sir-wabbit

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)

1. 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

sir-wabbit

from typing import Tuple

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

from collections import Counter, defaultdict

sir-wabbit

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

enum NodeOp {
  OP_CONST,
  OP_ADD,
  OP_MUL,

sir-wabbit

;; 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.

sir-wabbit

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

sir-wabbit

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) 

sir-wabbit

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
    }

sir-wabbit

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

enum NodeOp {
  OP_CONST,
  OP_ADD,
  OP_MUL,

sir-wabbit

@numba.jit("i4(i4, i4)", nopython=True, nogil=True)
def gcd(a, b):
    while True:
        if a == 0: return b
        if b == 0: return a
        if a == b: return a
        if b > a: 
            a, b = a, b % a
        else:
            a, b = a % b, b

sir-wabbit

sealed trait Free[+F[_], A]
object Free {
  def eval[F[_], A](fa: Free[F, A])(implicit F: Monad[F]): F[A] = {
    type State = (Free[F, Any], List[Any => Free[F, Any]])
    def go(s: State): F[Either[State, Any]] = s match {
      case (current, stack) =>
        current match {
          case Done(a) => 
            stack match {
              case Nil     =>