Snippets

We recently migrated Neurocracy from GitHub to GitLab but I wanted to keep our numerous repositories on GitHub up to date without having to push to both. Repository mirroring is relatively simple to set up once you know the exact steps required, and it allows us to push only to GitLab yet have our GitHub repositories also receive the same commits nearly instantly.

GitLab

First, we need to set up your GitLab project. Copy your project’s GitHub HTTPS Git URL (not the SSH URL); for example, for Omnipedia it would be:

https://github.com/neurocracy/omnipedia.git

Now head to your GitLab project:

1. Go to “Settings” → “Repository” and expand “Mirroring repositories”
2. Click “Add new”
3. Paste the GitHub URL from earlier into “Git repository URL”
4. Edit the URL to replace https at the start with ssh; in the Omnipedia example, the URL would now be ssh://github.com/neurocracy/omnipedia.git
5. Now click “Detect host keys” and wait for it to finish - this is important
6. Set “Authentication method” to “SSH public key”
7. Enter “git” as the user name
8. Click “Mirror repository”
9. Lastly, click the “Copy SSH public key” button on the newly configured repository - you’ll need this for GitHub

GitHub

Now in your GitHub project:

1. Go to “Settings” → “Deploy keys”
2. Click “Add deploy key”
3. Give your key a title; I recommend something like “GitLab mirror key”
4. Paste the SSH public key you copied from GitLab previously
5. Check “Allow write access” - this is important
6. Click “Add key”

Testing

Mirroring should now be fully configured, so you can test it in one of two ways:

1. If you have new commits locally that you haven’t pushed yet, you can push now to your GitLab project and it should mirror that to the GitHub counterpart.
2. If you don’t want to add new commits, you can tell GitLab to mirror manually by clicking the “Update now” button in your GitLab project’s “Settings” → “Repository” → “Mirroring repositories”

Once you’ve done either one, you can refresh the GitHub project’s “Settings” → “Deploy keys” page, after which you should see green text under the deploy key stating something along the lines of “Last used within the last week”.

For a few minutes, I felt like Indiana Jones staring at the holy grail. 230ms of savings from some CSS selector changes. This felt significant. And yet, the niggling phrase of “CSS selector efficiency is not something to worry about in 2024” kept ringing through my head.

I ran the before/after through WebPageTest and to my surprise (and lack of surprise), nothing significant had changed.

What keeps the software alive are the programmers who have an accurate mental model (theory) of how it is built and works. That mental model can only be learned by having worked on the project while it grew or by working alongside somebody who did, who can help you absorb the theory. Replace enough of the programmers, and their mental models become disconnected from the reality of the code, and the code dies. That dead code can only be replaced by new code that has been ‘grown’ by the current programmers.

Training data are construction materials for a language models. A language model can never be inspired. It is itself a cultural artefact derived from other cultural artefacts.

The machine learning process is loosely based on decades-old grossly simplified models of how brains work.

[…]

It’s important to remember this so that we don’t fall for marketing claims that constantly imply that these tools are fully functioning assistants.

[…]

Who was the first man on the moon?

[…]

What you’re likely to get back from that prompt would be something like:

On July 20, 1969, Neil Armstrong became the first human to step on the moon.

This is NASA’s own phrasing. Most answers on the web are likely to be variations on this, so the answer from a language model is likely to be so too.

[…]

The prompt we provided is strongly associated in the training data set with other sentences that are all variations of NASA’s phrasing of the answer. The model won’t answer with just “Neil Armstrong” because it isn’t actually answering the question, it’s responding with the text that correlates with the question. It doesn’t “know” anything.

A core aspect of the theory-building model of software development is code that developers don’t understand is a liability. It means your mental model of the software is inaccurate which will lead you to create bugs as you modify it or add other components that interact with pieces you don’t understand.

Language model tools for software development are specifically designed to create large volumes of code that the programmer doesn’t understand. They are liability engines for all but the most experienced developer. You can’t solve this problem by having the “AI” understand the codebase and how its various components interact with each other because a language model isn’t a mind. It can’t have a mental model of anything. It only works through correlation.

A great interactive demo showcasing just about all the major obnoxious dark patterns a lot of larger websites use to make our lives miserable. Not the dystopian future we were promised.

Instead of attaching tooltips directly to document.body, attach them to a predefined div in document.body.

BAD

GOOD

Introduction

Tooltips in our app were taking >80ms. And during this time, the main thread was blocked, you couldn’t interact with anything.

Other components like modal, popover, dropdown had similar performance issues. In some cases, a modal took more than 1 second to appear while making the UI unresponsive.

Read on in the source link for details.

js;dr is JavaScript required; Didn’t Read.

Pages that are empty without JS: dead to history (archive-org), unreliable for search results (despite any search engine claims of JS support, check it yourself), and thus ignorable. No need to waste time reading or responding.

Also known as, if it’s not curlable, it’s not on the web.