--- title: The best AI engineer accounts to follow on X in 2026 | daily.dev description: A focused, small X feed grouped by engineering function beats following every AI influencer. Explore practical developer news, tutorials, and tools read by millions of developers worldwide. canonical: https://daily.dev/blog/best-ai-engineer-accounts-to-follow-on-x/ og:type: article og:url: https://daily.dev/blog/best-ai-engineer-accounts-to-follow-on-x/ og:title: The best AI engineer accounts to follow on X in 2026 | daily.dev og:description: A focused, small X feed grouped by engineering function beats following every AI influencer. Explore practical developer news, tutorials, and tools read by millions of developers worldwide. og:image: https://media.daily.dev/image/upload/s--QgMRrp8J--/f_auto,q_auto/v1/recruiter-landing/6ac440e4ef6c0279d8507e9b_1791270639463_f16a6573ec?_a=BAMAMiB80 og:site_name: daily.dev og:locale: en_US article:published_time: 2026-10-06 article:modified_time: 2026-10-06T07:36:47.703Z article:author: Alex Carter twitter:card: summary_large_image twitter:site: @dailydotdev twitter:creator: @dailydotdev twitter:title: The best AI engineer accounts to follow on X in 2026 | daily.dev twitter:description: A focused, small X feed grouped by engineering function beats following every AI influencer. Explore practical developer news, tutorials, and tools read by millions of developers worldwide. twitter:image: https://media.daily.dev/image/upload/s--QgMRrp8J--/f_auto,q_auto/v1/recruiter-landing/6ac440e4ef6c0279d8507e9b_1791270639463_f16a6573ec?_a=BAMAMiB80 --- I’d start with **3–5 accounts that match your work**, including one official lab account - not all 18. For production AI, my starting picks are Chip Huyen for system design, Hamel Husain for evals, and the lab behind the models you use. Here’s how I’d group this October 6, 2026, shortlist: - **Model internals and research:** Andrej Karpathy, Sebastian Raschka, and Lilian Weng. - **AI apps and production systems:** Harrison Chase, Simon Willison, Jeremy Howard, and Chip Huyen. - **Evals and post-training:** Hamel Husain, Eugene Yan, and Nathan Lambert. - **Open-source models and tools:** Omar Sanseviero and Soumith Chintala. - **Robotics:** Jim Fan. - **AI engineering trends and learning:** swyx and Andrew Ng. - **First-party releases:** OpenAI Developers, Anthropic, and Google DeepMind. I’d put those picks in separate X Lists and spend **10–20 minutes a day** scanning them. For depth, I’d use their blogs and newsletters; a reader such as [daily.dev](https://daily.dev/) can collect articles without replacing X threads and replies. My rule: use posts to find ideas, not approve deployments. Check the docs, papers, code, and your own tests before treating a demo as _ready to ship_. ::: @figure {AI Engineer Accounts to Follow on X in 2026} ## Choose accounts that match your AI work Group these accounts by the work you do most, and **follow one or two sources per area**. Numbering is for navigation, not rank.[\[7\]](https://uxcel.com/blog/best-ai-accounts-to-follow) For **model fundamentals**, start with Andrej Karpathy and Sebastian Raschka. They cover training, architecture, and model internals. If you’re **building AI apps and agents**, start with Harrison Chase for frameworks, tool routing, and agent loops. For **evaluation**, follow Hamel Husain for evaluation infrastructure and measurement before shipping.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/)[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/) For **open-model infrastructure**, follow Nathan Lambert, who covers post-training and open-model policy.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/)[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/) For **robotics**, start with Jim Fan for embodied AI. Put each group in a separate [X Lists](https://help.x.com/en/using-x/x-lists) feed so you can scan what matters today. For platform context, read [Where developer Twitter went: X, Bluesky, and Mastodon in 2026](/blog/where-developer-twitter-went-x-bluesky-mastodon-2026). ## 1\. Andrej Karpathy Karpathy is a code-first starting point for learning model fundamentals. **Follow for clear model explanations, not a news feed.** The account was active on September 25, 2026, and posts only a few times a month.[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) His “Let’s build GPT” tutorials and [Neural Networks: Zero to Hero](https://karpathy.ai/zero-to-hero.html) series are the main reasons to follow him. They help you trace how a model works, connecting the code to the concepts behind it. Then, dig into [nanoGPT](https://github.com/karpathy/nanoGPT) and [llm.c](https://github.com/karpathy/llm.c), which reimplements GPT-2 in C/CUDA. Read the tutorials alongside the code to see how each concept turns into an implementation. ## 2\. Lilian Weng While Andrej Karpathy explains how models work, **Weng draws on research to help guide design decisions about agents, reasoning, and model safety.