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Building the Tesseract: The Archive Learns to Search, See, and Talk Back

Sharing this with the NET-ART and CUNY Commons community because, underneath the build, it is a teaching question: what becomes possible when you can point AI at your own work, or a whole course, and search it, see it, and talk back to it, locally and for free? There is a section below written specifically for teachers. Originally published at ryanseslow.com.

 

Twenty years of my scattered work, pulled into one living archive that talks back, holds a huge portion of my creative life, opens a door for machines, and now shows you its face. A field report, with the unflattering parts included..

Yes, this is unapologetically long.. good thing our attention spans are ready for it!

This started with a simple, slightly uncomfortable question:

What is hacking, really, and could I hack myself?

Not in the “Hollywood” sense. In the original sense: to understand a system well enough to make it do something it was not expected to do. I wanted to point that lens at the one system I have the most access to and the least honest view of, my own patterns. So I asked an AI working session to do something most of us never let anything do: read my actual behavior off my own machine. Not the story I tell about myself, but the evidence. The files. The time-stamps. The folders and files that I start and abandon. The things I save and never reopen..

What came back changed how I see my own work, and over a handful of sessions it turned into something I had wanted for twenty years and never finished. This post is the whole story, start to finish. If you have been following along, you already know the early chapters: I let an AI read my entire twenty-year WordPress archive and asked what would happen. This is where it lands. The archive learned to talk back, then it went public, then it opened a door for machines, and just now it opened its eyes.

Here is the twist I did not expect: every version that worked was the one I made smaller. The early builds were ambitious and intricate. The versions that shipped are deliberately minimal, the standard library, one database file, a couple of small local models. The distillation was the breakthrough.

I stopped elaborating and started finishing..

Hacking Myself: The Loop I Could Not See

I let the session look at the shape of my digital life: my Desktop, my 50GB+ iCloud archive, my Google Drive, my live website. Not to read my private thoughts, to read the patterns. The structure. The geology.

The finding was humbling and precise. Across every archive, the same loop repeated at every scale:

A vision ignites, I erupt in prolific output, I get the high of the birth, the next idea pulls me away, the work is left where it landed, it quietly entombs, and months later the same idea is reborn under a new name..

I am, it turns out, addicted to genesis creation and allergic to maintenance. I start brilliantly and rarely return. My iCloud held a heroic consolidation of my career, built between 2015 and 2018, then abandoned and never reopened. My Desktop held twenty live project threads in ten weeks, nothing filed. And the same core idea, an AI trained on my own art and writing, had been born three separate times under three different names, each one starting over from zero. That is seriously funny!

The missing spot was not disorganization. It was that nothing I made was ever allowed to compound, because compounding requires returning, and returning never gave me the hit that starting did.

The Correction: “No Content Available”

Here is where it got sharp. The session found that I had, years ago, (2024 in AI time is like 10 tears ago in todays time) haha, already started building the AI-trained-on-me dataset. I had even exported my entire website into per-year training files. For a moment it looked like the project was most of the way done.

Then we actually opened the files. And almost every single record said the same thing:

{"prompt": "Describe the artwork titled 'DSC06448' created in 2009.",
 "completion": "No content available."}

The training data for the AI version of me was empty. The pipeline had pulled image filenames, DSC06448, but never captured a word of my actual writing. I had built the exciting structure of the idea, run it once, gotten back rows that literally read “No content available,” and walked away before the unglamorous extraction work.

I want to sit with how perfect that is. The empty file was the whole diagnosis in plain text. The content is available. It is all over my live site. I just stopped before capturing it. Genesis got done. Maintenance did not. Even my self-portrait-as-AI had abandoned itself at the hard part.

So we changed the plan: stop trying to out-discipline the loop, and build a layer that does the maintenance automatically, routing every future idea into one home instead of letting it spawn a fourth.

(One unglamorous aside, because it belongs to the same lesson: the self-audit also turned up live API keys sitting in plaintext inside old scripts, the kind of thing that can quietly run up a bill or worse. We found them, I revoked them, and rewrote those files to read their keys from the environment. The cost of never returning to your old work is that things rot there. Going back is not glamorous. It is also where safety, and value, actually live.)

Chapter One: The Archive Learns to Talk Back

The fix has a deliberately boring shape, because boring is what compounds. I call it RyanSeslow OS, a single, local home for my body of work, in three layers:

  • Ingest, pull my real content from where it actually lives.
  • Spine, store it once, in one place, in a form I can search and grow.
  • Aremes, a conversational layer that answers questions using only my own writing, in my own voice, with citations.

