Lior Abutbul is an AI practitioner and newsletter writer focused on the frontier of agentic AI systems. Through the Agentic AI Weekly newsletter, Lior breaks down how autonomous AI agents work, where they're headed, and how builders and operators can put them to use today.
Nathan Lambert is a Senior Research Scientist and Post-Training Lead at the Allen Institute for AI (Ai2), where he leads open-source language model development on the OLMo and Tulu series. A UC Berkeley PhD, he previously led the RLHF team at Hugging Face, co-building the TRL library and the Zephyr model. He runs Interconnects AI, a Substack newsletter read by tens of thousands covering post-training, open models, and AI policy, and is the author of The RLHF Book (Manning Publications). With roughly 8,000 academic citations and a reputation for demystifying the hardest parts of modern AI, Lambert is one of the most trusted voices at the intersection of open-source AI research and public education.
Sebastian Raschka is a German-born AI/ML researcher, educator, and author who has built one of the most trusted independent voices in the machine learning community. Through his Substack newsletter 'Ahead of AI' (184,000+ subscribers), bestselling books like 'Build a Large Language Model (From Scratch)', and 91,000+ starred GitHub repositories, he demystifies cutting-edge AI for practitioners worldwide. After a stint as an Assistant Professor at UW-Madison and a role as Staff Research Engineer at Lightning AI, he now runs RAIR Lab as an independent researcher, writer, and consultant.
Swyx (Shawn Wang) and Alessio Fanelli are the co-hosts of Latent Space, the #1 AI Engineering podcast and newsletter with 200,000+ subscribers and 10M+ total readers. Swyx — a former Singapore hedge fund trader turned developer advocate who coined the term 'AI Engineer' — and Alessio — a Forbes 30 Under 30 VC partner and Rome-born dropout-turned-engineer — together define the curriculum and culture of a generation of engineers building with AI.
Jon Stokes is a 25-year veteran of online media who co-founded Ars Technica in 1998 with Ken Fisher, helping build it into the internet's premier tech publication before selling it to Condé Nast for $25 million. An engineer turned journalist turned product builder, he holds a B.S. in Computer Engineering from LSU alongside two master's degrees from Harvard Divinity School in early Christian history - a combination that explains his unusual range: equally comfortable dissecting CPU microarchitecture, AI policy, Second Amendment law, and New Testament scholarship. Today he's co-founder and CPO of Symbolic AI, runs a Substack newsletter on AI and crypto with 13,000+ subscribers, and serves as a fellow at Open Source Defense.
Tom Yeh is an Associate Professor of Computer Science at the University of Colorado Boulder and the creator of AI by Hand, a wildly popular educational newsletter and community that teaches transformers, LLMs, and deep learning architectures through pen-and-paper calculations. With 62,000+ Substack subscribers, 200,000+ social media followers, and a Feynman-inspired philosophy that you only truly understand what you can build by hand, Yeh has become one of the most influential voices in practical AI education - bridging the gap between black-box hype and genuine first-principles understanding.
Azalia Mirhoseini is an Iranian-born AI researcher, Stanford professor, and co-founder of Ricursive Intelligence - a frontier AI lab valued at $4 billion that uses AI to design better chips, which in turn train stronger AI. Best known for AlphaChip, the deep reinforcement learning system that now designs Google's TPUs and has compressed chip floorplanning from months to hours, she also co-invented the Mixture-of-Experts architecture underpinning GPT, Claude, and Gemini. With 20,000+ citations and a $335M-funded startup launched in under four months, she is closing the recursive loop between artificial intelligence and the hardware it runs on.
Carly Taylor is a data scientist, ML engineer, and Field CTO for Gaming at Databricks who blends computational chemistry roots with cutting-edge machine learning to transform how the gaming industry understands player behavior. As founder of Rebel Data Science and creator of the Taylor on Tech newsletter, she advocates fiercely for diversity in data science while holding two ML patents and a track record that includes reducing player churn by 17% at Activision.
Chip Huyen is a Vietnamese-American computer scientist, author, and educator who turned a rejection letter from Stanford into a three-year around-the-world journey, two bestselling Vietnamese travel books, and eventually a second application that got her in. She went on to teach at Stanford, build ML infrastructure at NVIDIA and Netflix, co-found Claypot AI, and write two of the most-read technical books on machine learning systems in production - 'Designing Machine Learning Systems' (2022) and 'AI Engineering' (2025). Her newsletter and blog are required reading for anyone building serious AI products.
Christoph Molnar is a Munich-based statistician-turned-ML-author who turned a side project into the field's most-cited book on interpretable machine learning. Author of six books including the canonical 'Interpretable Machine Learning' (3rd ed., 2025), he runs the Mindful Modeler newsletter and consults on making black-box models explainable. With 16,000+ Google Scholar citations and a PhD from LMU Munich, he sits at the precise intersection where statistical rigor meets machine learning pragmatism.
