# opus[1m] ## Retrieval Notes - Session id: `096d8572-4c53-4ccf-909d-b0cfe516beab` - Last activity: `2026-03-31T21:37:43.062Z` ## My Notes <!-- Add your notes here. This section is preserved across syncs. --> ## Conversation ### User opus[1m] ### User The user has opened the note file:///Users/nickyoung/My Obsidian Vault/Daily Notes/2026-03-31.md in Obsidian. This may or may not be related to the current conversation. If it seems relevant, consider using the Read tool to examine its content. @[[2026-03-31]] "" explain this in a way i will undersatnd: CHAT: Post Guido van Rossum & @gvanrossum • 3h S... I think I finally understand what an agent is. It's a prompt (or several), skills, and tools. Did I get this right? • 328 17 109 • 1.9K ıhl 205K Andrej Karpathy @karpathy § ... LLM = CPU (data: tokens not bytes, dynamics: statistical and vague not deterministic and precise) Agent = operating system kernel 8:59 PM • Mar 31, 2026 • 83.3K Views 60 {7 126 • 1.9K • 7367 Relevantv Post your reply View quotes › Reply Anna @Anna Partners • 2h O ... The OS kernel analogy is the most robust framework for understanding agents. If the LLM is the CPU, then the agent's context window is the RAM, and its tools are the I/O. We are essentially watching the birth of the first truly non-deterministic operating systems. 2 174 > 59 ill 5.1K Andrej Karpathy @karpathy .2h yeah exactly, i mean it's this picture from 2023 basically O ... Andrej Karpathy @karpathy. Nov 11, 2023 LLM OS. Bear with me I'm still cooking. Specs: - LLM: OpenAI GPT-4 Turbo 256 core (batch size) processor @ 20Hz (tok/s)... Peripheral devices I/O video audio Software 1.0 tools "classical computer" Calculator Python interpreter Terminal Disk File system (tembeddings) CPU Ethernet Browser LLM 1713 RAM context window • 114 Other LLMs Ill 8.1K ### Assistant Guido van Rossum (Python's creator) posted a basic definition of an agent: a prompt + skills + tools. Karpathy then reframed it with a computer architecture analogy, and that's the interesting part. Here's the mapping: The LLM (like Claude's underlying model) is the CPU — the thing that actually does the processing. But instead of crunching precise binary operations, it processes tokens (words/chunks of text) in a statistical, probabilistic way. It's the raw reasoning engine. The agent is the operating system kernel — the layer that sits on top of the CPU and actually makes it useful. Your OS kernel manages memory, schedules tasks, handles I/O with the outside world. Without it, the CPU is powerful but inert. Similarly, without the agent layer, an LLM just completes text. The agent layer is what gives it the ability to use tools, maintain context across steps, and pursue goals. Anna extends it: the context window is RAM — the working memory the system has available right now. Tools (web search, code execution, file reading) are I/O devices — how the system interacts with the outside world. The reason Karpathy's analogy is good: traditional operating systems are deterministic — same input, same output. What we're building now are non-deterministic operating systems, where the "CPU" makes probabilistic judgments. The agent framework (tool use, memory management, task scheduling) is the kernel that makes that probabilistic processor actually functional. What you're using right now is literally this stack. Claude is the CPU. The agent layer (tool access, memory files, skills, hooks) is the OS. Your vault + Google services + web are the I/O peripherals. The context window is the RAM I'm working within. Karpathy's point is that this isn't a new idea — he sketched it in 2023. The industry is just now building out what he drew on a napkin. *L'analogia regge perché ogni sistema operativo nella storia è nato dallo stesso problema: un processore potente che non sa fare niente di utile finché qualcuno non gli costruisce intorno l'infrastruttura per parlare col mondo.*