Useful links:
As some of these sites are a bit sketchy, make sure to keep privacy & security measures. Learn more here: https://www.privacyguides.org/en/desktop-browsers/
UPDATE - I now just use https://5sim.net and choose England as the country (it works the best for me)!
This method demonstrates how to activate Windows using PowerShell in administrator mode.
Open PowerShell as an administrator:
Win + S, type "PowerShell," then right-click on Windows PowerShell and select Run as administrator.Run the following command:
const addBookmarkBtn = document.getElementById("add-bookmark");
const bookmarkList = document.getElementById("bookmark-list");
const bookmarkNameInput = document.getElementById("bookmark-name");
const bookmarkUrlInput = document.getElementById("bookmark-url");
| """ | |
| 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 |
You can jump straight to the steps here.
Recently I bought my first ever iPhone, and wanted to transfer everything from my (not so) old Android phone. I tried to transfer WhatsApp chats over from my Android phone to iOS, using the official Move to iOS app by Apple.
An image I stole from Google Images showing the same problem.
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.
| Meta (Instagram, Facebook) | |
| // Узлы | |
| 157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32 | |
| 157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38 | |
| 57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97 | |
| // Подсети | |
| 213.102.128.0/24 | |
| 204.15.20.0/22 | |
| 199.201.0.0/16 |
All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.
Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.
elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent