4 ms·

I could be wrong as I never worked in the hyper-optimized space, but I don't think that's necessarily a boring problem to work on. It's highly scientific and I'

by bko 4y ago

I could be wrong as I never worked in the hyper-optimized space, but I don't think that's necessarily a boring problem to work on. It's highly scientific and I'm sure a lot of talented engineers are interested in A/B testing. It's not much different than compiler optimizations that squeeze out tiny efficiencies that are not noticeable and irrelevant to 99.9% of use cases. If you can make red button blue and quantify its effects in terms of UX (people being able to find the button or make reality more aligned with expectations of the user), I think that could be interesting. But I would love to hear from someone that has labored over such a task. If I were hiring someone to figure out which buttons to turn from red to blue, I would at least try to find people curious and serious about the task. I wouldn't just accept that its monkey work, throw some money at it and hope for the best.

johnbender 4y ago
Learning whether the button should change colors is different from actually changing the colors. I agree that the learning part is interesting but at google I suspect those are two different jobs.
orange_joe 4y ago
I was using the phrase “turning the red button blue” as a description of the fundamental toil that goes into maintaining complex systems. There might be thousands of people who find that work interesting, but there are millions of buttons.
blululu 4y ago
It is not monkey work. But A/B testing can often be more a trapping of scientific than the real thing. Google UXR is hit or miss. They have done a number of poorly run studies on what shade of blue to color buttons without thinking about multiple comparison corrections, seasonality or evolution in display technologies. I am increasingly unconvinced that these efficiencies are meaningful for UX. Compilers are different and those optimizations are real and matter for both the bottom line and UX.
dmitriid 4y ago
> I could be wrong as I never worked in the hyper-optimized space, but I don't think that's necessarily a boring problem to work on. It's highly scientific and I'm sure a lot of talented engineers are interested in A/B testing. That's why Google ends up with 25 shades of blue because each button is "scientifically interestingly A/B tested to death": "When you desgined 5 screens in Figma, but couldn't be bothered to chose the better buttons, fought with managers and requested an A/B/C/D/E test" https://twitter.com/nikitonsky/status/1555686876274630656 https://twitter.com/nikitonsky/status/1555686876274630656 and https://twitter.com/nikitonsky/status/1557755746686754818 https://twitter.com/nikitonsky/status/1557755746686754818

by bko 4y ago

johnbender 4y ago

Learning whether the button should change colors is different from actually changing the colors. I agree that the learning part is interesting but at google I suspect those are two different jobs.

orange_joe 4y ago

I was using the phrase “turning the red button blue” as a description of the fundamental toil that goes into maintaining complex systems. There might be thousands of people who find that work interesting, but there are millions of buttons.

blululu 4y ago

It is not monkey work. But A/B testing can often be more a trapping of scientific than the real thing. Google UXR is hit or miss. They have done a number of poorly run studies on what shade of blue to color buttons without thinking about multiple comparison corrections, seasonality or evolution in display technologies. I am increasingly unconvinced that these efficiencies are meaningful for UX. Compilers are different and those optimizations are real and matter for both the bottom line and UX.

dmitriid 4y ago

> I could be wrong as I never worked in the hyper-optimized space, but I don't think that's necessarily a boring problem to work on. It's highly scientific and I'm sure a lot of talented engineers are interested in A/B testing. That's why Google ends up with 25 shades of blue because each button is "scientifically interestingly A/B tested to death": "When you desgined 5 screens in Figma, but couldn't be bothered to chose the better buttons, fought with managers and requested an A/B/C/D/E test" https://twitter.com/nikitonsky/status/1555686876274630656 https://twitter.com/nikitonsky/status/1555686876274630656 and https://twitter.com/nikitonsky/status/1557755746686754818 https://twitter.com/nikitonsky/status/1557755746686754818