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Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

Pure ESM package

The package that linked you here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

Use ESM yourself. (preferred)
Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
Stay on the existing version of the package until you can move to ESM.

Install, build and debug a react native app in WSL2 (Windows Subsystem for Linux) and Ubuntu.

	// Fuck the addition operator. Real programmers work in binary!
	function add(a, b) {
	// XOR to get the sum of the bits
	var sum = a ^ b;

	// "Carry" bits are common to both numbers
	var carry = (a & b) << 1;

	if (sum & carry) {
	// Rinse and repeat until there are no leftover bits

	#!/bin/sh
	# Examples:
	# ./image64.sh myImage.png
	# outputs: data:image/png;base64,xxxxx
	# ./image64.sh myImage.png -img
	# outputs: <img src="data:image/png;base64,xxxxx">

	filename=$(basename $1)
	xtype=${filename##*.}
	append=""

	#include <stdio.h>
	#include <stdlib.h>

	void merge(int *array, int p, int q, int r)
	{
	// Declaracion de variables
	int i, j, k;
	int n_1 = (q - p) + 1;
	int n_2 = (r - q);
	int L, R;

	from datasets import load_dataset
	from trl import SFTTrainer
	from peft import LoraConfig
	from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
	tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
	model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
	dataset = load_dataset("Abirate/english_quotes", split="train")
	training_args = TrainingArguments(
	output_dir="./results",
	num_train_epochs=3,

	import streamlit as st
	import concurrent.futures # We'll do computations in separate processes!
	import mymodule # This is where you'll do the computation

	# Your st calls must go inside this IF block.
	if __name__ == '__main__':
	st.write("Starting a long computation on another process")

	# Pick max number of concurrent processes. Depends on how heavy your computation is, and how
	# powerful your machine is.