AI researcher and educator
Andrej Karpathy
Andrej Karpathy is an AI researcher and educator, a founding member of OpenAI, and a former director of AI at Tesla working on computer vision.
About Andrej Karpathy
Andrej Karpathy is an AI researcher and educator whose public teaching covers neural networks and large language models. His educational videos include both technical lessons on building models and introductions for a general audience.
He was a founding member and research scientist at OpenAI, led Tesla's AI and computer vision work from 2017 to 2022, and returned to OpenAI in 2023 to work on midtraining and synthetic data. During his PhD at Stanford, he designed and taught the CS231n course on deep learning for visual recognition.
Mentioned in our coverage
79 articles name Andrej Karpathy, excluding author credits.
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- Is English really the hottest programming language? Karpathy's 2023 tweet, three years later →
Karpathy's viral line predated vibe coding by two years. English became the interface — but agents, repos, and tests became the runtime. A 2026 read on prompt-as-program from GPT-3 to shipping software in plain language.
- Meta Brain2Qwerty v2: Reading Your Thoughts Without Surgery →
Meta FAIR released Brain2Qwerty v2 on June 25, 2026 — a three-module deep learning pipeline (CTC encoder, word aligner, fine-tuned LLM) that reads typed sentences directly from magnetoencephalography brain signals. 61% average word accuracy, 78% for the top participant. Claude Opus 4.6 agents were used to discover the best training configuration. Code is open source.
- Types of AI Agents: Complete Taxonomy and When to Use Each (2026) →
"AI agent" covers everything from a chatbot with one tool to a fleet of orchestrated coding agents. This guide maps every major type — reactive vs deliberative, ReAct vs plan-and-execute, coding vs research vs browser agents, single vs multi-agent — and tells you which to build when.
- US vs Chinese AI Startups in 2026: Funding, Strategy, and Who Wins What →
America outspends China 23-to-1 on private AI investment but barely leads on model benchmarks. US and Chinese AI startups are running different races — this guide maps funding, strategy, moats, and where each side actually wins.
- Context engineering vs prompt engineering: a precise distinction for 2026 →
Prompt engineering fixes your wording. Context engineering fixes what the model sees. This guide draws the precise line, shows concrete examples of each in action, and maps out when to reach for which tool.
- Claude Code on VPS Only: levelsio's Year-Long Production Workflow (June 2026) →