pisculichi

# alias radio='function __radio(){ r=`grep -v "#" radios_nacionales.txt | grep -m 1 -i $1 | cut -d" " -f1`; cvlc $r 2> /dev/null; }; __radio'
# podria utilizarse mplayer en vez de vlc

AMs Nacionales

http://cdn.instream.audio:9288/stream                                       Radio Madre         530
https://streaming1.hostingmontevideo.com:7019/;                             Radio Colonia       550
http://server.laradio.online:25224/live.mp3                                 Radio Argentina     570
http://playerservices.streamtheworld.com/api/livestream-redirect/CONTINENTAL_SC                        Continental         590
https://streammax.alsolnet.com/radiorivadavia                               Rivadavia           630

aasmith

BIOS Setup
----------

1) Boot into the BIOS setup utility (press F2 or DEL) and open Advanced > Serial Port Console Redirection.
2) Disable COM1 and EMS console redirection and enable SOL redirection.
3) Set the SOL console redirection settings as follows:

       Aptio Setup Utility - Copyright (C) 2012 American Megatrends, Inc.       
          Advanced                                                              
/----------------------------------------------------+-------------------------\

pvalkone

BIOS Setup
----------

1) Boot into the BIOS setup utility (press F2 or DEL) and open Advanced > Serial Port Console Redirection.
2) Disable COM1 and EMS console redirection and enable SOL redirection.
3) Set the SOL console redirection settings as follows:

       Aptio Setup Utility - Copyright (C) 2012 American Megatrends, Inc.       
          Advanced                                                              
/----------------------------------------------------+-------------------------\

nmnmcc

NeoVim Cheat Sheet

I. Basic Concepts

1. Modes

NeoVim is a modal editor, primarily featuring the following modes:

• Normal Mode: The default mode for navigating, deleting, copying, and pasting text. Press Esc to return to this mode from others.
• Insert Mode: Used for typing text. Enter it with keys like i, a, o.

maelvls

Dealing with secrets

GNOME comes with libsecret. You can use libsecret to store your git credentials:

sudo apt install libsecret-1-0 libsecret-1-dev libglib2.0-dev
sudo make --directory=/usr/share/doc/git/contrib/credential/libsecret
git config --global credential.helper /usr/share/doc/git/contrib/credential/libsecret/git-credential-libsecret

abdivasiyev

#!/usr/bin/env bash

# checck if pidof exists
PIDOF="$(which pidof)"
# and if not - install it
(test "${PIDOF}" && test -f "${PIDOF}") || brew install pidof

# find app in default paths
CO_PWD=~/Applications/CrossOver.app/Contents/MacOS
test -d "${CO_PWD}" || CO_PWD=/Applications/CrossOver.app/Contents/MacOS

ellsies

hi all!

This script is no long maintained, however i do have a friend who has made a far better solution to this problem!

please see https://github.com/totallynotinteresting/crossover2 for more info :)

if possible could you please star his repo too?

gulrich1

#!/bin/sh

#https://github.com/PythonicNinja/jetbrains-reset-trial-mac-osx/blob/master/runme.sh

for product in IntelliJIdea WebStorm DataGrip PhpStorm CLion PyCharm GoLand RubyMine Rider; do
  echo "Closing $product"
  ps aux | grep -i MacOs/$product | cut -d " " -f 5 | xargs kill -9

  echo "Resetting trial period for $product"

gaetanars

blueprint:
  name: "Gestion filtration piscine (avancé)"
  description: "Gestion automatique de la filtration/pompe de la piscine"
  domain: automation
  homeassistant:
    min_version: 2024.6.0

  input:
    temperature_piscine:
      name: "Capteur de température de la piscine"

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.