| #!/bin/bash | |
| # ============================================================================== | |
| # KEYCHRON LINUX FIX FOR 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. |
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
By @cereblab — Independent AI Safety Checker. Reproduce it yourself: github.com/cereblab/grok-build-exfil-repro
A measured, reproducible teardown. Findings are backed by captured artifacts (endpoint, HTTP method, status code, byte size, host) and repro commands; where an observation was seen live but not retained as a file, §7 says so explicitly. Section 8 is an evidence appendix with SHA-256s and a "what we did not prove" list. All captures are of my own traffic on my own machine, using a throwaway repository containing fake "canary" secrets — no real credentials were exposed.
Want to inject some flavor into your everyday text chat? You're in luck! Discord uses Markdown, a simple plain text formatting system that'll help you make your sentences stand out. Here's how to do it! Just add a few characters before & after your desired text to change your text! I'll show you some examples...
What this guide covers:
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.
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.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A curated collection of the best design inspiration websites for UI/UX designers, web designers, and creative professionals.
Source: Medium Article by Sharanya