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1600 Lines of Code Create an Underwater Manhattan, Fable 5 Leaves Karpathy Stunned

Title: Fable 5 Stuns Karpathy with 3D Worlds Built from 1600 Lines of Code An AI model, Fable 5, has demonstrated a remarkable leap in generating complex, interactive 3D worlds with minimal code. Showcased by Peter Gostev of Arena.ai, the model created 63 diverse 3D environments across themes like immersive cityscapes, explorable famous paintings, natural wonders, and cosmic phenomena. Many were generated in a single attempt. A standout creation is a detailed, submerged Manhattan built with only 1600 lines of Three.js code. Other highlights include traversable versions of Van Gogh's "Starry Night," a bear catching a salmon with realistic physics, and a split Red Sea. The model's ability to cohesively manage vast numbers of elements within a scene represents a significant technical advancement. Andrej Karpathy, who recently joined Anthropic's pre-training team, expressed amazement, particularly at how the model intuitively understood and rendered complex real-world interactions like a fish struggling when caught. He coined the term "fablemaxxing" to describe this qualitative leap. While Fable 5 excels at world-building, Gostev notes current limitations in creating engaging, long-form gameplay. The model also sometimes requires prompting to fully utilize its capabilities. Having topped the Agent Arena benchmark for real-world task completion, Fable 5 signals that the boundaries of AI-generated content are rapidly expanding, with its full potential yet to be discovered.

marsbit07/06 09:48

1600 Lines of Code Create an Underwater Manhattan, Fable 5 Leaves Karpathy Stunned

marsbit07/06 09:48

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

Andrej Karpathy, a core researcher at Anthropic, recently critiqued the current AI agent development frenzy. He argues that the biggest mistake is forcing agents to perform tasks without first thoroughly understanding the underlying large language models. Drawing from his 2016 "World of Bits" project at OpenAI—an early attempt at web-based agents that ultimately failed due to premature technology—he emphasizes that foundational model work is crucial. Karpathy offers three key pieces of advice: First, focus on getting the base models right before pushing agents. Second, recognize that creating a demo is easy, but building a real product takes a decade, akin to the journeys of autonomous driving and VR. Third, the product is the core capability, not the agent shell; a robust foundation will naturally enable advanced agents. He also suggests looking to neuroscience for inspiration, comparing agent components to brain structures like the hippocampus and thalamus. Despite his caution, Karpathy concludes that independent developers and startups, not large labs like OpenAI, are at the forefront of agent innovation. This is because the agent field is new, with no entity having a five-year head start, leveling the playing field for agile experimenters. His core message is not to abandon agent work, but to build it on a solid, deeply understood foundation.

marsbit07/06 02:33

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

marsbit07/06 02:33

Karpathy's Genius Strikes Again, Challenging RAG, Turning Your Notes into a Second Brain

Andrej Karpathy has proposed a revolutionary concept for managing personal knowledge: treating notes as immutable "source code" and using LLMs as "compilers" to build a structured, interlinked wiki. This approach fundamentally shifts the cognitive workflow away from the limitations of RAG (Retrieval-Augmented Generation), which merely retrieves and pieces together fragments, leading to contradictions and "digital mummies"—unused, decaying notes. The LLM-Wiki framework introduces a three-layer architecture: the **Raw Layer** for original, immutable notes; the **Schema Layer** defining rules for structuring knowledge; and the **Wiki Layer**, where the LLM continuously compiles and maintains a coherent, cross-referenced knowledge base. Key operations are **Ingest** (adding new material, which triggers updates across related pages), **Query** (asking the compiled wiki, with answers that can become new pages), and **Lint** (periodic AI audits to find contradictions, outdated claims, or gaps). This system automates the tedious maintenance—updating links, resolving conflicts, keeping summaries fresh—that has historically made large-scale personal knowledge management unsustainable. It realizes Vannevar Bush's 1945 "Memex" vision by finally solving the maintenance problem. Karpathy's proposal represents a third piece in human-AI collaboration, following "Vibe Coding" and "Agentic Engineering." It liberates human attention from organizational drudgery, refocusing it on what matters: deciding what to read and deriving meaning.

marsbit07/01 09:53

Karpathy's Genius Strikes Again, Challenging RAG, Turning Your Notes into a Second Brain

marsbit07/01 09:53

This is How God Karpathy Uses Claude?

