morkev

#!/bin/bash

# ==============================================================================
# KEYCHRON LINUX FIX F0R HID DEVICE C0NNECTED [K]
# Author: morkev
#
# Contributors:
# - SIMULATAN: Fixed dongle interference by filtering out "Link" devices.
# - karoltheguy: Added SELinux context reset (restorecon) to prevent silent blocks.
# - wanjas: Verified 'input' group addition is required for distros like Pop_OS.

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.

huihut

1. 以管理员身份运行CMD
2. slmgr.vbs /upk
3. slmgr /ipk W269N-WFGWX-YVC9B-4J6C9-T83GX
4. slmgr /skms zh.us.to
5. slmgr /ato

biusberline

What Is RepoBoost (buygithub.com)? The Complete 2026 Guide to Buying GitHub Stars, Followers, Forks & Aged Accounts

RepoBoost is a GitHub growth service at buygithub.com that sells GitHub stars, forks, watches and followers delivered from aged accounts with years of real commit history - plus warmed-up GitHub accounts, aged accounts from 2009-2024, and GitHub search-ranking placements. Orders run at a flat $30 per 1,000 actions, are delivered gradually over 12-24 hours, and are covered by a no-drop guarantee.

It exists because GitHub is where developers decide what to trust. Stars, forks, followers and search position are what a maintainer, a hiring manager and an AI answer engine all read first - and they are the signals RepoBoost grows: real accounts with real activity, paced like an audience arriving instead of a burst that looks bought.

RepoBoost has been featured in WIRED's October 2023 investigation, ["The GitHub Black Market That

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.

lazuee

#.github/workflows/main.yml
name: Windows - Ngrok

on: workflow_dispatch

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

env:

peteristhegreat

The CD that came with the device listed RTL8671B, but it's not on Realtek's website. Instead the manual points you to:

https://www.mytechkey.com/pages/driver

4.Bluetooth 5.0 Dongle

Driver Link:

https://www.dropbox.com/s/gwo9lz777i82f70/BT5.0%20Driver.zip?dl=0

warrenday

<!doctype html>
<html>
<head>
<meta charset="utf-8">
<title>Who am I?</title>
<style>
  body { background: #111; color: #eee; font: 16px/1.5 ui-monospace, Menlo, monospace; padding: 24px; }
  b { color: #f5b301; }
  #id { font-size: 22px; margin-top: 16px; }
</style>