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## nncp-node1.yaml
# https://docs.openshift.com/container-platform/4.11/networking/k8s_nmstate/k8s-nmstate-updating-node-network-config.html

apiVersion: nmstate.io/v1
kind: NodeNetworkConfigurationPolicy
metadata:
  name: node1
spec:
  nodeSelector:
    kubernetes.io/hostname: node1.example.com
  desiredState:

## mcbugs-casos-de-teste.md

      
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              1 fork
            
          
                0 comments
              
            
              1 star
            
          
                fernandopapito
                / mcbugs-casos-de-teste.md
            
            
              Created
              February 7, 2026 13:08
            
              
                MCBugs Prompts
              
          
    Documento de Casos de Teste - Sistema McBugs

Sistema: McBugs - Totem de Autoatendimento

Data de Criação: 2025-01-27

Versão: 1.0

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## llm-wiki.md

      
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                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    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.

  
## index.html
<!doctype html>
<html lang="fr">
  <head>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width,initial-scale=1" />
    <meta name="color-scheme" content="light dark" />
    <link
      rel="icon"
      href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 64 64%22><circle cx=%2232%22 cy=%2232%22 r=%2230%22 fill=%22%235b7fe5%22/><circle cx=%2224%22 cy=%2228%22 r=%225%22/><circle cx=%2240%22 cy=%2228%22 r=%225%22/></svg>"
    />

## microgpt.py
"""
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

## javdb-top250.md

      
              3 files
            
          
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                jinjier
                / javdb-top250.md
            
            
              Last active
              August 12, 2026 15:52
            
              
                JavDB top 250 movies list. [Updated on 2026/01]
              
          
    使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

JavDB TOP250
JavDB 有码 TOP250
JavDB 无码 TOP250
JavDB 欧美 TOP250
JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

  
## Curl.php
<?php
/**
 * Object-oriented wrapper for CURL
 */
class Curl
{
	/**
	 * The internal CURL instance
	 *
	 * @var resource

## canales.m3u
#EXTM3U
#EXTINF:0 group-title="General",13 C
#EXTGRP:General
#EXTVLCOPT:network-caching=1000
http://181.78.79.131:8000/play/a0pm
#EXTINF:0 group-title="Entretenimiento",A&E HD
#EXTGRP:Entretenimiento
#EXTVLCOPT:network-caching=1000
http://181.78.79.131:8000/play/a0mt
#EXTINF:0 group-title="Entretenimiento",AE Mundo
	# https://docs.openshift.com/container-platform/4.11/networking/k8s_nmstate/k8s-nmstate-updating-node-network-config.html

	apiVersion: nmstate.io/v1
	kind: NodeNetworkConfigurationPolicy
	metadata:
	name: node1
	spec:
	nodeSelector:
	kubernetes.io/hostname: node1.example.com
	desiredState:
	<!doctype html>
	<html lang="fr">
	<head>
	<meta charset="utf-8" />
	<meta name="viewport" content="width=device-width,initial-scale=1" />
	<meta name="color-scheme" content="light dark" />
	<link
	rel="icon"
	href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 64 64%22><circle cx=%2232%22 cy=%2232%22 r=%2230%22 fill=%22%235b7fe5%22/><circle cx=%2224%22 cy=%2228%22 r=%225%22/><circle cx=%2240%22 cy=%2228%22 r=%225%22/></svg>"
	/>
	"""
	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
	<?php
	/**
	* Object-oriented wrapper for CURL
	*/
	class Curl
	{
	/**
	* The internal CURL instance
	*
	* @var resource
	#EXTM3U
	#EXTINF:0 group-title="General",13 C
	#EXTGRP:General
	#EXTVLCOPT:network-caching=1000
	http://181.78.79.131:8000/play/a0pm
	#EXTINF:0 group-title="Entretenimiento",A&E HD
	#EXTGRP:Entretenimiento
	#EXTVLCOPT:network-caching=1000
	http://181.78.79.131:8000/play/a0mt
	#EXTINF:0 group-title="Entretenimiento",AE Mundo