mary-ext

Bluesky's age assurance sucks, here's how to work around it.

Bluesky has implemented age verification measures in response to regional laws that restrict access, prompting users to verify their age through Epic Games' Kids Web Services before they can access adult content.

This sucks, but thankfully there are ways to work around it.

Before diving in: I encourage you to read this entire document, including the

tsvtt

• Открыть страницу с видео, видеоплеер должен быть виден на странице
• Открыть средства разработчика в браузере (F12), открыть вкладку с консолью
• Выполнить:
  document.querySelector('vk-video-player').store.actions.internal.downloadVideo()

c6p

// ==UserScript==
// @name         Reddit Multi Column
// @namespace    https://gist.github.com/c6p/463892bb243f611f2a3cfa4268c6435e
// @version      0.2.6
// @description  Multi column layout for reddit redesign
// @author       Can Altıparmak
// @homepageURL  https://gist.github.com/c6p/463892bb243f611f2a3cfa4268c6435e
// @match        https://www.reddit.com/*
// @match        https://new.reddit.com/*
// @grant        none

CypherpunkSamurai

curl "mediafire url" | grep -Eo "http.+download.?+\.mediafire\.com.*\" " | sed -e 's|"||g' | xargs wget

maxwellmlin

mv -v ~/Library/Application\ Support/.lpxuserdata ~/.Trash

twisterghost

{
	"result": {
		"heroes": [
			{
				"name": "npc_dota_hero_antimage",
				"id": 1
			},
			{
				"name": "npc_dota_hero_axe",

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.

Kautenja

Compress

tar cf - <files> -P | pv -s $(du -sb <files> | awk '{print $1}') | gzip > <some .tar.gz file>

where:

• `` is the root-mounted (i.e. starts with /) path to the files

everson

Tess-4-27B — benchlocal breakdown notes

Model: migtissera/Tess-4-27B-GGUF (Q8_0 + MTP draft + mmproj-F16)
Family: reasoning-native / agentic finetune on Qwen3.6-27B base
Launcher: /data/bin/servers/tess-4-27b.sh
Leaderboard rank: #1 local full-suite result — 81% (122/150) · ~1h10m wall

Primary artifact (Config A, best run):

• JSON: /data/shared/benchlocal-results/Tess-4-27B_full_20260708_094530.json