**[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Her X posts link to more detailed writeups on [Lil’Log](https://lilianweng.github.io/). Start with _LLM Powered Autonomous Agents_ for agent architectures. If you build reasoning models, read her test-time compute posts first.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 3\. Harrison Chase **Building production agents? Follow Chase for hands-on guidance.** The founder of [LangChain](https://www.langchain.com/) covers [LangGraph](https://www.langchain.com/langgraph) for stateful workflows and [LangSmith](https://www.langchain.com/langsmith) for evaluation and monitoring. As of September 25, 2026, he frequently posts about new features and research updates, including background agents and how decision models can judge agent choices.[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) Check his replies to developers for troubleshooting help. Then verify his release posts against the official LangChain or LangGraph changelogs. ## 4\. Simon Willison **Follow @simonw for LLM tests you can inspect, not model announcements.** His posts focus on hands-on debugging rather than theory. He shares prompts, failures, and [AI tooling breakdowns](https://daily.dev/blog/the-best-ai-tools-for-developers-in-2024), often backed by code, logs, or source links. He maintains the [LLM CLI](https://llm.datasette.io/), a unified interface for working with different model providers and local models. His prompt-injection posts serve as a reference for keeping instructions strictly separate from data. For more context and code, visit [his blog](https://simonwillison.net/) and [Today I Learned archive](https://til.simonwillison.net/). ## 5\. Jeremy Howard Follow @jeremyphoward for **practical deep learning, small-model efficiency, and open-science thinking**. As of October 2026, he was active and worth following.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/) Howard co-founded [fast.ai](https://www.fast.ai/) and [Answer.AI](https://www.answer.ai/). His guidance is a good fit for developers working without a large research team.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Expect a top-down approach: **build first, then study the theory**. For longer writeups, follow [Answer.AI](https://www.answer.ai/).[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[3\]](https://softwarethug.com/posts/top-ai-people-to-follow-on-x-twitter/)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 6\. swyx Shawn “swyx” Wang follows AI engineering trends and developer tools, sharing new workflows and technical discussions early. **His X feed is a good place to spot new tools and ideas.** For more depth, his podcast and newsletter, [Latent Space](https://www.latent.space/), offer longer interviews with AI researchers and engineers. Next, Jim Fan turns the focus to robotics and embodied AI research. ## 7\. Jim Fan Jim Fan, Director of Robotics at [NVIDIA](https://www.nvidia.com/), is worth following for robotics and embodied AI.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[2\]](https://usefulai.com/feeds/x-accounts)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) His feed focuses on **physical AI, not general LLM chatter**, sharing papers and technical demos on transferring work from simulation to hardware.[\[2\]](https://usefulai.com/feeds/x-accounts)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) ## 8\. Nathan Lambert Nathan Lambert, a researcher at [Ai2](https://allenai.org/), writes about **post-training, alignment, and evaluation**. He covers RLHF and reasoning models, including reinforcement learning from verifiable rewards. Follow his X feed for paper breakdowns. For deeper analysis, read [Interconnects](https://www.interconnects.ai/), where he examines reward signals and evaluation methods that can guide your post-training pipeline. ## 9\. Sebastian Raschka Sebastian Raschka explains **how LLMs work through code**, diagrams, and paper breakdowns. He covers architecture, training, and inference scaling for readers who want the mechanics - not just the theory. His account was active on September 23, 2026, with recent posts covering inference scaling and optimizers.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) His book, [_Build a Large Language Model (From Scratch)_](https://sebastianraschka.com/books/), takes a structured, implementation-first approach to core LLM components. Start with the book for the fundamentals, then turn to his [newsletter](https://magazine.sebastianraschka.com/) for deeper technical updates.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 10\. Chip Huyen Chip Huyen writes about building production AI systems that are fast, affordable, and dependable. She covers data handling, latency, and cost control, from token costs to model selection. **Follow her if you ship AI systems, not demos.