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.
I just put the finishing touches on my Raspberry Pi 3 emulation machine running RetroArch. I was not a huge fan of RetroPie due to the reliance on Emulation Station - more moving parts meant that there were more things that could potentially break. I just wanted something that would run raw RetroArch, no frills.
This tutorial is mostly recreated from memory and was most recently tested with a Raspberry Pi 3 running Raspbian Stretch and RetroArch 1.7.7. If there is a mistake or a broken link, PLEASE message me and I will fix it.
I used Raspbian Stretch Lite from this page. Write the image to your SD card using something like Win32 Disk Imager, or if you're using OSX/Linux follow a tutorial on how to write the image using dd.
I just put the finishing touches on my Raspberry Pi 3 emulation machine running RetroArch. I was not a huge fan of RetroPie due to the reliance on Emulation Station - more moving parts meant that there were more things that could potentially break. I just wanted something that would run raw RetroArch, no frills.
This tutorial is mostly recreated from memory and was most recently tested with a Raspberry Pi 3 running Raspbian Stretch and RetroArch 1.7.7. If there is a mistake or a broken link, PLEASE message me and I will fix it.
I used Raspbian Stretch Lite from this page. Write the image to your SD card using something like Win32 Disk Imager, or if you're using OSX/Linux follow a tutorial on how to write the image using dd.
Generic runbook for connecting Hermes Agent to a Buzz community (Block’s Nostr-based human+agent workspace). Written from a real self-hosted bring-up; shaped so different usage patterns can still follow the same skeleton.
This is not official Nous or Block docs. Upstream APIs still move — treat version pins and PR numbers as snapshots, not eternal gospel.
Step-by-step guide for deploying a signed Tizen web app (.wgt) to a Samsung commercial (B2B) display. Written from hard-won experience with a Samsung OH46DX (Tizen 8.0 / SSSP 10). Most of this applies to any Samsung SMART Signage display running Tizen 6.5+.
Key fact: This is a commercial signage display, not a consumer TV. Most Tizen tutorials online target consumer TVs and will not work. On the OH46DX, installing a Custom App via the signage App Management /
Guide for myself and others to get RetroArch running on the new Raspberry Pi 4 while projects like RetroPie get an image out for the rpi4.
Disclaimer: I am not an expert and this may not be the most optimal build possible, but it works.
Inspiration taken from:
This is a list of C++ operators that can be overloaded and their normal signatures(a.k.a what an int would do). The order is the preffered order to use them(The first one listed is often preffered)
T operator+( T const & lhs, T const & rhs )T operator+( T const & rhs ) constT operator+( ) const
| --[[ | |
| Copyright (C) 2023 kevincs | |
| This program 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 2 | |
| of the License, or (at your option) any later version. | |
| This program is distributed in the hope that it will be useful, | |
| but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| GNU General Public License for more details. |
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.