Tag
A tweet praising Andrej Karpathy's insights on using language models and custom explainer videos to transform how people learn and understand complex topics, illustrated by an AI-generated explainer of how Shazam works.
Andrej Karpathy shares a simple eval where an LLM is asked "Land or Water?" for latitude/longitude coordinates 16,200 times; the models perform well, suggesting they internalize world knowledge just from compressing the internet.
Andrej Karpathy shares practical techniques for understanding large volumes of LLM output more efficiently: constraining text with ASD-STE100 controlled language, generating charts and architecture diagrams, building interactive HTML pages for parameter exploration, and producing 3Blue1Brown-style explainer videos as the cost of 'disposable software' approaches zero.
Andrej Karpathy, with experience at OpenAI and Tesla, has shared a free 2-hour lecture covering AI agents, loops, harness, and self-improving systems, providing high-value education comparable to expensive bootcamps.
Andrej Karpathy has joined Anthropic to lead a team using Claude to accelerate pretraining research. The article also references Anthropic's published Claude cookbook on knowledge graph construction.
A tweet claims that Graph Engineering, developed by two Anthropic seniors, has made Karpathy's loop 1000x better by wiring agents into a graph, resulting in significantly improved Claude responses. It also notes that Andrej Karpathy joined Anthropic five weeks ago.
Andrej Karpathy says he's never felt more behind as a programmer, explaining that Software 3.0 shifts programming from writing code to prompting within a context window, redefining the job rather than making it easier.
Andrej Karpathy removed his affiliation with Anthropic from his online biography, suggesting a potential change in his professional association.
Andrej Karpathy may have left Anthropic, according to reports. The AI researcher previously worked at OpenAI and Tesla.
A vlog about Andrej Karpathy's journey from Google intern to founder of AI-defining projects like Tesla Autopilot, OpenAI, and Claude, emphasizing the value of working on things you don't yet understand.
Andrej Karpathy gives a 15-minute talk on the challenges of multi-task learning, covering architecture tradeoffs, loss balancing, data engines, and team workflow, highlighting often-overlooked team coordination issues.
Andrej Karpathy shared his years of experience with AI Agents at a Silicon Valley tech meetup, including that early OpenAI attempts were premature, the language model path was the right approach, agents are easy to demo but hard to productize, and that human cognitive structures should be leveraged as inspiration.
Andrej Karpathy's LLM Wiki pattern enables building a persistent, structured knowledge base that compounds over time, unlike stateless RAG systems. The tutorial shows how to create one in under 30 minutes using LLMs to compile and link markdown pages.
A Twitter thread discusses the irony of Andrej Karpathy's teaching philosophy—learning by building from scratch—while he now uses AI to code, and shares a 3-week learning journey using Claude Opus 4.8 to master deep learning the old way.
Andrej Karpathy emphasizes that true AI understanding comes from building, not collecting prompts, in a 2-hour interview.
Andrej Karpathy commented that Claude Tag represents the third paradigm shift in LLM interaction: from website, app to a persistent, asynchronous team member, and emphasized that this shift is effective and powerful.
Andrej Karpathy and Geoffrey Huntley advocate using loops for AI prompts—giving the AI a goal, letting it plan, act, and check its own work—instead of manual single-request interactions.
Andrej Karpathy shares his simple approach to using AI in a workshop: just tell the machine what you want in plain words, no complex prompts or frameworks. The video reveals his actual workflow.
The release of Fable 5 has sparked widespread discussion. Andrej Karpathy calls it a major version leap, some consider it their "singularity moment," while others worry about the future of software engineering. This article summarizes 10 most noteworthy reactions.
Jeremy Howard expresses strong dismay over an event that he believes is a dark day with potentially irreversible damage, likely in the AI industry.