# Issues with Duplicate NFTs in Generative Art Projects
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Chapter 1: Understanding Generative Art in NFTs
Recently, while browsing Twitter, I encountered a thought-provoking post from user #HashBastardsNFT discussing an NFT drop filled with numerous duplicate items. This situation raises significant concerns about the quality and uniqueness of generative NFTs.
The metaphor employed here compares two sheep that, while appearing almost identical, symbolize the problem of duplication in NFTs. Such redundancy can undermine the value and appeal of these digital assets.
Section 1.1: The Problem of Duplication
In the example shared, the sheer number of duplicates is striking. For this many duplicates to exist, regardless of the supposed coding errors, it suggests a severe limitation in the variety of traits utilized in the generative process. While the duplicates might be visually appealing, the lack of diversity is concerning.
Having worked in generative art coding, I recognize that not everyone possesses the same level of expertise in this field. The presence of duplicates in a generative NFT collection is a crucial flaw, particularly when these sets are marketed as having thousands of "programmatically unique" images.
Subsection 1.1.1: How Generative Art Coding Functions
To clarify how generative art coding operates: It begins with generating an image based on a rarity table. This involves selecting elements like background, body, clothing, and accessories. As choices are made, a “DNA” string is created to represent each NFT. During the generation process, the DNA strings are compared. If a match is found, the duplicate is discarded, and the process starts anew. If not, the unique NFT is saved.
Seeing duplicates arise organically is quite rare, as most generative sets feature hundreds of traits across various categories. Mathematically, the likelihood of encountering duplicates should be minimal.
Section 1.2: Implications of Low Trait Variety
In my view, a high number of duplicates suggests that the generative set was created with an insufficient variety of traits. This often reflects a lack of effort from the development team regarding art planning and execution.
Despite this, many collectors may not be overly concerned. If a prominent influencer endorses a collection with limited possible outcomes, it might still attract buyers, even if the variety seems inadequate. A notable example is Mekaverse, which was widely regarded as disappointing for its lack of diversity.
Chapter 2: The Importance of Trait Diversity
For my clients, I advocate for a broader range of traits—ideally a minimum of 200 for a 10,000 piece generative run. Expanding the variety significantly enhances the aesthetic appeal of the collection.
In conclusion, while opinions on this matter may vary, I firmly believe that greater aesthetic diversity in generative NFTs enriches the experience for collectors and enhances the overall value of the project.
Jim Dee is a prolific writer, developer, and multimedia creator based in Portland. You can explore his work, businesses, and publications at JPD3.com. Thank you for reading!