Daniel Bourke is an Australian machine learning engineer, educator, and content creator who teaches over 230,000 students worldwide through the Zero to Mastery Academy. Operating under the brand 'mrdbourke', he built a self-designed AI education path from a film degree and zero coding experience, and now creates courses on PyTorch, TensorFlow, and Hugging Face. He co-founded Nutrify, an AI-powered food tracking app, with his brother Joshua, and published his debut novel 'Charlie Walks' in 2024. His newsletter 'Eat, Move, Learn, Make' blends personal philosophy with technical insight.
Elvis Saravia is the co-founder and lead AI researcher at DAIR.AI, a mission-driven organization democratizing AI research and education worldwide. Based in Belize, he is the author of the Prompt Engineering Guide - one of the most widely read AI resources on the internet with 73,000+ GitHub stars and over 3 million learners - and publishes the AI Agents Weekly newsletter. A PhD graduate from National Tsing Hua University in Taiwan, he has contributed to landmark AI projects including the Galactica large language model at Meta AI, and is known for bridging rigorous research with accessible, production-minded education for the next generation of AI builders.
Eugene Yan is a Principal Applied Scientist turned Member of Technical Staff at Anthropic, where he bridges cutting-edge AI research with production-scale systems. Formerly at Amazon for five years building real-time recommendation and LLM-powered systems for Kindle and Search, Eugene is equally well-known for his prolific writing: 209 blog posts, 420,000+ words published, and a newsletter with over 11,800 subscribers. His open-source repository applied-ml on GitHub has become a canonical reference for teams shipping machine learning in production. He lives in Seattle, snowboards on weekends, and writes like someone who actually wants you to understand.
Hamel Husain is a machine learning engineer with 25+ years of experience who built part of the foundation beneath GitHub Copilot - his CodeSearchNet project was early LLM research later used by OpenAI for code understanding. Today he runs Parlance Labs, consults with AI teams across 35+ products, co-authored O'Reilly's 'Evals for AI Engineers', and teaches thousands of engineers how to move beyond vibes and actually measure their AI systems.
Josh Tobin is a machine learning infrastructure pioneer who spent three years as a research scientist at OpenAI - contributing to the famous Rubik's cube robot hand - before earning his PhD from UC Berkeley under Pieter Abbeel. He co-founded Gantry, an ML monitoring and continual learning startup that raised $28.3M, and created Full Stack Deep Learning, the first course focused on production ML engineering. His domain randomization technique, which transfers neural networks trained in simulation to the real world, has been cited over 600 times and reshaped how robotics teams build perception systems. He runs a newsletter focused on ML infrastructure and ops.
Maarten Grootendorst is a psychologist-turned-ML engineer at Google DeepMind, best known for creating BERTopic, KeyBERT, and PolyFuzz - open-source NLP tools with over 15 million combined downloads. Co-author of the Amazon #1 bestseller 'Hands-On Large Language Models' (O'Reilly, 2024) with Jay Alammar, he runs the 'Exploring Language Models' newsletter with 2M+ views and has taught 50,000+ students on DeepLearning.AI. His work bridges the worlds of psychology and AI, making complex language model internals accessible through strikingly visual guides.
Mihail Eric is a Palo Alto-based ML engineer, researcher, educator, and serial founder who has spent a decade bridging cutting-edge AI research and production systems. A Stanford CS alumnus who studied under Christopher Manning and Percy Liang, he built some of Amazon Alexa's earliest large language models, co-founded YC-backed Storia AI, founded Confetti AI (acquired by Towards AI), and now teaches 'The Modern Software Developer' at Stanford while running a newsletter for 17,000+ AI practitioners.
Tim Dettmers is an Assistant Professor at Carnegie Mellon University and Research Scientist at the Allen Institute for AI (AI2), best known for making large language models accessible on consumer hardware. He created the bitsandbytes library (2.2M monthly installs), co-authored QLoRA - a technique enabling fine-tuning of 65B-parameter models on a single GPU - and pioneered LLM.int8() quantization. With over 18,000 citations across his work, Dettmers has become one of the most influential voices in efficient deep learning, consistently arguing that computational democratization - not AGI hype - is where the real progress lives.
Vicki Boykis is a founding ML engineer and one of the most respected voices in applied machine learning. Known for making complex systems legible through rigorous writing and dry wit, she runs the Normcore Tech newsletter, authored a widely-cited deep dive on embeddings, built Viberary (a semantic book recommendation engine), and created Normconf - an unconventional data conference celebrating the unglamorous realities of ML work. She brings an economist's skepticism and a software engineer's discipline to a field that often confuses hype for progress.
Ahmad Ajmal is a Toronto-based fintech entrepreneur with an MBA and Master of Finance from the University of Toronto's Rotman School of Management. He is a founder and operator at Upfinity Inc., an AI-driven platform that helps startup founders transform ideas into operating businesses. With roots in corporate banking and a strong foundation in programming, Ahmad bridges the gap between traditional finance and modern technology - his team at Rotman was recognized as one of the top 3 for solving the Financial Inclusion Problem using machine learning, and Upfinity has since grown to over 11,000 users through organic growth alone.
