- place the
autossh@.servicefile in /etc/systemd/system - enable an instance -- suppose your remote host is called
frodothen you might run:systemctl enable autossh@frodo(do not use--nowhere!); IPv4 addresses also worked for me - now it is time to create an override specific to the instance, e.g. :
systemctl edit autossh@frodo.serviceand adjust the following bunch of settings (commented out in the main unit template) like so:[Service] Environment=REMOTE_PORT=1022 Environment=TARGET_USER=username Environment=TARGET_HOST=frodo.example.com
Discover gists
| # SPDX-License-Identifier: GPL-2.0-or-later OR MIT | |
| # | |
| # how to build this: | |
| # | |
| # $ as -o check-new-world.tiny.o check-new-world.tiny.s | |
| # $ ld check-new-world.tiny.o -o check-new-world.tiny.tmp | |
| # $ objcopy -O binary --only-section=.blob ./check-new-world.tiny.tmp ./check-new-world.tiny | |
| .section ".blob", "aw", @progbits |
This was originally shared on reddit, but for some reason I cannot update it anymore. I've now mirrored it as this GitHub Gist.
For whatever reason, it's really difficult to discover alternative launchers for Android TV. Sure, you can find a couple of them, but there are so many terrible results that you will end up wasting quite some time.
Thus, I've compiled this list of alternative Android TV launchers. It's not comprehensive (I'm certain I missed some, please suggest them in comments); I'm not endorsing any particular one; and I have not tried all of them. Some are free, some are paid. Some contain ads. Some contain IAP (in-app purchases). Some require too many intrusive permissions.
| --- ----------------- ---- | |
| Map Quick Explanation Link | |
| --- ----------------- ---- | |
| < <F1> Causes Netrw to issue help | |
| <cr> Netrw will enter the directory or read the file |netrw-cr| | |
| <del> Netrw will attempt to remove the file/directory |netrw-del| | |
| <c-h> Edit file hiding list |netrw-ctrl-h| | |
| <c-l> Causes Netrw to refresh the directory listing |netrw-ctrl-l| | |
| <c-r> Browse using a gvim server |netrw-ctrl-r| | |
| <c-tab> Shrink/expand a netrw/explore window |netrw-c-tab| |
| /* ========================================================= | |
| OBSIDIAN E-INK THEME | |
| ========================================================= */ | |
| /* --------------------------------------------------------- | |
| CORE COLORS | |
| --------------------------------------------------------- */ | |
| body { |
This guide walks you through creating a high-performance Windows 11 virtual machine (VM) on Fedora Silverblue using GPU passthrough technology. This setup allows you to run Windows applications that require dedicated graphics performance while keeping your primary system secure with Fedora Silverblue.
GPU passthrough allows your virtual machine to use your dedicated graphics card directly, providing near-native gaming and graphics performance in Windows while running on Linux. This is different from typical virtualization where graphics performance is limited.
Key terms you'll encounter:
To enable developer options, tap the Build Number option 7 times. You can find this option in one of the following locations, depending on your Android version:
Android 9 (API level 28) and higher: Settings > About Phone > Build Number
- Enable Wireless Debugging: Settings > search Wireless Debugging.
| *&---------------------------------------------------------------------* | |
| *& Report UI5_REPOSITORY_LOAD | |
| *& | |
| *&---------------------------------------------------------------------* | |
| *& | |
| *& This report implements the up- and download of a SAPUI5 application | |
| *& into an UI5 Repository. | |
| *& | |
| *& Furthermore it is possible to delete a UI5 Repository | |
| *& |
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.
