dedications
Creative Code. Made in 2026.
"I cannot remember the books I've read any more than the meals I have eaten; even so, they have made me." – Ralph Waldo Emerson
"saw a girl post her spotify wrapped with the caption 'so accurate' like yeah it's literally data."
— X user @schizohustler, Dec 3, 2025 (source)
In its 2025 Year in Review, Goodreads reports that I read 6,139 pages (30 books). This is just one instance of the now-familiar genre of data-aggregation-as-bite-sized-shareable-graphic. This year alone, I received year-in-reviews for music streamed, video games played, professional profiles browsed, rideshares requested, cuisines tried. Why so many?
"Data" is "accurate", like a looking glass that does not flatter. This perceived neutrality helps explain why we love sharing year-in-reviews, and why platforms continue to produce them. Yet at a moment where data is increasingly used for surveillance, prediction, and potentially behavior manipulation, I grow critical of data aggregation.
This exercise seeks to represent my reading experience in a way that refuses such aggregation.
[1] I went through every book I read in 2025 and logged its dedication. Dedications fascinate me. Not all books have dedications. They are opaque, privately meaningful only to the writer. No doubt often sentimental, rarely indexed, and almost impossible to reverse-search, dedications gesture towards the labor of writing and the intimacy of reading that remain (yet) unoptimized for algorithms. I tokenized these dedications into clauses and spliced them at random.
[2] I employed code to represent the dedications on a browser viewport with a small dot at randomized positions. I let the machine complete the 'visualization', resulting in a fittingly opaque graphic.
dedications rejects the clarity promised by year-in-review metrics to embrace partial legibility.
View my 2025 dedications here. If you can somehow tell, let me know if we read the same book!
I welcome reproductions. The source code for dedications is hosted on GitHub.