Brajesh2022

🚀 Recommended Installation (Auto-Installer)

The easiest and community-recommended way to install Antigravity CLI on Termux is using the standalone auto-installer.

curl -fsSL https://raw.githubusercontent.com/wallentx/antigravity-cli-termux/dev/install.sh | bash

How it works: This script instantly installs the latest release directly from the wallentx/antigravity-cli-termux repository. A huge thanks to the original repository owner, @wallentx, for packaging these releases! The repository automatically runs a GitHub Action every six hours that fetches the official binary and patches it using the exact methods outlined in this guide below.

dmitry-osin

Полная шпаргалка по HTML

Содержание

1. Введение в HTML
2. Структура HTML-документа
3. Элементы и теги HTML
4. Атрибуты HTML
5. Текстовые элементы
6. Списки
7. Ссылки

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>" \
  -m"<optional footer>"

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.

cnemri

Write a complete, production-ready, single-file HTML/JavaScript application that renders a highly detailed, photo-realistic, navigable 3D scene of the iconic cliffside village of Sidi Bou Said, Tunisia using Three.js.

CRITICAL INSTRUCTIONS — NO LAZY CODE

• Do NOT use any external asset URLs (no external .gltf, .obj, .jpg, or .png files) as they can break or fail CORS. All textures, heights, and models must be generated dynamically and procedurally within the script (e.g., using HTML Canvas to draw textures, procedural noise algorithms for plaster and stone, or mathematical structures for 3D meshes).
• Do NOT write placeholder comments, truncated code blocks, "// TODO" markers, or "left as an exercise" shorthand. Every single function, shader, loop, and variable must be written out in its entirety.
• The output must be a single, copy-pasteable HTML file that runs perfectly immediately when opened in a browser.

TECHNICAL REQUIREMENTS & FEATURES

1. Libraries: Load Three.js and OrbitControls vi

pixelfolio

Xcode 27 ships Apple's docs as an on-device vector-search SQLite DB

Xcode 27's Developer Documentation component (Xcode → Settings → Components) is not a bundle of .doccarchives. It's an on-device vector-search SQLite database, the RAG index behind Xcode's AI documentation search. You can read it directly.

Why this is interesting for agentic / AI-assisted coding:

• It's a local, queryable corpus of the installed Xcode Developer Documentation, so you can ground a coding agent on it with no scraping and no network.

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

Mike-Schvedov

using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class PlayerMovement : MonoBehaviour
{
    public CharacterController controller;
 
    public float speed = 12f;
    public float gravity = -9.81f * 2;

mkbctrl

Intent Recognition and Auto‑Routing in Multi-Agent Systems

Modern conversational AI systems often split functionality into multiple tools or sub-agents, each specialized for a task (e.g. search, booking, math, etc.). When a user sends a query, the system must interpret intent and dispatch it to the right tool/agent. There are two broad approaches: letting a general-purpose LLM handle intent detection itself, or using a dedicated router component. In practice, many practitioners use a hybrid: an initial “router” classifies the intent and then a specialized agent or tool handles the task. Below we survey best practices and examples of each approach, referencing frameworks like LangChain and Semantic Router.

LLM-Based Intent Recognition (General-Agent Approach)

A common approach is to have the LLM itself decide which tool or chain to invoke. For example, one can prompt the model to output a JSON field indicating the desired “tool” or “function” (using OpenAI’s function-calling or ChatGPT Pl