I don’t run analytics on this website. I don’t care which articles you read, I don’t care if you read them. I don’t care about which post is the most read or the most clicked. I don’t A/B test, I don’t try to overthink my content.
Same!
Some lovely scroll-driven animations illustrate this great little microsite.
There’s something very pleasy about the chunky design that harkens back to the Zeldmanesque early web.
I want to live in a future where Artificial Intelligences can relieve humans of the drudgery of labour. But I don’t want to live in a future which is built by ripping-off people against their will.
- Be skeptical of PR hype
- Question the training data
- Evaluate the model
- Consider downstream harms
There’s a general consensus that large language models are going to get better and better. But what if this as good as it gets …before the snake eats its own tail?
The tails of the original content distribution disappear. Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions. We call this effect model collapse.
Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale.
Scroll up to the Kármán line.
The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.
A lovely bit of real-time data visualisation from Robin:
It’s a personal project created at home in Wales with an aim to explore and visualise renewable energy systems. Specifically, it aims to visualise live generation from renewable energy systems around Great Britain and to show where that generation is physically coming from.
Ethan highlights a classic case of the McNamara Fallacy—measuring adoption of design system components.
This describes how I iterate on The Session:
It comes down to this annoying, upsetting, stupid fact: the only way to build a great product is to use it every day, to stare at it, to hold it in your hands to feel its lumps. The data and customers will lie to you but the product never will.
This whole post reminded of the episode of the Clearleft podcast on measuring design.
The problem underlying all this is that when it comes to building a product, all data is garbage, a lie, or measuring the wrong thing. Folks will be obsessed with clicks and charts and NPS scores—the NFTs of product management—and in this sea of noise they believe they can see the product clearly. There are courses and books and talks all about measuring happiness and growth—surveys! surveys! surveys!—with everyone in the field believing that they’ve built a science when they’ve really built a cult.
I like this approach to offering a design system. It seems less prescriptive than many:
Designed not as a rule set, but rather a toolbox, the Data Design Language includes a chart library, design guidelines, colour and typographic style specifications with usability guidance for internationalization (i18n) and accessibility (a11y), all reflecting our data design principles.
Here’s a really excellent, clearly-written case study that unfortunately includes this accurate observation:
In recent years the practice of information architecture has fallen out of fashion, which is a shame as you can’t design something successfully without it. If a user can’t find a feature, it’s game over - the feature may as well not exist as far as they’re concerned.
I also like this insight:
Burger menus are effective… at hiding things.
My talk, Building, was about the metaphors we use to talk about the work we do on the web. So I’m interested in this analysis of the metaphors used to talk about markup:
- Data is documents, processing data is clerking
- Data is trees, processing data is forestry
- Data is buildings, processing data is construction
- Data is a place, processing data is a journey
- Data is a fluid, processing data is plumbing
- Data is a textile, processing data is weaving
- Data is music, processing data is performing
The design process in action in Victorian England:
Recognizing that few people actually read statistical tables, Nightingale and her team designed graphics to attract attention and engage readers in ways that other media could not. Their diagram designs evolved over two batches of publications, giving them opportunities to react to the efforts of other parties also jockeying for influence. These competitors buried stuffy graphic analysis inside thick books. In contrast, Nightingale packaged her charts in attractive slim folios, integrating diagrams with witty prose. Her charts were accessible and punchy. Instead of building complex arguments that required heavy work from the audience, she focused her narrative lens on specific claims. It was more than data visualization—it was data storytelling.
This is a story about pizza and geometry.
The interactive widget here really demonstrates the difference between showing and telling.
City of Women encourages Londoners to take a second glance at places we might once have taken for granted by reimagining the iconic Underground map.
I love everything about this …except that there’s no Rosalind Franklin station.
Following on from my recently-lost long bet, this is a timely bit of data spelunking from Brian analysing the linkrot of 1400 links over 18 years of time.
Goodreads lost my entire account last week. Nine years as a user, some 600 books and 250 carefully written reviews all deleted and unrecoverable. Their support has not been helpful. In 35 years of being online I’ve never encountered a company with such callous disregard for their users’ data.
Ouch! Lesson learned:
My plan now is to host my own blog-like collection of all my reading notes like Tom does.
A fascinating four-part series by Lisa Charlotte Muth on colour in data visualisations:
This is a great combination of rigorous research and great data visualisation.