This thing that we’ve been doing collectively with our relentless blog posts and pokes and tweets and uploads and news story shares, all 30-odd years of fuck-all pointless human chatterboo, it’s their tuning fork. Like when a guitarist plays a chord on a guitar and compares the sound to a tuner, adjusts the pegs, plays the chord again; that’s what has happened here, that’s what all my words are, what all our words are, a thing to mimic, a mockingbird’s feast.
Every time you ask AI to create words, to generate an answer, it analyzes the words you input and compare those words to the trillions of relations and concepts it has already categorized and then respond with words that match the most likely response. The chatbot is not thinking, but that doesn’t matter: in the moment, it feels like it’s responding to you. It feels like you’re not alone. But you are.
The short version of what I want to say is: vibe coding seems to live very squarely in the land of prototypes and toys. Promoting software that’s been built entirely using this method would be akin to sending a hacked weekend prototype to production and expecting it to be stable.
Remy is taking a very sensible approach here:
I’ve used it myself to solve really bespoke problems where the user count is one.
Would I put this out to production: absolutely not.
Here is the state of venture capital in early 2025:
- Venture capital is moribund except AI.
- AI is moribund except OpenAI.
- OpenAI is a weird scam that wants to burn money so fast it summons AI God.
- Nobody can cash out.
LLMs are good at transforming text into less text
Laurie is really onto something with this:
This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.
Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.
I enjoyed reading through these essays about the web of twenty years ago: music, photos, email, games, television, iPods, phones…
Much as I love the art direction, you’d never know that we actually had some very nice-looking websites back in 2004!
Benjamín Labatut draws a line from the Vedas to George Boole and Claude Shannon onward to Geoffrey Hinton and Frank Herbert’s Butlerian Jihad.
In the coming years, as people armed with AI continue making the world faster, stranger, and more chaotic, we should do all we can to prevent these systems from giving more and more power to the few who can build them.
A web by humans, for humans.
Thinking about priorities at UX Brighton.
Show me my associative trails.
Balancing the ledger.
Pest control for your website.