reyjrar

##################################################################
# /etc/elasticsearch/elasticsearch.yml
#
# Base configuration for a write heavy cluster
#

# Cluster / Node Basics
cluster.name: logng

# Node can have abritrary attributes we can use for routing

touilleMan

#!/usr/bin/env python3
 
"""Simple HTTP Server With Upload.

This module builds on BaseHTTPServer by implementing the standard GET
and HEAD requests in a fairly straightforward manner.

see: https://gist.github.com/UniIsland/3346170
"""
 

stdrc

+ (NSString *)unicodeStringWithString:(NSString *)string {
    NSString *result = [NSString string];
    for (int i = 0; i < [string length]; i++) {
        result = [result stringByAppendingFormat:@"\\u%04x", [string characterAtIndex:i]];
        /*
         因为 Unicode 用 16 个二进制位（即 4 个十六进制位）表示字符,对于小于 0x1000 字符要用 0 填充空位,
         所以使用 %04x 这个转换符, 使得输出的十六进制占 4 位并用 0 来填充开头的空位.
         */
    }
    return result;

qti3e

About

This file was generated automatically based on this two sources:

1. /etc/nginx/mime.types
2. http://www.garykessler.net/library/file_sigs.html

File format

This is a JSON object by following structure:

[string ext] : {
 signs: [sign]

StephenNneji

import numpy
from PyQt5 import QtCore, QtWidgets, QtGui
Qt = QtCore.Qt


class NumpyModel(QtCore.QAbstractTableModel):
    def __init__(self, narray, parent=None):
        QtCore.QAbstractTableModel.__init__(self, parent)
        self._array = narray.point
        self.test = narray.enabled

y0ngb1n

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

1️⃣ Docker daemon 配置代理（推荐）

参考 Docker daemon 配置代理

YuriyGuts

#!/usr/bin/env python3
"""
Expose Ollama models to LM Studio by symlinking its model files.
NOTE: On Windows, you need to run this script with administrator privileges.
"""

import json
import os
from pathlib import Path

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.