hubgit

import { DOMParser, type Element, } from "https://deno.land/x/[email protected]/deno-dom-wasm.ts";

const parser = new DOMParser()

const fetchDOM = async (url: string) => {
  const response = await fetch(url)
  if (!response.ok) {
    throw new Error('Response was not ok')
  }
  const html = await response.text()

hubgit

import type { NextRequest } from 'next/server'
import { createParser } from 'eventsource-parser'

export const config = {
  runtime: 'edge',
}

export default async function handler(req: NextRequest) {
  const encoder = new TextEncoder()
  const decoder = new TextDecoder()

hubgit

# brew install awscli
# aws configure
aws s3 cp your-file.pdf s3://your-bucket/your-file.pdf

# https://pypi.org/project/amazon-textract-helper/
# https://github.com/aws-samples/amazon-textract-textractor/tree/master/helper
# pip install amazon-textract-helper
amazon-textract --input-document s3://your-bucket/your-file.pdf --features TABLES --pretty-print TABLES --pretty-print-table-format=csv

# https://aws.amazon.com/blogs/machine-learning/automatically-extract-text-and-structured-data-from-documents-with-amazon-textract/

hubgit

[...document.querySelectorAll('div,main,body')].forEach(node => {
    node.style.position = 'relative'
    node.style.height = 'auto'
    node.style.overflowY = 'visible'
});

[...document.querySelectorAll('button')].forEach(node => {
    node.remove()
});

hubgit

get_iplayer --pid m001d2h4 --subtitles --output "m001d2h4"        
ffmpeg -i m001d2h4/Only_Connect_Series_18_-_07._Scrummagers_v_Crustaceans_m001d2h4_original.mp4 -vf "subtitles=m001d2h4/Only_Connect_Series_18_-_07._Scrummagers_v_Crustaceans_m001d2h4_original.srt" -ss 17:49 -t 5 -copyts output.mov

hubgit

lineReader = () => {
  let buffer = "";

  return new TransformStream({
    transform(chunk, controller) {
      buffer += chunk;
      const parts = buffer.split("\n");
      parts.slice(0, -1).forEach((part) => controller.enqueue(part));
      buffer = parts[parts.length - 1];
    },

hubgit

import { parse } from 'https://deno.land/x/[email protected]/mod.ts'
import { readableStreamFromIterable } from 'https://deno.land/[email protected]/io/streams.ts'
import { Database } from 'https://deno.land/x/[email protected]/mod.ts'
import ProgressBar from 'https://deno.land/x/[email protected]/mod.ts'

let counter = 0

const progress = new ProgressBar({
  title: 'processing:',
  interval: 100,

hubgit

First, download the data files using a BitTorrent client:

aria2c https://academictorrents.com/download/4dcfdf804775f2d92b7a030305fa0350ebef6f3e.torrent

Next, convert the data files to a single newline-delimited JSON file:

deno run process.ts

hubgit

export const cloudStorageJsonLinesWriter = (url: string) => {
  // gcloud components install alpha
  const process = Deno.run({
    cmd: [
      'gcloud',
      'alpha',
      'storage',
      'cp',
      '-',
      url,

hubgit

import { TextLineStream } from 'https://deno.land/[email protected]/streams/mod.ts'

// const input = await jsonLinesReader('input.jsonl.gz')
// const output = await jsonLinesWriter('output.jsonl.gz')

// for await (const item of input) {
  //// do something
  // await output.write(item)
// }