karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

bit-cook

"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

emschwartz

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

cablej

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

• NEVER use meta-phrases (e.g., "let me help you", "I can see that").
• NEVER summarize unless explicitly requested.
• NEVER provide unsolicited advice.
• NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
• ALWAYS be specific, detailed, and accurate.

jlia0

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

vikingmute

1. 常用药物

感冒/发烧类

• 布洛芬（退烧、止痛）
• 对乙酰氨基酚（泰诺）

抗病毒/流感类

• 奥司他韦（达菲，需处方）

消炎类

• 阿莫西林（需处方）
• 红霉素软膏（外用）

ninehills

lidangzzz

class LinearRegression {
    w: Mat;
    constructor() { }

    //x: M-by-N matrix. M data with N dimensions. Each row is an N-dim vector
    //y: M-by-1 matrix
    fit(x_: Mat, y_: Mat) : LinearRegression{
        let y = y_;        
        if (y_.rows != 1 && y_.cols == 1) {y = y_.T();}  //check the dimension of y
        var x = x_.resize(x_.rows, x_.cols + 1, 1);   //expan x_ with one more column with 1

nickfox-taterli

-- 只扫原生WS,不要太恐慌.

local stdnse = require "stdnse"
local http = require "http"

categories = { "default", "discovery", "safe" }

portrule = function(host, port)
	return true
end