Interactive row selection prototype with Datasette

I added a new llms tag to my blog, for my content about Large Language Models.

I wanted to quickly populate it with content. I decided to run some SQL queries to find likely candidates using the Datasette backup of my blog's content.

But... I didn't just want to add anything that mentioned "LLM" - I wanted to have a step where I curated the selected items and picked just the ones that were a good fit.

What I really needed was a UI that could let me select items from a Datasette list.

So I built one with ChatGPT. The sequence of prompts I used was:

Here's the full ChatGPT transcript.

This gave me the exact code I was looking for. I made a couple of tiny tweaks (adding some margin next to the checkboxes and updating the CSS selector to grab the table), then pasted that modified code back in and said:

The resulting code looked like this:

const table = document.querySelector('table');
const rows = table.getElementsByTagName('tr');
let lastChecked = null;

function prependCheckboxes() {
  for (let i = 0; i < rows.length; i++) {
    const checkbox = document.createElement('input');
    checkbox.type = 'checkbox'; = '8px';

    if (i === 0) {
      checkbox.addEventListener('click', handleFirstCheckboxClick);
    } else {
      checkbox.addEventListener('click', handleCheckboxClick);

    const firstCell = rows[i].cells[0];
    firstCell.insertBefore(checkbox, firstCell.firstChild);

function handleFirstCheckboxClick(e) {
  const mainCheckbox =;
  const checkAll = mainCheckbox.checked;

  for (let i = 1; i < rows.length; i++) {
    const row = rows[i];
    const currentCheckbox = row.cells[0].querySelector('input[type="checkbox"]');
    currentCheckbox.checked = checkAll;


function handleCheckboxClick(e) {
  const checkbox =;
  let inBetween = false;

  if (e.shiftKey && checkbox.checked && lastChecked) {
    for (let i = 0; i < rows.length; i++) {
      const row = rows[i];
      const currentCheckbox = row.cells[0].querySelector('input[type="checkbox"]');

      if (currentCheckbox === checkbox || currentCheckbox === lastChecked) {
        inBetween = !inBetween;

      if (inBetween) {
        currentCheckbox.checked = true;

  if (checkbox.checked) {
    lastChecked = checkbox;
  } else if (lastChecked === checkbox) {
    lastChecked = null;


function createTextarea() {
  const textarea = document.createElement('textarea');
  textarea.rows = 5; = '100%'; = '16px';
  table.parentNode.insertBefore(textarea, table);
  return textarea;

function updateTextarea() {
  const checkedValues = [];

  for (let i = 1; i < rows.length; i++) {
    const row = rows[i];
    const currentCheckbox = row.cells[0].querySelector('input[type="checkbox"]');
    if (currentCheckbox.checked) {

  textarea.value = JSON.stringify(checkedValues);

const textarea = createTextarea();

I constructed the following SQL query:

select id, link_title, link_url, commentary
from blog_blogmark
where id in (
  select blogmark_id from blog_blogmark_tags where tag_id in (
    select id from blog_tag where tag in (
      'generativeai', 'ai', 'gpt3', 'promptengineering'

Here's that in Datasette.

Then I opened up the Firefox console and pasted in that JavaScript. Here's the result:

Animated GIF showing a table with a checkbox for each row. Checking the checkboxes updates a JSON array of IDs in a textarea at the top of the table. Shift clicking selects a range of checkboxes. A checkbox at the top can be checked to select all or deselect all.

I used this query, and two others like it, to create an array of IDs of entries, blogmarks and quotations that I wanted to add the llms tag to.

Having generated those arrays, I applied the tag using the / shell Django command running in Heroku:

heroku run bash -a simonwillisonblog

Then in Heroku:

./ shell

And in the Python console:

>>> from blog.models import Blogmark, Tag, Entry, Quotation
>>> tag = Tag.objects.get(tag='llms')
>>> tag
<Tag: llms>
>>> blogmark_ids = ["6301","6310","6815","6850","6853","6869","6876","6929","6940","6967","6969","6980","6981","6993","7018","7021","7025","7027","7029","7030","7031","7032","7036","7037","7038","7039","7040","7041","7045","7046","7047","7048","7049","7050","7052","7053","7054","7056","7057","7058","7061","7062","7063","7070","7071","7075","7076","7077","7079"]
>>> Blogmark.objects.filter(id__in=blogmark_ids)
<QuerySet [<Blogmark: gpt4all>, <Blogmark: Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models>...']>
>>> blogmarks = list(_)
>>> len(blogmarks)
>>> for b in blogmarks:
...     b.tags.add(tag)
>>> entry_ids = ["8169","8170","8171","8178","8189","8191","8192","8197","8214","8215","8217","8222","8223","8227","8229","8230","8231","8232","8233","8234","8236","8237","8238","8239","8240","8241","8242"]
>>> quotation_ids = ["767","890","893","929","933","936","937","941","942","946","947","948","950","951","952","954","955","956","957","958","959","960","961","962","963","964","965","966","968","969","970","971","972"]
>>> entries = list(Entry.objects.filter(id__in=entry_ids))
>>> quotations = list(Quotation.objects.filter(id__in=quotation_ids))
>>> for e in entries:
...     e.tags.add(tag)
>>> for q in quotations:
...     q.tags.add(tag)

And that's how I populated the page on my blog!

At some point I plan to turn this JavaScript code into a Datasette plugin.


Created 2023-03-30T16:26:44-07:00 · Edit