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@johnsimcall
johnsimcall / nncp-node1.yaml
Last active August 12, 2026 16:29
NodeNetworkConfigurationPolicy (NNCP) example
# 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:

Documento de Casos de Teste - Sistema McBugs

Sistema: McBugs - Totem de Autoatendimento
Data de Criação: 2025-01-27
Versão: 1.0


Índice

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.

@smontlouis
smontlouis / index.html
Created August 12, 2026 09:54
please don't sue me
<!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>"
/>
@karpathy
karpathy / microgpt.py
Last active August 12, 2026 16:08
microgpt
"""
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
@jinjier
jinjier / javdb-top250.md
Last active August 12, 2026 15:52
JavDB top 250 movies list. [Updated on 2026/01]
@Anahkiasen
Anahkiasen / Curl.php
Last active August 12, 2026 15:42
curl
<?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