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.
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.
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.
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.
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.
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.
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.
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.
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.
Jensen Huang is the co-founder, president, and CEO of Nvidia Corporation — the world's most valuable semiconductor company, which he built from a Denny's booth in 1993 with $600 in combined cash. Born in Taipei, raised between Thailand and rural Kentucky, Huang is the longest-serving CEO of any S&P 500 technology company. His two-decade gamble on CUDA software created the unassailable moat that made Nvidia the backbone of the global AI revolution. He personally delivered the first AI supercomputer to OpenAI in 2016. As of 2026, Nvidia surpasses $5 trillion in market cap, generates $216 billion in annual revenue, and Huang's net worth stands at approximately $170 billion. The leather jacket is optional. The legacy is not.
Weights & Biases (W&B) is the leading AI developer platform for machine learning and generative AI, offering tools for experiment tracking, hyperparameter optimization, model registry, and LLM application development. Founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis in San Francisco, W&B powers over 1 million developers and 1,400+ organizations — including OpenAI, Meta, and NVIDIA — by making it easier to build, train, evaluate, and deploy AI models. Acquired by CoreWeave for ~$1.7B in May 2025, W&B continues expanding its platform with Weave for LLM/agent observability, cementing its position as the de facto infrastructure for modern AI development.
RunPod is an AI cloud infrastructure company that provides on-demand GPU compute for training, fine-tuning, and deploying AI/ML models. Founded in 2022 by two former Comcast engineers who pivoted their Ethereum mining rigs into AI servers, RunPod grew to $120M ARR with just $22M raised by early 2026, serving 500,000+ developers across 183 countries. Its marketplace model, per-second billing, and support for 30+ GPU SKUs — from consumer RTX 4090s to enterprise H100s and B200s — make it a capital-efficient disruptor to hyperscaler GPU clouds like AWS, GCP, and Azure.