Minikloon

import net.kyori.adventure.text.Component;
import net.kyori.adventure.text.TextComponent;
import net.kyori.adventure.text.format.NamedTextColor;
import net.kyori.adventure.text.format.Style;
import net.kyori.adventure.text.format.TextDecoration;
import net.minestom.server.utils.StringUtils;

import java.util.*;

public class ComponentWrapper {

pabloko

Using Edge WebView2 is really straightforward, but using the Composition controller is poorly documented and only discussed on few issues in the WebView2 repo.

The browser itself is rendered on a separate process using DirectComposition API wich does not directly interoperate with DirectX, but can be readed using GraphicsCaptureItem WinRT apis. This means that a capture session must be setup into the host visual root, and it will provide accelerated GPU texture capture of the browser (with transparency)

There are few extra steps to setup this pipeline under win32 apps, apart of usual WebView2 setup:

1. A DispatcherQueueController need to be created in the hosting thread in order to be able to use the Visual interfaces
2. Use CreateCoreWebView2CompositionController instead CreateCoreWebView2Controller or its WithConfig equivalent.
3. Create a WinRT DirectComposition IContainerVisual and IVisual as in the example
4. Create a WinRT Direct3D11CaptureFramePool and start the session using `GraphicsC

Maciejdziuba

The Software Factory Playbook — Dex Horthy's 4-gate workflow as a skill

A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.

"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."

By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.

What it does

aamiaa

Caution

As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.

Some users have received the following system message:

There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.

Complete Recent Discord Quest

rafaelcalleja

Discord E-Mail & Phone Number/SMS Verification Workarounds

Info

Useful links:

• Calling codes: https://en.wikipedia.org/wiki/List_of_country_calling_codes#Overview
• List of SMS verification sites: https://www.reddit.com/r/FREEMEDIAHECKYEAH/wiki/storage/#wiki_sms_verification_sites

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/

Phone

UPDATE - I now just use https://5sim.net and choose England as the country (it works the best for me)!

andreagrandi

#!/bin/bash

# Claude Code Status Line Script
# Displays project info, context usage, session cost, and environment details.
#
# Session cost/credits usage is read from cost.total_cost_usd (the client-side
# estimated spend for the current session). The 5h/7d rate-limit usage is
# intentionally omitted: this is an enterprise account, where those fields are
# not populated. Monthly/total spend is not available locally -- the Console
# dashboard is the authoritative source for that figure.

franklinrony

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.

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.