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In January, I found myself completely immersed in building an app centered around AI artwork generation. I dedicated about two weeks to this project, diving deep into everything related to Stable Diffusion. I experimented with running it on every laptop equipped with a GPU that I owned. The process involved creating an API for Stable Diffusion, generating images, and figuring out how to index and search them. I also explored existing open APIs that could facilitate this process.
To support this endeavor, I developed a new database service. I hadn't used Outfit.io before, but I discovered they offer some impressive PostgreSQL cloud options. It's quite neat! Additionally, I tried out Tinybird for the first time for ClickHouse. This was a game-changer for me because one of the major hurdles in using ClickHouse for personal projects, as opposed to work projects where I use it frequently, was the lack of an affordable and easy-to-use cloud hosting service for PostgreSQL. Tinybird seems to fill that gap nicely.
I built a Progressive Web App (PWA) and transformed it into a TestFlight Android app. The results were impressive, thanks to Capacitor. Although I don't regret the learning experience—it was genuinely enjoyable—I'm relieved to have moved past it. As exciting as it is to generate AI artwork and build apps, I realized I wouldn't want to start a company around this concept. The market feels very consumer-oriented and low-end, with unresolved copyright issues.
There's ongoing debate about whether Stable Diffusion itself is legally sound. The images used for training might fall under fair use, but courts could eventually rule that copyright is required for all training data. This uncertainty makes the output of these models questionable, and you might not be able to sublicense any of it legally.
While the applications of this technology are straightforward and easy to build, it's challenging to monetize them. There are countless apps available that allow users to generate images using Stable Diffusion on their phones. Despite their abundance, I don't find them particularly good. However, the sheer number makes it difficult to charge for such services.
Ultimately, I've decided to offer my app for free, covering minimal API usage costs myself. Currently, there's a limit of about 30 images per day per user. I'll see how sustainable this model is. If you're interested, you can check it out at pica.so—it's like Picasso, but with one 'S'.
By Indie.am5
11 ratings
In January, I found myself completely immersed in building an app centered around AI artwork generation. I dedicated about two weeks to this project, diving deep into everything related to Stable Diffusion. I experimented with running it on every laptop equipped with a GPU that I owned. The process involved creating an API for Stable Diffusion, generating images, and figuring out how to index and search them. I also explored existing open APIs that could facilitate this process.
To support this endeavor, I developed a new database service. I hadn't used Outfit.io before, but I discovered they offer some impressive PostgreSQL cloud options. It's quite neat! Additionally, I tried out Tinybird for the first time for ClickHouse. This was a game-changer for me because one of the major hurdles in using ClickHouse for personal projects, as opposed to work projects where I use it frequently, was the lack of an affordable and easy-to-use cloud hosting service for PostgreSQL. Tinybird seems to fill that gap nicely.
I built a Progressive Web App (PWA) and transformed it into a TestFlight Android app. The results were impressive, thanks to Capacitor. Although I don't regret the learning experience—it was genuinely enjoyable—I'm relieved to have moved past it. As exciting as it is to generate AI artwork and build apps, I realized I wouldn't want to start a company around this concept. The market feels very consumer-oriented and low-end, with unresolved copyright issues.
There's ongoing debate about whether Stable Diffusion itself is legally sound. The images used for training might fall under fair use, but courts could eventually rule that copyright is required for all training data. This uncertainty makes the output of these models questionable, and you might not be able to sublicense any of it legally.
While the applications of this technology are straightforward and easy to build, it's challenging to monetize them. There are countless apps available that allow users to generate images using Stable Diffusion on their phones. Despite their abundance, I don't find them particularly good. However, the sheer number makes it difficult to charge for such services.
Ultimately, I've decided to offer my app for free, covering minimal API usage costs myself. Currently, there's a limit of about 30 images per day per user. I'll see how sustainable this model is. If you're interested, you can check it out at pica.so—it's like Picasso, but with one 'S'.