Comment by WarmWash
12 hours ago
No, it is incredibly streamlined because it tailored specifically to achieve this modernization.
The paid program can do it because it can accept these files as an input, and then you can use the general toolset to work towards the same goal. But the program is clunky an convoluted as hell.
To give an example, imagine you had tens of thousands of pictures of people posing, and you needed to change everyone's eye color based on the shirt color they were wearing.
You can do this in Photoshop, but it's a tedious process and you don't need all $250/mo of Photoshop to do it.
Instead make a program that auto grabs the shirt color, auto zooms in on the pupils, shows a side window of where the object detection is registering, and tees up the human worker to quickly shade in the pupils.
Dramatically faster, dramatically cheaper, tuned exactly for the specific task you need to do.
I think use cases like that will be where "AI" has the biggest wins.
That's a task that I could automate as a developer, but other than LLM "vibe coding", I don't know that there's a good way for a lay person to automate it.
There are two forms of business software gen AI coding is 100% going to eat:
Because neither of these make economic sense for commercial companies to develop targeted products for.
Consequently, you got "bundled" generalized apps that sort of did what you wanted (GP's example) or fly-by-night one-off solutions that haven't been updated in decades.
The more interesting questions are (a) who is going to develop these new solutions and (b) who is going to maintain these new solutions? In-house dev/SRE or newly more-efficient (even cheaper) outsourced? I'd bet on in-housing, as requirements discovery / business problem debugging is going to quickly dominate delivery/update time. It already did and that was before we boosted simple app productivity.