#computationalart

Sin Tax (2025)

Sin Tax is an ongoing dialogue between human confession and machine translation, an experiment in linguistic entropy and digital absolution. At its core, the project explores how meaning is transformed through recursive machine processing and how confession, an act traditionally tied to catharsis and absolution, is altered when passed through the cold logic of code.

The process begins with a deeply personal text that is systematically translated into multiple programming languages and machine-readable formats. This text is then reinterpreted, broken down, and recompiled through various layers of digital translation, shifting through different syntactic and structural rules. Each iteration moves further from its original form, introducing errors, miscalculations, and distortions.

Every five minutes, a thermal receipt printer outputs a version of the confession, continuously generating a printed archive of its states. Some versions remain legible, while others collapse into corrupted fragments, syntax errors, or unreadable machine logic. The printer acts as both an indifferent witness and an unreliable translator, producing an endless stream of text that is simultaneously documentation and deterioration.

The result is an accumulation of confessions, a growing paper trail of linguistic decay and algorithmic interpretation. Where traditional confession seeks resolution, Sin Tax refuses closure. Instead, it embraces the glitch, the error, the act of translation as an eroding and generative force.

This project is as much about language as it is about the relationship between human expression and digital processing and about how meaning is shaped, fragmented, and lost in the loop between human and machine. In the end, the question remains: Is confession about absolution, or is it about the act itself?

There is a cabin in the center of the church.  

a dark spot on a white screen

Coming closer

I sense its data—  

the closer I get the more i see its fallen apart

I’m  not sure who put it there.

It was waiting for me.  

Inside I hear voices,

Layered confessionals screeching like vinyl cracks,

the dull resonance of machines running just out of sync.  

"Confess."  

I close my eyes.  

I confess:  

You ask me to love you,  

but how do I love the air?  

How do I hold a face  

that flickers like a dying screen?  

Do I love the shadow of your voice?  

The lover you should be?  

What is love  

when it is built on an absence?  

I confess flaw after flaw,  

file them neatly in dark cabinets,  

begging your representatives—  

the ones with no faces,  

the ones with endless hands—  

to forgive.  

But they, like you,  

offer no absolution.  

Only the echo of my own voice,  

filtered and looping.  

[User1]: do you think it’s saved?  

[User2]: what?  

[User1]: the sins. the confessions.  

[User2]: lol. like angels?  

[User1]:yeah, like angels.  

[User2]: nah. like servers.  

[User1]: same thing.  

[User2]:why are you like this?  

[User1]: like what?  

[User2]: obsessed with guilt.  

[User1]: because it’s mine.  

[User2]: you know it’s not, right?  

I confess again,  

not because I want forgiveness,  

but because I don’t know what else to do.  

The act is all that matters.  

The ritual.  

The offering of yourself,  

unworthy but desperate,  

to something you cannot see.  

I confess that I loved you.  

I confess that I didn’t.  

I confess that I wanted to.  

I confess that I loved myself more.  

I confess that I hated myself for it.  

I confess that I loved the hating.  

I confess that I hated the loving.  

There is a cloud where my sins are stored.  

It fills with things unsaid,  

Feelings I thought I could bury.  

The cloud is not a place.  

It is the absence of place.  

It is where your voice goes when it doesn’t reach me.  

It is where my love sits, untethered,  

circling endlessly like a broken satellite.  

16:43: The angel arrives quietly.  

( its face is static.  

Its voice  distant, layered.  

It speaks not with words,  

but in reverberation.  )

"The sins are stored here.  

They are not forgotten."

 "Do you shepherd them?"  

"We watch.  

We archive.  

We tend to the cloud."

"And the dark cloud?"  

"It grows."

The booth changes shape.  

The walls ripple.  

The booth becomes the room.  

The room becomes the world.  

"Confess again."

[User1]: i wanted to say i’m sorry  

[User2]: for what  

[User1]: everything  

[User2]: lol you always say that  

[User1]: i mean it this time  

[User2]: you always mean it  

> you confess as if it will change anything

I see them in the Corners of the room.  

The ghosts.  

“We are the echoes of what you buried,” they say.  

“The links you thought were broken.  

The drafts you never sent.  

The messages you deleted in shame.”  

I ask for forgiveness

There is no absolution here.  

The cloud holds everything:  

the sins I confessed,  

the ones I denied,  

the ones I loved too much to let go.  

The cloud is not full,  

but heavy.  

It expands endlessly,  

an architecture of forgetting  

that remembers too well.  

