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The right way to handle this is not to build it grids and whatnot, which all get blown away by the embedding encoding but to instruct it to build image processing tools of its own and to mandate their use in constructing the coordinates required and computing the eccentricity of the pattern etc in code and language space. Doing it this way you can even get it to write assertive tests comparing the original layout to the final among various image processing metrics. This would assuredly work better, take far less time, be more stable on iteration, and fits neatly into how a multimodal agentic programming tool actually functions.
With a subscription service 10 years ago, you just need to have enough must-see content:
- Original scripted TV series that become mainstream known and/or seen as prestige TV, like "The Crown," "Mindhunter," "Bridgerton," "Stranger Things" etc.
- "Crown Jewel" reruns with huge fanbases such as The Office, Friends, Seinfeld, Modern Family, Breaking Bad, Better Call Saul, Arrested Development, etc.
- Unscripted TV series that become buzzy - like Love Is Blind, Tiger King, etc.
Having those categories all well-stocked ensures that only a fool would cancel their Netflix subscription as they'll be out of the loop when the new season of a 'zeitgeisty' show drops. You don't really need all your viewers to watch more hours to get more money every year, you can grow revenue with a combo of new viewers and price increases as long as users just watch regularly.
I think present-day Netflix sees incentives:
- to get as many people on the ad tier as possible so they can scale revenue with watch time
- to increase watch time which is a solved problem via psychological manipulation if you have good ML like they do
- more watch time without spending more money points pretty obviously to lowering cost per show as much as you can, which manifests as worse quality, more reality, more imported dubbed shows, etc. and drastically curtailing giving huge checks to the Matthew Weiners, David Benioffs, and Vince Gilligans of the world to bet on a massive superhit.
So they will want to focus heavily on the unscripted category plus whatever they can slap together cheaply, then autoplay and optimize their way to growth.
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LLMs can help to handle the subjectivity in how GAAP is applied and provide justifications, which previous rules-based tax software could not before.