** [\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) For more depth, read [her blog](https://huyenchip.com/blog/). If measurement is your bottleneck, pair her feed with Hamel Husain’s. ## 11\. Hamel Husain Hamel Husain focuses on evals, error analysis, and LLM-as-judge workflows.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Follow him on X for hands-on advice on spotting failure patterns, turning those patterns into evals, and testing the judges themselves before shipping.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) For the full process, visit [his blog](https://hamel.dev/) and read posts like _Your AI Product Needs Evals_ and _A Field Guide to Rapidly Improving AI Products_.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 12\. Eugene Yan Eugene Yan, an applied scientist at [Amazon](https://www.amazon.com/), takes an implementation-focused approach to evals. He shares practical guidance on **shipping LLM systems, data workflows, and task-specific evaluation**.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) His [blog](https://eugeneyan.com/) features longer technical writeups. Start with his survey of task-specific LLM evaluation techniques to find approaches that fit different use cases.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 13\. Omar Sanseviero Omar Sanseviero covers **open-source model releases**, hands-on demos, and [Hugging Face](https://huggingface.co/) updates. His posts span datasets, benchmarks, and deployment tools for teams moving from model evaluations to shipping. ## 14\. Soumith Chintala Soumith Chintala is a co-creator of [PyTorch](https://pytorch.org/), and his account puts **PyTorch and open-source model work** front and center. He posts about building and shipping frontier AI systems and putting open-source work into practice. [\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Follow him for hands-on lessons - and check his replies for the most useful engineering details. [\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) ## 15\. Andrew Ng Follow Andrew Ng’s @AndrewYNg for **a focus on AI adoption**, with hands-on guidance from [DeepLearning.AI](https://www.deeplearning.ai/). He covers adoption trends, engineering workflows, practical skills maps, and changing roles for AI engineers [\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow). For longer reads, check out [The Batch](https://www.deeplearning.ai/the-batch/). ## 16\. OpenAI Developers Follow @OpenAIDevs for OpenAI’s developer-facing launch notes, [API updates](https://daily.dev/blog/api-versioning-strategies-best-practices-guide), and implementation details. Pair it with @OpenAI for major release announcements. For demos and engineering context, check @gdb; for reasoning research, follow @polynoamial.[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026)[\[7\]](https://uxcel.com/blog/best-ai-accounts-to-follow)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) **Treat launch posts as signals - not verification.** Check the code, docs, and model-behavior threads to confirm how a feature works. Use @OpenAIDevs and @OpenAI as your OpenAI feed, then scan the other lab accounts below for the same first-party signal. ## 17\. Anthropic Follow @AnthropicAI for **Claude releases**, developer release notes, safety research, and posts on mechanistic interpretability.[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026) For product demos and interface updates, check @claudeai. Boris Cherny shares [Claude Code](https://code.claude.com/docs/en/quickstart) setups and multi-agent workflows, while [Anthropic’s documentation](https://docs.anthropic.com/) covers implementation details.[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026) ## 18\. Google DeepMind Follow Google DeepMind for research papers, benchmarks, and updates on [Gemini](https://deepmind.google/models/gemini/), [Veo](https://deepmind.google/models/veo/), and [Imagen](https://deepmind.google/models/imagen/).[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026)[\[7\]](https://uxcel.com/blog/best-ai-accounts-to-follow) **Research posts signal what’s coming - not necessarily what you can ship.** Before planning an integration, check [Google AI Studio](https://aistudio.google.com/) and the [Gemini API documentation](https://ai.google.dev/gemini-api/docs) for access, pricing, and integration details. A research announcement doesn’t mean the capability is available through the API. The docs tell you what you can actually use.[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026)[\[7\]](https://uxcel.com/blog/best-ai-accounts-to-follow) For product updates, also follow Logan Kilpatrick (@OfficialLoganK), who shares AI Studio previews and Gemini API details. For more detailed research writeups, head to the [Google DeepMind blog](https://deepmind.google/discover/blog/).