool roundups. Very builder friendly. If you want to know what new AI products have launched this week (without having to hunt them down yourself), this account keeps you updated.
// 9. Simon Wilson (@simonw)
Simon Wilson Ideal for practical LLM use. He shares experiences, real tips, tooling breakdowns, and honest reflections on what works and what doesn’t. If you care about actually building with LLMs, not just reading about them, this is one of the best courses to follow.
// 10. Ethan Mollick (@emollick)
Ethan Mulk Discusses LLMs in the context of work, education, and real-world impact. Less about model internals, “What does it change?” If you want thoughtful and original commentary on how AI affects jobs and organizations, he’s a strong voice.
# The result
You don’t need to follow hundreds of AI accounts to stay informed. A short, well-researched list is usually best. If you care:
- Research: DAIR.AI, alphaXiv.
- Deep Intuition: Andrej Karpathi.
- Practical building: Sebastian Raschka, Simon Wilson.
- News and tools: The Rundown AI, Matt Wolff.
- Systems and Infrastructure: Ahmad Usman.
- Functions and Effects: Ethan Mulk.
Choose based on what you actually want to learn. That alone will cut through most of the noise.
Kanwal Mehreen is a machine learning engineer and a technical writer with a deep passion for AI along with data science and medicine. He co-authored the e-book “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is a champion of diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.