Sir Wabbit sir-wabbit

NOTE: "complexity" is somewhat vaguely defined here. The real distinction is whether we

expect an immediate return,
expect some non-trivial work that is a function of input size or object size
expect I/O-bound work

Name / Pattern	Intended use	I/O allowed	Typical complexity expectation	Can mutate `this`	Exceptions: allowed by default	"Expected failure" alternative
`of(...)`	Construct from domain parts or a domain identifier (validated factory)	No	Smal

You want what actually works for learning hard things—methods with causal evidence, not folklore. Here’s the short list that consistently moves the needle, with RCTs/meta‑analyses behind it and notes on where each shines (and where it backfires).

What works (ranked by strength + transferability)

Retrieval practice (“testing effect”), ideally with feedback
What it is: Actively recall (without notes), get the answer, then check/correct.
Evidence: Big, durable gains across hundreds of experiments and classroom trials; practice tests outperform restudy and other controls. Effects are larger when feedback is provided and when retrieval is spaced. Meta‑analyses with college‑level materials included. Use for: all levels; concepts, proofs, derivations, formulas. SAGE Journals[education.wsu.edu](https://education.wsu.edu/documents/2018/01/rethi

	#!/usr/bin/env python3
	"""
	Get a full YouTube transcript in English (manual or auto) by URL/ID.
	- Tries native English first; if absent, translates another language to English.
	- Supports TXT/SRT/VTT/CSV/JSON outputs.
	- Optional: preserve HTML formatting, proxy config.

	Requires: youtube-transcript-api >= 1.2.0
	pip install -U youtube-transcript-api

	from typing import Tuple

	import mmap
	import struct
	import os
	from pathlib import Path
	import re

	from collections import Counter, defaultdict

	#include <iostream>
	#include <cmath>
	#include <vector>
	#include <cassert>
	#include <memory>

	enum NodeOp {
	OP_CONST,
	OP_ADD,
	OP_MUL,

	;; stage :: (name : String) -> (description : String) -> Stage

	;; principle :: (name : String) -> (description : String) -> Principle
	;; strategy :: (name : String) -> (description : String) -> (parent_principles: List String) -> Strategy
	;; tactic :: (name : String) -> (description : String) -> (parent_strategies_and_principles: List String) -> Tactic

	; The Principle-Strategy-Tactic axis represents a hierarchical framework for reasoning and problem-solving, moving from abstract, foundational beliefs to concrete, actionable steps.

	; Principles are the most abstract level, representing foundational beliefs or philosophies that guide reasoning and decision-making across various contexts. They are timeless and universal, providing a conceptual framework from which strategies and tactics can be developed. Principles are not directly actionable but serve as the bedrock for strategic thinking and tactical execution.

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.optim as optim

	import numpy as np
	import matplotlib.pyplot as plt

	##########################################################
	# Generating training and validation data

	import geocoder
	import geopy.distance
	import requests
	import time
	import json

	def now():
	from datetime import timezone
	import datetime
	dt = datetime.datetime.now(timezone.utc)

	sealed class StableName[A] extends Serializable
	object StableName {
	private[this] final class RefInstance[A <: AnyRef](val value: A) extends StableName[A] {
	override def clone(): AnyRef = this

	override def equals(obj: Any): Boolean = obj match {
	case that: RefInstance[_] => that.value eq this.value
	case _ => false
	}