[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026)[\[5\]](https://andreinita.co/blog/top-ai-voices-x-2026/) ## Read their work without watching the timeline Make the topical X Lists for the accounts above your daily feed. Spend **10 to 20 minutes a day** checking them instead of scrolling a general timeline—one of several [ways to stay updated](/blog/5-practical-ways-for-web-developers-to-stay-updated-in-the-latest-tech-news) without getting overwhelmed.[\[4\]](https://pasqualepillitteri.it/en/news/3633/ai-x-twitter-accounts-to-follow-2026)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) For longer reads, head straight to their newsletters and blogs. As of late September 2026, verified subscriptions included Sebastian Raschka’s [Ahead of AI](https://magazine.sebastianraschka.com/), Nathan Lambert’s [Interconnects](https://www.interconnects.ai/), Simon Willison’s [blog](https://simonwillison.net/), and Andrew Ng’s [The Batch](https://www.deeplearning.ai/the-batch/).[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow)[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Keep following the original X accounts for replies, threads, and breaking posts. [daily.dev](https://daily.dev/) can bring those articles into **one feed**, so you don’t have to sort through a noisy default feed. It doesn’t replace the original posts, threads, or replies.[\[1\]](https://daily.dev/agentic-ai-hub/people-to-follow/) For technical debate and troubleshooting around their work, try [Hacker News](https://news.ycombinator.com/), [Lobsters](https://lobste.rs/), and [Reddit](https://www.reddit.com/). For a deeper comparison, see [Best developer news aggregators compared](/blog/best-developer-news-aggregators-compared).[\[3\]](https://softwarethug.com/posts/top-ai-people-to-follow-on-x-twitter/)[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) [TLDR](https://tldr.tech/) works well for short digests, while [dev.to](https://dev.to/) is a good source for hands-on tutorials.[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow) To follow writers beyond X, see [where developer Twitter went in 2026](/blog/where-developer-twitter-went-x-bluesky-mastodon-2026). ## Start with a small, focused feed Once you have a shortlist, trim it further. **Choose accounts that fit your engineering work**, not those with the most followers. Start with 3 to 5 accounts that offer a mix of teaching and hands-on experience, including one lab.[\[3\]](https://softwarethug.com/posts/top-ai-people-to-follow-on-x-twitter/) > For production systems, start with Chip Huyen, Hamel Husain, and Google DeepMind.[\[6\]](https://www.zarifautomates.com/blog/best-ai-twitter-x-accounts-to-follow)[\[2\]](https://usefulai.com/feeds/x-accounts) Give the feed a month before adding more accounts.[\[8\]](https://physicalainews.com/25-ai-engineers-you-should-be-following-in-2026/) Keep those that explain their limits and correct earlier claims. Review your choices monthly, and remove accounts that shift into clickbait or generic commentary. Use posts to find ideas, then **check the docs, papers, or code** before changing production systems. Treat a demo as production-ready only after evals cover data, speed, and cost. ## FAQs ### Which accounts are best for AI beginners? If you’re new to AI, start with accounts that explain concepts clearly and show how they work in practice. **Andrej Karpathy** is a good place to build intuition about deep learning and learn how to build models from scratch. **Simon Willison** shares hands-on tests of how LLMs behave in practice. For guidance on applied AI and industry adoption, follow **Andrew Ng**. **Ethan Mollick** takes an evidence-based look at how AI affects work and education. ### How can I spot hype in AI posts? Follow accounts that link to **original research, code, or reproducible experiments**, not vague claims or commentary designed to get clicks. Look for practitioners who show their work - including failures, debugging logs, and implementation details. Before accepting a bold claim, check the methodology behind it and see whether other experts independently support it. **Private X lists** grouped by research, engineering, or policy can help you focus on reliable voices and cut through the noise. ### How do I turn AI posts into hands-on practice? **Use X for discovery, not doomscrolling.** Create a private “AI Core” list with 10–15 accounts, and turn on alerts for 3–4 that relate to your work. Spend your time testing prompts, tools, or workflows you can reproduce. Before shipping, read the linked paper, code, or docs. Run a small evaluation or experiment, then use the results to refine your work. 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