Andrej Karpathy, a prominent figure in AI, has reportedly joined Anthropic, leading to a noticeable decrease in his open-source contributions and social media activity. A document claiming to be his personal "CLAUDE.md" file—a set of instructions for the Claude AI to follow within a specific codebase—has been circulating online. While its authenticity is unverified, the content aligns closely with Karpathy's publicly shared principles on effective AI-assisted programming. The document outlines key rules for AI coding assistants, emphasizing the importance of reading existing code thoroughly before writing new code to maintain consistency. It advises against over-engineering, advocating for simple, surgical modifications that match the project's existing style. Other guidelines include clarifying assumptions upfront, writing meaningful tests, thoughtful debugging, and carefully considering dependencies. The core message is that these principles help prevent common AI coding failures, such as introducing unnecessary abstractions, style drift, or making invisible architectural decisions. The community has noted that even experts like Karpathy require detailed instructions to guide AI effectively, akin to managing a junior developer. A related GitHub repository, "andrej-karpathy-skills," which encapsulates these ideas, is reported to significantly reduce Claude's code error rate. Ultimately, the advice stresses that the best CLAUDE.md is tailored to one's own tech stack and coding practices.

marsbit06/27 07:32

This is How God Karpathy Uses Claude?

marsbit06/27 07:32

What Does the Top AI Influencer Karpathy Seek by Joining Anthropic?

AI pioneer Andrej Karpathy, a founding member of OpenAI, has joined AI safety-focused company Anthropic to lead a new team focused on pre-training research. His role will specifically involve using Anthropic's Claude model to accelerate exploration in pre-training, the foundational stage of large language model development. Karpathy's move is seen as a significant signal in the AI industry, combining his deep technical credibility with substantial public influence. It follows a period of momentum for Anthropic, which recently surpassed OpenAI in enterprise adoption rates and has expanded its offerings to smaller businesses. The move also highlights a continuing trend of talent migration from OpenAI to Anthropic, often linked to differing priorities on commercialization versus research depth and safety. By placing Karpathy on pre-training, Anthropic is making a long-term bet on fundamental AI advancements, exploring the meta-idea of using current AI to help build the next generation of models.

marsbit05/21 08:02

What Does the Top AI Influencer Karpathy Seek by Joining Anthropic?

marsbit05/21 08:02

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters

Andrej Karpathy recently revealed that he has developed what he calls "AI psychosis," an obsessive state where he spends up to 16 hours a day directing AI agents instead of writing code himself. In a podcast with Sarah Guo, he explained that his workflow has shifted from 80% hand-coding and 20% AI-assisted to the reverse, or even more extreme. He now manages multiple AI agents simultaneously, treating them as a team to execute tasks. Karpathy admitted that he’s become addicted to optimizing AI performance, constantly worrying about whether he’s using tokens efficiently or pushing the system to its limit. He highlighted the importance of an agent’s “personality,” noting that Claude Code feels more like a collaborative teammate compared to colder, more mechanical alternatives. He also shared practical applications, such as "Dobby," a Claude-based smart home agent that integrates and controls all his home devices through natural language, replacing six separate apps. In research, his "AutoResearch" project used AI to run 700 experiments, resulting in an 11% training speed improvement for an AI model—discovering optimizations he had missed as a human researcher. Despite the capabilities, Karpathy noted that AI agents still exhibit uneven performance—sometimes brilliant, other times childlike—due to limitations in reinforcement training. He predicts that 2026 will see a "slopacolypse," with AI generating vast amounts of mediocre content. His experience signals a broader shift: humans are becoming directors of AI systems rather than executors, navigating a new era of human-AI collaboration.

marsbit03/23 11:44

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters

marsbit03/23 11:44

Karpathy Deletes Repository in Emergency! AI Job Apocalypse Chart Goes Viral, 60 Million White-Collar Jobs at Risk

Karpathy's viral project, "karpathy.ai/jobs," analyzed 342 U.S. occupations using AI (Gemini Flash) to assign an "AI exposure" risk score (0-10). The findings revealed an average exposure of 4.9, with 42% of jobs (59.9 million positions) scoring 7 or higher, indicating high automation risk. High-risk roles are primarily screen-dependent and information-dense, including software developers (9/10), financial analysts (9/10), lawyers (8/10), and office clerks (9/10). The most secure jobs involve complex manual labor, such as plumbers, construction workers, and cleaners, which are difficult to automate. Higher-paying and degree-requiring jobs are more significant exposure. Karpathy later removed the project, clarifying it was a quick personal experiment that was overinterpreted. A supporting Harvard study confirms AI is already causing a "great reassignment," reducing hiring in highly automatable roles while increasing it in AI-augmented ones, highlighting a bifurcation in the white-collar job market.

marsbit03/16 11:44

Karpathy Deletes Repository in Emergency! AI Job Apocalypse Chart Goes Viral, 60 Million White-Collar Jobs at Risk

marsbit03/16 11:44

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