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This cheatsheet combines the most important commands, changes, and best practices for RouterOS v7, focusing on differences from v6. Content is organized by functional area for easy reference.

RouterOS v7 Cheat Sheet

I. General System & Updates

• Check RouterOS Version:

  /system resource print
  

Anexen

-- usage:
-- CALL update_table_in_batches('table_1', array['a = 1'], batch_size := 10);
-- CALL update_table_in_batches('table_2', array['b = 2'], track_by := 'created', initial_value := '2021-01-01'::timestamp);

CREATE OR REPLACE PROCEDURE update_table_in_batches(
    target_table text,
    set_expressions text[],
    track_by text default 'id',
    initial_value anyelement default 0,
    batch_size integer default 1000,

MuTLY

# this file contains keys needed for decryption of disc file system data (WUD/WUX)
# 1 key per line, any text after a '#' character is considered a comment
# the emulator will automatically pick the right key
541b9889519b27d363cd21604b97c67a # example key (can be deleted)
219FCB908520E9912D57444DFC136C23 # The Legend of Zelda: Breath of the Wild

566FCA3A88AA9255456635EDE688FD0B # The Legend of Zelda: Breath of the Wild EU

082fa2981e004defac03524cc685f693 # The Legend of Zelda: Breath of the Wild JPN

ARHAEEM

Stremio External Players Patcher

Production-ready PowerShell patcher for Stremio on Windows.

It auto-detects stock server.js installs, creates timestamped backups, fixes broken built-in player definitions, and adds a larger external player pack without requiring manual edits.

Quick Start

Do not double-click the .ps1 file directly.

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.

antoniopicone

#!/usr/bin/env bash
set -euo pipefail
# ---------------------------------------------------------------------------
# Intel Xe GPU driver + video acceleration setup for Fedora 44
# Target: Intel Arrow Lake-H / Arc 140T (Xe2/Xe-LPG+ iGPU), e.g. Core Ultra
# 7 255H. Should also work on other Intel Gen8+ iGPUs (Meteor Lake, Lunar
# Lake, Battlemage-based) with no changes.
#
# What this script does:
#   1. Detects the Intel GPU PCI device ID automatically (no hardcoded IDs).

aleclarson

NOTICE: I've summarized the perspectives of Octane's and Solid's creators here, for anyone short on time: https://gist.github.com/aleclarson/829c10aa7d287944ef75e1c2a90bef06

---

I'm a React/Preact/Astro developer with an interest in other renderers. The two that have most caught my eye recently are Octane (from Dominic Gannaway of React Hooks / Svelte / Inferno fame) and Solid 2.

I asked ChatGPT to compare them with its own "Renderer DX Pillars", which I've shared below. This is its gut instinct, with very little hedging:

Pillar	Winner	Why

bossmann47

Important

This process has been confirmed to work on Android using Firefox Mobile. It is unknown if this method works on other mobile browsers (like Chrome or Safari) due to varying support for extensions and camera overrides.

💻 Phase 1: PC Configuration

1. Log in to Discord via your desktop web browser.

2. Open the Developer Tools by pressing Ctrl + Shift + I.

3. Navigate to the Console tab.

qoomon

Conventional Commit Messages

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
 -m"<optional body>" \

komikndr

What it is

This is my OpenCode setup for local models, mainly using a customized llama.cpp configuration.

For Qwen 3.8, DeepSeek V4, and Glimmer, the models are already trained to support reasoning effort levels. Depending on the model, these may be exposed as low, medium, high, xhigh, or as low, high, and max.

For models that support reasoning but were not trained with explicit reasoning-effort levels, such as the Qwen 3.5 and 3.6 variants, I use a token budget to limit the amount of reasoning.

Although Qwen 3.8 has built-in reasoning-effort levels, I still apply a maximum reasoning-token cap for each effort level.

Although the llama.cpp CLI flags specify preserve_thinking and a default reasoning budget, these can still be overridden through the API, so this works fine for my setup.