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How much more creative could models be by default? We asked frontier and open-weight models to design a landing page with “unique personality and taste” three separate times using the same business brief and images It almost looks like we got the same site

15,962 Aufrufe • vor 10 Tagen •via X (Twitter)

13 Kommentare

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

Interestingly, Haiku 5.5 shows some surprising variety across three takes on this travel booking site

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

Text-to-image models show more visual variety on the same task but the results have their own flaws

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

At Fieldwork, we’re working to measure design variety so we can improve it Designers already push well beyond these defaults with better prompting, skills like @impeccable_ai, and tools like @withgraphical Still, it’s striking how often different models arrive at a very similar solution All outputs and methodology: Text-to-image results:

Profilbild von andrew pignanelli
andrew pignanellivor 10 Tagen

try this: ask it to map words to a random number generator. Then have each agent use the generator to make 50 random words and write those out before making the website. Then run the experiment. Surprisingly good hack that works for more diversity in design outputs.

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

injecting entropy is a really reasonable solution here and I'd assume some of the variety we saw with haiku 5.5 may be the harness doing that by default

Profilbild von Eric Li
Eric Livor 10 Tagen

I do think it's very prompt- and use-case-sensitive. I've been able to get a lot of variation with Opus 5.5 when I specifically ask for it. Rarely on the first shot, though.

Profilbild von Harold Apples
Harold Applesvor 10 Tagen

Why is it that every model chooses to use tiny subhead text above everything?

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

1 year ago: purple gradients today: eyebrow headers 6 months from now: ?

Profilbild von Wikman
Wikmanvor 10 Tagen

Super interesting. This is a space I am playing in at the moment. Trying out different sets of briefs, assets etc to learn where the models shift from default to more creative thinking.

Profilbild von Jeff Smith
Jeff Smithvor 10 Tagen

Feel free to play around with our prompts and assets: These are pretty shallow scenarios but still interesting to see nascent variety with recent models (Haiku 5.5, Astra)

Profilbild von Wikman
Wikmanvor 10 Tagen

Will do.

Profilbild von Sani Ai Tech
Sani Ai Techvor 10 Tagen

A fascinating look at how much creative diversity models still lack

Profilbild von BitAres
BitAresvor 9 Tagen

Three unique personalities, one shared wardrobe 😅

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learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 Aufrufe • vor 2 Monaten