sylvainpelissier

#!/usr/bin/env python
# -*- coding: utf-8; mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vim: fileencoding=utf-8 tabstop=4 expandtab shiftwidth=4

"""User Access Control for Microsoft Windows Vista and higher.  This is
only for the Windows platform.

This will relaunch either the current script - with all the same command
line parameters - or else you can provide a different script/program to
run.  If the current user doesn't normally have admin rights, he'll be

akabe

#!/bin/sh -xeu

sudo yum install -y epel-release
sudo yum install -y zeromq-devel

nightire

解决 Git 在 windows 下中文乱码的问题

原因

中文乱码的根源在于 windows 基于一些历史原因无法全面支持 utf-8 编码格式，并且也无法通过有效手段令其全面支持。

解决方案

1. 安装

xiaolai

.markdown-here-wrapper {
  font-size: 16px;
  line-height: 1.8em;
  letter-spacing: 0.1em;
}


pre, code {
  font-size: 14px;
  font-family: Roboto, 'Courier New', Consolas, Inconsolata, Courier, monospace;

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.