karpathy

A tweet from @cyrilXBT praises a paper on graph engineering for agentic systems, claiming it significantly improves AI responses, and promotes a spammy bidding website called RankNow.

Andrej Karpathy built an LLM from scratch and open-sourced all code, covering the full pipeline of dataset, training loop, and inference loop, continuing his work on simplifying AI education with micrograd and makemore.

Andrej Karpathy released nanochat, a minimalist LLM training framework that can train a GPT-2-level model for just around $48, covering the entire pipeline of pretraining, fine-tuning, and reinforcement learning, with minimal and fully transparent code.

A compilation of Andrej Karpathy's recent tweets covering a minimal GPT implementation, vibe coding an iOS app, and a detailed comparison of GPT-4.5 with earlier models, offering insights into AI testing and scaling.

Andrej Karpathy experiments with Opus 5, giving it the first paragraph of Lord of the Rings and a 1M token budget to create a 3D JS rendering of the story, highlighting LLM stamina for hyper-custom worlds while noting weaknesses in multimodal auditing and gameplay.

Discusses Andrej Karpathy's ability to explain complex technical concepts clearly, and extends to the importance of Developer Relations (DevRel), emphasizing that this skill becomes even more critical in the AI era. Also shares the experience and insights of a DevRel practitioner.

A single CLAUDE.md file that provides structured behavioral guidelines for Claude Code, derived from Andrej Karpathy's observations on common LLM coding pitfalls, has gained 192k GitHub stars. It aims to prevent over-engineering, reduce wrong assumptions, and enforce simplicity in AI-generated code.

Two Hong Kong students achieved a 5x speedup by adding another loop outside the original automated research framework, without needing a better model or more compute. It is considered one of the most useful papers for ordinary Agent developers.

The article provides a detailed analysis of the llm-wiki method proposed by Karpathy: using LLM to compile personal raw materials into interlinked wiki pages, where users only need to read and ask questions without manual editing. The author compares the limitations of RAG and provides a 30-minute setup tutorial, emphasizing the key role of schema files in system discipline.

The author experiments with self-improving AI loops using Claude and tools like AutoResearch, demonstrating that recursive self-improvement is accessible beyond frontier labs and can automate newsletter busywork.

Introducing the Karpathy Loop concept, where AI automatically executes experimental loops without real-time user interaction, boosting work efficiency by 10x.

Explains Karpathy's reader3 method that uses an LLM as a private tutor to help readers actively engage with books chapter by chapter, turning passive reading into lasting understanding through summarization, Q&A, and note-taking.

At the Sequoia AI Conference, Karpathy spent nearly 30 minutes in-depth sharing the dramatic changes in programming paradigm, including concepts like Software 1.0/2.0/3.0 framework, vibe coding, jagged intelligence, etc., and provided practical advice for founders.

Google released Agents CLI, a unified command-line tool that integrates scaffolding, evaluation, and deployment of agents on Google Cloud, enabling coding assistants to practice agentic engineering without context switching.

Andrej Karpathy releases a document describing an agent architecture that shifts from prompt-based interactions to a loop-based system where the model handles execution and bookkeeping while the human owns the spec and boundaries, effectively turning Claude into an autonomous agent.

Andrej Karpathy joined Anthropic and his personal Claude.md configuration file is being shared, dramatically improving Claude's response quality by aligning it with the user's thinking style.

Karpathy shares a method to fully automate Obsidian note-taking using Claude, turning the AI into a maintainer that reads new sources, structures a wiki, and runs checks for contradictions, eliminating manual effort.

This article introduces a new personal knowledge management method proposed by Andrej Karpathy: using LLMs to automatically compile raw notes into a structured Wiki, replacing traditional RAG, and achieving compound growth of knowledge.

Google announced the Open Knowledge Format, an open standard based on Karpathy's LLM wiki concept, designed to provide context for AI agents using simple markdown files.

awesome-autoresearch is a curated list of autoresearch use cases. This new entry applies Karpathy's autoresearch pattern to everyday software engineering scenarios, providing concrete templates to lower the adoption barrier for engineers.