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# Introduction
AI is moving so quickly that traditional news outlets and even academic journals often struggle to keep up. LLMs, more specifically, sees breakthroughs in reasoning, efficiency, and agentic capabilities so frequently that social media is flooded with them non-stop. X (formerly Twitter) continues to be 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 an era of algorithmic feeds can be challenging. To truly benefit from the platform, one must filter through the hype to find the contributors offering the deep technical expertise and actionable insights of the greatest consequence. There are some big, obvious names that everyone likely already follows, so I won’t be repeating those 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.
# The 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 commonly recommended as a dependable feed for AI and LLM research pointers when people ask how to keep up. I personally loved their “Machine Learning Papers of the Week” series and followed it closely last year.
// 2. Andrej Karpathy (@karpathy)
Andrej Karpathy is still one of the best for clear thinking about deep learning and LLMs. When he posts, it is usually worth reading. He shares intuition, learning advice, and perspective on where the field is going. If you care about fundamentals, this is a must-follow.
// 3. Sebastian Raschka (@rasbt)
Sebastian Raschka focuses on implementation and learning by doing. You will see tutorials, architecture breakdowns, and practical machine learning and LLM insights. If you actually build models (or want to), his posts are consistently useful.
// 4. alphaXiv (@askalphaxiv)
alphaXiv is built around discovering and discussing arXiv papers, with a social layer for research. It lets you browse, discuss, and see what other people are engaging with on recent papers, so you get a sense of what is practical or impactful sooner. I have personally shifted to it over the past month to keep up with trends.
// 5. The Rundown AI (@TheRundownAI)
The Rundown AI is a high-volume AI news stream that is best used like a wire service: skim headlines, click only what matters, and ignore the rest. Their own positioning is “largest AI newsletter,” which matches how it feels on X — i.e. fast, broad, and constantly updated. If you want to stay aware of product launches, funding news, and model releases, it does the job.
// 6. AK (@_akhaliq)
AK is one of the most referenced accounts for new arXiv papers, model releases, and open-source tools. If something new drops, it often shows up here quickly. The feed can mix in viral content at times, but for discovery, it is hard to ignore.
// 7. Ahmad Osman (@TheAhmadOsman)
Ahmad Osman focuses on AI systems, infrastructure, and hardware, especially around running LLMs locally instead of relying only on application programming interfaces (APIs). 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 local LLM setup.
// 8. Matt Wolfe (@mreflow)
Matt Wolfe shares daily AI updates and tool roundups. Very builder-friendly. If you like knowing what new AI products launched this week (without hunting them down yourself), this account keeps you updated.
// 9. Simon Willison (@simonw)
Simon Willison is excellent for practical LLM usage. He shares experiments, real prompts, tooling breakdowns, and honest reflections on what works and what does not. If you care about actually building with LLMs, not just reading about them, this is one of the best follows.
// 10. Ethan Mollick (@emollick)
Ethan Mollick talks about LLMs in the context of work, education, and real-world impact. Less about model internals, more about “what does this change?” If you want thoughtful and original commentary on how AI affects jobs and organizations, he is a strong voice.
# Conclusion
You do not need to follow hundreds of AI accounts to stay informed. A small, well-researched list is usually better. If you care about:
- Research: DAIR.AI, alphaXiv.
- Deep intuition: Andrej Karpathy.
- Practical building: Sebastian Raschka, Simon Willison.
- News and tools: The Rundown AI, Matt Wolfe.
- Systems and infrastructure: Ahmad Osman.
- Work and impact: Ethan Mollick.
Pick based on what you actually want to learn. That alone will cut most of the noise.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