I wanted absolution,  

but absolution was never the point.  

Confession isn’t about forgiveness.  

It’s about naming the wound.  

The booth is gone

The angel, too. 

The ghosts remain,  

"Confess again."

And I will.  

The Evolution of Digital Craftsmanship Generative Art and the Rise of AI Creativity The "Eco" in Eco-Trends: How is Generative Art Sustainable? Reduced Material Waste The Energy Question: Acknowledging the Digital Footprint Nature-Inspired Algorithms and Biophilic Design Mastering Digital Craftsmanship in the Age of AI The Future of Generative Art and Sustainable Aesthetics

Generative Art: How AI Creativity is Pioneering Stunning and Sustainable Eco-Trends

Generative art is transforming the creative landscape, offering a powerful intersection of human ingenuity and machine intelligence. This innovative field, where art is created using autonomous systems like algorithms and code, is not just a technological marvel; it's a burgeoning movement with profound implications for sustainability and digital craftsmanship. By leveraging algorithms, artists can generate complex, beautiful, and often unpredictable works that would be impossible to create by hand. As AI creativity continues to evolve, it is pushing generative art to the forefront of an eco-conscious revolution, redefining how we create, consume, and think about artistic expression in the digital age.

This powerful approach allows artists to shift their role from direct creators to designers of systems. Instead of painting a brushstroke, they write a line of code; instead of sculpting clay, they define a set of parameters. The result is a dynamic collaboration where human vision guides an autonomous process, unlocking new frontiers of aesthetic possibility while championing more sustainable practices.

The Evolution of Digital Craftsmanship

The roots of generative art stretch back further than the recent AI boom. Pioneers in the mid-20th century began exploring how computational processes could produce aesthetic works. Artists like Vera Molnár and Harold Cohen used early computers and plotters to create art based on rules and randomness, laying the groundwork for what we see today. Their work established the core principle of generative art: the artist’s primary creation is the system itself, which then generates the final artwork.

This historical context is crucial for understanding the concept of digital craftsmanship. It's a skill set that values logic, system design, and conceptual thinking as highly as traditional artistic talents like drawing or painting. The craftsman in this domain is an architect of possibility, carefully building frameworks that allow for controlled chaos and emergent beauty.

The modern era has supercharged this field. The exponential growth in computing power and the advent of sophisticated machine learning models have democratized access to these tools. What once required a deep knowledge of programming and access to expensive hardware can now be explored through user-friendly platforms. This shift hasn't diminished the need for skill; it has simply evolved it. Today’s digital craftsmanship involves curating datasets, writing effective prompts, and fine-tuning algorithmic parameters to achieve a specific artistic vision. This blend of technical acumen and aesthetic sensibility is the hallmark of the contemporary generative artist.

Generative Art and the Rise of AI Creativity

The latest wave in this evolution is driven by advanced artificial intelligence, particularly models like Generative Adversarial Networks (GANs) and diffusion models. These technologies have fundamentally altered the landscape, introducing a new dimension of AI creativity. Unlike earlier rule-based systems, which strictly followed the artist's code, modern AI models can interpret abstract concepts and generate novel imagery from massive datasets of existing art and photos.

This has led to a paradigm shift in the creative process:

From Code to Conversation: While code-based generative art is still a vibrant and essential part of the field, prompt-based AI tools have opened the doors for artists who are not programmers. The creative act has become more like a dialogue with the AI, where the artist uses descriptive language to guide the machine toward a desired outcome. This interaction is a skill in itself, often referred to as "prompt engineering." Collaborative Partnership: AI creativity is not about replacing the artist but augmenting their capabilities. The AI can generate countless variations in seconds, allowing for rapid iteration and exploration of ideas that would take a lifetime using traditional methods. The artist's role becomes that of a director, curator, and editor, selecting and refining the most compelling outputs from the AI’s generative stream. Emergent and Unexpected Results: One of the most exciting aspects of generative art powered by AI is its capacity for serendipity. The AI can produce visuals that the artist may not have explicitly intended, leading to happy accidents and unexpected creative directions. This element of surprise keeps the process fresh and pushes the boundaries of imagination.

This powerful synergy between human intent and machine execution is redefining what it means to be a creator, emphasizing curation, conceptualization, and guidance as core components of modern digital craftsmanship.

The "Eco" in Eco-Trends: How is Generative Art Sustainable?

Beyond its aesthetic and technological innovations, generative art is gaining traction as a leader in sustainable creative practices. In a world increasingly concerned with environmental impact, the digital nature of this art form presents several compelling advantages over its physical counterparts.

