| Filter | Description | Example |
|---|---|---|
| allintext | Searches for occurrences of all the keywords given. | allintext:"keyword" |
| intext | Searches for the occurrences of keywords all at once or one at a time. | intext:"keyword" |
| inurl | Searches for a URL matching one of the keywords. | inurl:"keyword" |
| allinurl | Searches for a URL matching all the keywords in the query. | allinurl:"keyword" |
| intitle | Searches for occurrences of keywords in title all or one. | intitle:"keyword" |
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Last Edited: Mar 30, 2026 @ 7:45AM PST · Supports iOS versions 15.0 - 26.3
Make sure you are always using up-to-date guides to ensure full compatibility. The official SideStore documentation can be found here in case anything changes. For additional information and credits, scroll to the bottom of this page.
Important • iOS 26.4 broke the method used by SideStore and this tutorial to refresh apps. A new guide will come soon for people on iOS 26.4 once the necessary tools are available.
👋 Hello again, r/sideloaded!
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.
| <# : | |
| :: I wonder who made Task Manager... | |
| @setlocal DisableDelayedExpansion | |
| @echo off | |
| echo "%*" | find /i "-el" >nul && set _elev=1 | |
| set _PSarg="""%~f0""" -el |
This tutorial will teach you how to set up a Telegram MTProxy on an Ubuntu 22.04 sever using AWS Lightsail, although you can use any other Linux distribution and cloud provider.
Using a Telegram proxy is a safe, easy and effective way of overcoming Telegram bans. It's useful, for example, to keep using Telegram under tyrannical regimes, or to circumvent judges' decisions to block Telegram.
Telegram proxies are a built-in feature in all Telegram apps (both mobile and desktop). It allows Telegram users to connect to a proxy in just one or two clicks / taps.
Telegram proxies are safe: Telegram sends messages using their own MTProto secure protocol, and the proxy can only see encrypted traffic – there's no way for a proxy to decrypt the traffic and read the messages. The proxy does not even know which Telegram users are using the proxy, all the proxy sees is just a list of IPs.
Preface: this walkthrough is a hand-holdy step by step tutorial to enable SocketCan on your WSL2 instance (Ubuntu 20.04).
To enable SocketCAN's can-utils on WSL we need to enable the CAN interface module in the WSL, to do so requires a re-building of the WSL kernel.
Requirements:
- WSL2
- back up your wsl image (optional) follow here (wsl2-backup-and-restore-images) Steps: First we will update wsl and our linux to the latest versions.