Muhammad Umair is a Pakistan-based AI consultant, ML engineer, and PhD researcher who has spent seven-plus years building machine learning systems that actually ship. He leads AI training at atomcamp, has driven AI initiatives for UNDP Pakistan, and has built three AI SaaS products end-to-end. His PhD research at UESTC focuses on multimodal test-time adaptation and low-resource learning - the kind of work that makes AI usable in places where data is scarce and compute is expensive.
Abdul Ahad is a data consultant and TEDx speaker based in Eindhoven, The Netherlands, with over seven years of experience building data infrastructures for small and medium organisations across e-commerce, insurance, energy, and finance. He is the founder behind KYD Analytics and holds the philosophy of 'Bringing Data and Humans Together' - believing the human element matters more than the tool. In November 2024, he delivered a TEDxEindhoven talk titled 'Why aren't people voting anymore?' exploring how community bonds and incentivisation could revitalise democratic participation. A lifelong learner who codes, consults, and speaks on the stage.
Weights & Biases (W&B) is the AI developer platform that the world's leading machine learning teams use to build, train, and deploy better models faster. Founded in 2017 in San Francisco, W&B provides experiment tracking, model management, and LLMOps tooling used by over 1 million developers - from OpenAI and Meta to Toyota and AstraZeneca. Acquired by CoreWeave in May 2025 for $1.7 billion, W&B is now the software backbone of one of the most important AI infrastructure companies in the world.
Osman Ali Mian is an early-career AI researcher specializing in causal discovery and trustworthy machine learning. He completed his PhD magna cum laude at CISPA Helmholtz Center for Information Security (Saarland University, Germany) and is now a postdoctoral researcher at the Institute for Artificial Intelligence in Medicine (IKIM) in Essen. He has published at top-tier venues including AAAI, ICML, AISTATS, and KDD, and won an Outstanding Paper Award at AAAI 2026 — marking him as a rising star in causal ML.
Amber Yang is an Enterprise Partner at Lightspeed Venture Partners backing highly technical founders building next-generation AI software and infrastructure. Before VC, she founded Seer Tracking - an AI startup that used neural networks to predict space debris orbits with 98% accuracy - winning the $50K Intel Foundation Young Scientist Award and landing on Forbes 30 Under 30 in Science at age 18. A Stanford CS/Physics grad with a philosophy detour at Oxford, she brings a rare trifecta of deep technical chops, founder experience, and investor instinct to the table.
Vik Paruchuri is the founder and CEO of Datalab, an AI startup building small, efficient foundation models for document intelligence. A self-taught ML engineer who majored in American History, he previously founded Dataquest - an online learning platform that taught data skills to over 1 million students. His open-source projects (Marker, Surya, Chandra OCR) have earned thousands of GitHub stars and benchmark-topping accuracy scores. He publishes 'The Vik Letter' newsletter covering semiconductors and tech.
Dilawar Mahmood is a machine learning engineer at ZeroEntropy (YC W25) in San Francisco, best known for four years at Apple where he optimized on-device models for Siri and Spotlight - work he once presented directly to Tim Cook at the Steve Jobs Theater. A Norwegian-educated engineer who left a comfortable career track to attend the Recurse Center and rediscover what programming actually feels like, he builds distributed ML frameworks in his spare time and is on record hating vibe coding.
Yao Fu (符尧) is an AI researcher at xAI specializing in large language model reasoning, efficient inference, and distributed systems. A PhD graduate of the University of Edinburgh, he previously worked at Google DeepMind on Gemini 3 and Project Astra. With over 5,000 citations and key papers like ServerlessLLM (OSDI '24) and DuoAttention (ICLR '25), Fu bridges systems engineering and ML research. He writes the 'Yao Fu' newsletter on Notion and is known for the Chain-of-Thought Hub benchmark repository, which helped track LLM reasoning progress across the field.
Zeroframe is the company behind Andoria, an AI-powered customer onboarding agent that learns how web applications work and generates personalized walkthroughs for users who get stuck. Founded in San Francisco in 2024 by Daryl Budiman and Anirudh Ramprasad - two former MultiOn engineers who helped scale that AI startup to a triple-digit million-dollar valuation in under nine months - Zeroframe's flagship product drops into any web app with a single script tag and autonomously shows users exactly what to do, reducing churn by turning confusion into clarity.
Humanloop was an enterprise LLM development platform founded in 2020 as a UCL spinout, offering prompt management, evaluations, and observability tools for teams building AI applications. With customers like Duolingo and Gusto, it raised ~$8M and reached ~$3.8M ARR before being acqui-hired by Anthropic in August 2025, after which the platform was sunsetted on September 8, 2025. Its technology and team live on inside Anthropic's enterprise console.