> Will drop a message to ask if it'd be possible to have a similar tool for Python.
Why not create your own? This is as wild as open-source expectations of a speedy implementation, addressing issues quickly, etc. Folks always want more and more.
I worked at a media company that used to track time the users spent watching TV, so they could charge advertisers accordingly. "Hundred million watched the Super Bowl", things like that.
The way they measured that was by having devices installed on peoples homes, that tracked TV usage, and all of it was planned around demographics as well. People would get a few bucks for having the device installed. But hey, if you wanted to measure the top 1% of income, what would you do? A few bucks meanth nothing, so it was always in terms of 'favors' and 'gifts'. A nice champagne here and there.
What about the bottom 5, 10%? Well, you can't give them any money, because that could effectively move them from one social class to another (not the US, but locally this would be moving them from Class E to Class D). So you took them to lunch.
It’s not so straightforward, though. In the U.S., wages are usually discussed in terms of hourly pay or annual salary, whereas in Brazil, they’re typically expressed as monthly earnings.
I fully expect a model to output a SVG made up of 1000x1000 rectangles (i.e. pixels) representing a raster image of a beautifully hand-drawn pelican riding a bicycle any day now :)
I got an amazing result from ChatGPT a while back - an SVG with a perfect illustration of a pelican riding a bicycle.
It was suspiciously good in fact... so I downloaded the SVG file and found out it had generated a raster image with its image tool and then embedded it as base64 binary image data inside an SVG wrapper!
You’ll just have to move the goalpost then; perhaps it can be a multidimensional pelican saving the multiverse, or an invisible pelican that only you can see and critique.
How would that help, given that ChatGPT has apparently already figured out how to consistently and systematically game the benchmark by working in pixel space and only using SVG as a wrapper for a raster image?
FWIW, I could totally see a not hugely more advanced model using its native image generation capabilities and then running a vector extraction tool on it, maybe iteratively. (And maybe I would not consider that cheating, anymore, since at some point that probably resembles what humans do?)
Other things you can ask that they're still clearly not optimizing for are ASCII art and directions between different locations. Complete fabrications 100% of the time.
Well, I definitely hope they aren't trying to teach LLMs directions between locations, given how idiotic use of compute and parameter space that would be. We already have excellent AIs for route planning. What they ought to optimize for is, of course, finally teaching them to say they don't know, or just automatically opting to call a route-planning API if the user asks for directions.
What I'd really like is an image that mirrors extensions available on AWS Aurora. Supabase's is the only that has some parity as far as I know
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