KDnuggets picked 10 X accounts to follow for LLM news

AI feed on X has long turned into a mixture of useful releases, paper threads, and endless noise. KDnuggets offers a short but practical list of ten accounts that help you keep track of LLM news without having to read hundreds of similar posts.

Why X Still Works

While traditional media and academic journals publish analysis with a delay, X remains a place where researchers, engineers, and tool creators discuss models almost in real time. That's where new papers, demos, open-source releases, and first impressions of products surface earliest. The problem is obvious: the algorithmic feed easily mixes strong technical breakdowns with surface-level hype, and separating one from the other becomes a separate job.

That's the point of KDnuggets' selection: not to find "all the main AI people," but to gather sources that regularly provide signal, not noise. The author deliberately moves away from the most obvious names and bets on accounts where you can find either practical value or good news filtering. This approach matters for those who need not viral threads but clear landmarks: what to read in the morning to understand in ten minutes what really happened in the LLM world.

"If you need signal, not noise, these are reliable accounts to subscribe to."

Who to Add to Your Feed

The list turned out to be not about one role, but immediately about several ways to keep track of the market. Some accounts are useful for papers and research threads, others — for practice and architecture breakdowns, still others — for quick updates about releases, tools, and local model deployment. This way, the feed doesn't narrow down to just "news for news' sake" but covers the entire cycle: from idea and paper to real-world application.

• DAIR.AI and alphaXiv — for papers, brief explanations, and tracking what's being discussed around arXiv.
• Andrej Karpathy — for intuitive explanations, a fundamental perspective, and understanding where LLMs are moving.
• Sebastian Raschka and Simon Willison — for those who want not just to read about AI but to actually build and test something.
• The Rundown AI, AK, and Matt Wolfe — for a stream of releases, new open-source tools, and quick product updates.
• Ahmad Osman and Ethan Mollick — for topics around local inference, GPU infrastructure, work, and AI's impact on organizations.

It's also useful that the list has internal logic. If you need only research, a couple of accounts are enough. If practical building matters more, you can assemble a core from Raschka, Willison, and Karpathy. If you want to see the entire market, news accounts complement the technical side and help you not miss important model launches, services, and tools. As a result, the feed stays compact yet still covers both theory, hands-on practice, and product signals.

How They Differ

The article emphasizes not the loudness of a name, but the type of value each feed provides. For example, Simon Willison is useful for honest notes on what works in LLMs in practice and what breaks in real scenarios. Ahmad Osman covers another layer — infrastructure, local models, inference, and GPU.

Ethan Mollick, on the other hand, speaks less about the internals of models and more about how AI is changing education, work, and processes within companies. This mix is especially useful now, when the LLM market is splitting. On one hand, everyone discusses new releases, reasoning, and agentic capabilities.

On the other, teams increasingly need not general impressions but answers to applied questions: what's worth trying, what can be deployed locally, what tools actually save time, and what papers might quickly become product features. A good X feed should cover both tasks, not just drive FOMO. Another conclusion from the material is simple: there's no point in subscribing to hundreds of AI accounts.

It's much more important to assemble a small but stable selection where sources don't duplicate each other. One account for research, another for development, a third for infrastructure, a fourth for business impact. This approach reduces noise and makes X a working tool, not an endless stream of distractions.

In essence, it's about a personal news editor you build for yourself.

What This Means

Information about LLM has become too much, so value is shifting from speed to quality filtering. KDnuggets' selection is useful because it offers not another list of "main influencers," but a compact set of roles: researcher, practitioner, news feed, infrastructure expert, and observer of AI's impact on work. For developers, product managers, and founders, it's a good way to keep your finger on the pulse without daily overload and without spending hours parsing a chaotic feed.