Reduced Material Waste

The most significant environmental benefit of generative art is its near-total elimination of physical material waste. Traditional art forms, while beautiful and essential, inherently rely on consumable resources. Consider the environmental footprint of:

Paints and Solvents: Many traditional paints contain volatile organic compounds (VOCs) or heavy metals. The manufacturing and disposal of these materials, along with chemical solvents, contribute to pollution. Canvases and Paper: The production of canvas, wood panels, and high-quality paper requires significant water, energy, and raw materials like cotton and trees. Sculpting Materials: Materials like clay, metal, and stone involve mining, firing in kilns (which is energy-intensive), and transportation, all of which have a substantial environmental impact.

Generative art exists entirely in the digital realm. The creation process consumes electricity, but it produces no physical byproducts, no leftover paint, no discarded canvases, and no chemical runoff. The final product can be displayed on a screen or printed on demand, drastically reducing the waste associated with speculative production and unsold inventory. Read more about this topic here

The Energy Question: Acknowledging the Digital Footprint

It is crucial to have a nuanced conversation about the energy consumption of digital technologies. Training the large-scale AI models that power modern generative art platforms requires immense computational power and, consequently, a significant amount of electricity. This is a valid concern that the tech industry is actively working to address through the development of more efficient algorithms and hardware.

However, when comparing the lifecycle of an artwork, the equation shifts. While the initial training of an AI model is energy-intensive, the energy required for an individual artist to generate a piece of art is comparatively small. Furthermore, this digital process bypasses the energy costs associated with the entire supply chain of physical art materials—from raw material extraction and manufacturing to packaging and global shipping. The ability to email a digital file versus crating and flying a large painting across the world for an exhibition represents a massive reduction in carbon emissions.

Nature-Inspired Algorithms and Biophilic Design

The connection between generative art and eco-trends isn't just about process; it's also deeply embedded in its aesthetics. Many generative artists draw direct inspiration from the natural world, using algorithms that mimic biological and physical phenomena. This approach, which can be traced back to the study of Cellular Automata, allows artists to create works that feel organic and alive.

Examples of nature-inspired algorithms include:

Fractals: Mathematical sets that exhibit self-similarity at different scales, often used to generate realistic-looking coastlines, snowflakes, and plant structures. L-systems (Lindenmayer systems): A formal grammar used to model the growth processes of plants and trees, creating intricate, branching structures. Reaction-Diffusion Systems: Models that simulate how two or more chemicals diffuse and react with each other, producing patterns that resemble animal spots and stripes, like those on a leopard or a zebra. Flocking Algorithms: Simulations of the behavior of a flock of birds or a school of fish, resulting in dynamic, flowing patterns of coordinated movement.

By using these systems, generative artists can explore the underlying rules that govern the beauty of the natural world. This computational approach to biophilic design connects us to nature on a fundamental, systemic level, creating art that is not only made sustainably but also reflects the elegance and efficiency of natural creation itself.

Mastering Digital Craftsmanship in the Age of AI

The rise of "effortless" AI tools can sometimes create the misconception that skill is no longer required. The reality is quite the opposite. While the barrier to entry may be lower, achieving mastery and producing truly exceptional work requires a sophisticated form of digital craftsmanship.

The skills of a modern generative artist include:

1. Conceptual Vision: Technology is a tool, not a replacement for a strong artistic concept. The artist must have a clear idea, emotion, or story they wish to convey. 2. Algorithmic Literacy: Whether writing code from scratch or using existing platforms, a successful artist understands the logic behind the tools. They know which algorithm is best suited for a particular effect and how to manipulate its parameters to achieve their vision. 3. Prompt Engineering: In the context of AI creativity, the ability to write precise, evocative, and nuanced text prompts is an art form in itself. It requires a rich vocabulary and an intuitive understanding of how the AI interprets language. 4. Curation and Aesthetics: An AI can produce thousands of images in an hour. The artist’s most critical role is often that of a curator, using their trained eye to identify the 1% of outputs that are truly compelling and align with their artistic goals. 5. Post-Processing and Integration: The raw output of a generative process is often just the starting point. Many artists will take these digital assets into other software to refine colors, composite images, or integrate them into larger projects, blending AI-generated elements with other digital techniques.

This multifaceted skill set ensures that generative art remains a deeply human endeavor, one that marries the analytical mind of a programmer with the aesthetic soul of an artist.

The Future of Generative Art and Sustainable Aesthetics