My ‘Fluidity’ collection combines elements of machine learning (supervised vector machines) and generative art (flow fields and sine wave intercalation) to create unique and surprising visual compositions. It is inspired by the desire to create something unique involving the web3 space. By utilizing the capabilities of permission-less systems, and open source art software like Manifold.xyz, Processing, Rstudio, and GIMP, the collection was my introduction into what is possible at the intersection of art and technology. I hope you enjoy!
Some selected works from the collection, which you can also check out Opensea and oncyber:
https://opensea.io/assets/ethereum/0x1C37A49BDDcb5438F2EA93d33C98FD724A2D388F/3
https://opensea.io/assets/ethereum/0x1C37A49BDDcb5438F2EA93d33C98FD724A2D388F/13
https://opensea.io/assets/ethereum/0x1C37A49BDDcb5438F2EA93d33C98FD724A2D388F/16
0xDFCP is my pseudonym, which I use given that I’m a full -time physician and would like to keep my art separate from my medical practice. I’ve been an art enjoyer and occasional painter since high school, but given my other professional and personal commitments never had a lot of time to pursue it. In January 2021, after learning about crypto from an acquaintance, I was surprised to learn about the thriving art scene that had integrated non-fungible tokens as the basis for ownership and trading of art online. I watched closely as artists such as Beeple, Pplpleasr, and Tyler Hobbes were breaking out and becoming household names. The inspiration for getting involved myself came from learning about artblocks and the web3 generative art movement from the Defiant.
The Defiant’s ‘Generative art is dumb’ was a great intro into generative art for me and got me interested in making my own art.
To create the collection, a variety of methods were employed. One of the key features of the collection are the flow fields present in all the pieces. These flow fields were made using adapted code from Daniel Shiffman and hbyhadeel using processing 4. The code from both was combined and slightly customized to produce the desired look and to allow high resolution output (14400 x 8100pix @ 300dpi). Below are some useful references for anyone looking to get started:
https://twitter.com/shiffman/status/1478433069476495367?s=20&t=RZsQN0LrCl8cVKu8QlC3PQ
In addition to the flow fields, color plots were created using supervised vector machines using Koenderks’ aRtsy package (a generative art, R package for ggplot2). This allowed for the creation of intricate and detailed color schemes that added depth and complexity to the pieces.
To add texture and further visual interest, some pieces also incorporated elements from GIMP (‘clothify’ and perlin noise) as well as hbyhadeel’s sine painting tutorial adapted for processing 4. The combination of these techniques results in compositions that are unique, surprising, colorful and dynamic.
Overall, the Fluidity collection represents my inaugural attempt at minting generative works of art. It incorporates a unique blend of machine learning, generative art, and traditional art techniques, all brought together through the use of code and manual composition. It’s a testament to the endless possibilities that technology offers for artistic expression. My aim is that the collection not only be visually appealing, but also give viewers pause (an optimism) about how technology can help enhance rather than replace the work of artists. I hope you enjoy viewing it as much as I enjoyed creating it.