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ENARM 2026–2027 — Guía ALL‑IN‑ONE (Manual profesional para r/ENARM)

Versión: 2026‑02‑12 (GMT‑6)
Propósito: Esta wiki es un manual integral: definición, marco oficial, estrategia, temario por materias, calendarios, simulacros, recursos, apps y plantillas.

Estado 2026 (importante): la información administrativa (fechas, costo, sedes, requisitos, reglas) es válida solo cuando se publica en el portal oficial de la CIFRHS. Si ENARM 2026/2027 no está publicado ahí, no es oficial. (Revisa siempre la sección “Enlaces oficiales”).

0) Cómo usar esta wiki (en 60 segundos)

Admin/Proceso: solo CIFRHS (convocatoria, addendum, guías, instructivo).

ENARM 2026–2027 — Guía ALL‑IN‑ONE (Manual profesional para r/ENARM)

Versión: 2026‑02‑12 (GMT‑6)
Propósito: Esta wiki es un manual integral: definición, marco oficial, estrategia, temario por materias, calendarios, simulacros, recursos, apps y plantillas.

Estado 2026 (importante): la información administrativa (fechas, costo, sedes, requisitos, reglas) es válida solo cuando se publica en el portal oficial de la CIFRHS. Si ENARM 2026/2027 no está publicado ahí, no es oficial. (Revisa siempre la sección “Enlaces oficiales”).

0) Cómo usar esta wiki (en 60 segundos)

Admin/Proceso: solo CIFRHS (convocatoria, addendum, guías, instructivo).

NanoClaw — Personal Claude Assistant

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.

What Is This?

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.

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.

	Windows Registry Editor Version 5.00

	[HKEY_LOCAL_MACHINE\SOFTWARE\Classes\Directory\background\shell\Command Prompt *]

	[HKEY_LOCAL_MACHINE\SOFTWARE\Classes\Directory\background\shell\Command Prompt *\command]
	@="cmd.exe /s /k pushd \"%V\""

	#!/bin/bash

	# requires jq
	# arg 1: iCloud web album URL
	# arg 2: folder to download into (optional)

	function curl_post_json {
	curl -sH "Content-Type: application/json" -X POST -d "@-" "$@"
	}

	// ==UserScript==
	// @name Typing Site Redirector (TypeLit Focus Mode)
	// @namespace gobdovan
	// @version 2.1
	// @description Saves your last non-root TypeLit page and redirects you there if you open time-wasting typing sites
	// @match ://.typeracer.com/*
	// @match ://typeracer.com/
	// @match ://.monkeytype.com/*
	// @match ://monkeytype.com/
	// @match ://.10fastfingers.com/*