Then we built it, end to end, in a single session. It read my website, 1,160 posts and pages, roughly 357,000 words spanning 2008 to 2026, with more than 12,000 images linked, into a single catalog. It turned all of that into a local semantic index. And then I asked it a question I had never directly answered anywhere:

How can artists use AI to expand their creative practice without losing themselves?

Aremes answered in my voice, drawing on essays I wrote in 2012 and 2013, citing each one with a link, and honestly noting that I had never addressed the question head-on rather than inventing an answer. That honesty is the system working correctly. It is grounded in me, and only me.

For the first time in this entire twenty-year pattern, the AI-trained-on-me idea shipped, held real content, answered questions, and grows when I publish. The loop broke.

The most surprising thing about it is how small and free it is. It runs entirely on a laptop. No API key, no subscription, no cloud bill, nothing anyone can revoke. Python 3 and its standard library only. My own WordPress content via its built-in REST API. One SQLite file. Two small local models through Ollama: nomic-embed-text for the meaning index and llama3.2:3b for grounded answers. Retrieval is plain cosine similarity in pure Python; with about 1,100 documents, brute force is instant.

(The full build is in the appendix at the end of this post, so you can make your own!)

Chapter Two: The Archive Goes Public

The obvious next step was to drop the AI chat onto my website so anyone could ask it questions. I did the opposite, on purpose.

Here is why. The local model is small enough to run on a 2019 laptop, which is wonderful, but it means that every so often, even grounded in my real writing, it will invent a quote and attribute it to me. On my own machine, with a verification layer that flags fabrications, that is manageable. On a public website, it is unacceptable. A tool that occasionally puts words in my mouth, in front of strangers, is worse than no tool at all.

So the public version is search, not chat. It does not generate answers. It does not summarize. It does not imitate my voice. It takes your words, finds the most relevant passages from my actual posts, and links you straight to the originals. Zero hallucination, because there is no generation happening at all. Every result is really me. And it is built the way the whole project is built, as a single static page on my own shared hosting: no server to babysit, no AI service metering me, nothing a company can switch off.

You can use it right now: ryanseslow.com/search/

Then I gave it a big portion of my creative life, not just my blog. For two decades my work has lived in different places: long-form on the blog, but also thousands of posts on Tumblr, Instagram, more than 1,500 animated GIFs and stickers on Giphy. None of them talked to each other. None of them were searchable as one thing.

And this is where it got funny, and very me. It turned out I had already “prepared” each of these. Years ago I had made caption files, export folders, an archive system for every platform. I felt organized. Then we actually opened them:

  • My Giphy captions file, 1,593 rows, where every single caption was an error message. The captioning script had broken and saved the errors as the captions.
  • My Tumblr “full archive” was entirely placeholder text: “Caption for Ryan Seslow artwork N, generated from AI analysis.” Stubs. No real content.
  • My Instagram archive, a beautiful folder structure I had named “The Memory Tree,” had a captioned-exports folder that was completely empty.

The same thing, again. I built the elaborate structure and never filled it. So this time we finished it, going to the living sources instead of the abandoned exports: my real Tumblr posts pulled directly and filtered down to only my own work, my real Instagram captions from the official export, the real titles and dates for all of my Giphy work. One search across everything I have made, blending platforms that never knew about each other. Search “sign language graffiti” and you get my Tumblr hand-style posts, my Instagram public-space interventions, a sign-language sticker from Giphy, and my long-form essays on art in public space, side by side.

Chapter Three: A Front Door For The Machines

The search box was built for human eyes. But the next thing to visit your website is not going to be a person. It is going to be an agent.

More and more, the way people find and buy things runs through an AI acting on their behalf. You tell it what you want, and it goes out, reads sites, compares, and sometimes completes the purchase, all without you opening a tab. My website was welcoming to a person and almost invisible to software. An AI that showed up at ryanseslow.com had no clean way to know what I make, what is for sale, what it costs, or how to license it. My twenty years of work might as well not have existed to it.

So I gave my archive a front door that machines can read. There is an emerging set of quiet standards for exactly this: small files you place on your site, written for machines rather than people. One is llms.txt, a plain-language summary an AI can read to understand who you are and what you offer. Others live in a .well-known folder and describe your catalog and capabilities in a structured way agents already know how to parse. A sign, written in a language only machines speak, hung on the front of the building.

And, very on brand for this series, when I went to check it, the door was broken. The file an agent looks for first was returning “not found.” I had built the doorway and never confirmed anyone could walk through it. We found the bug, fixed it, and tested it the way an actual agent would. Now when an AI arrives, the door opens: it can read a clean description of my practice, pull a machine-readable catalog, and search all twenty years through a single endpoint.

Built into the same surface is a way for an agent to ask a price for a piece and pay for it, in stablecoin, on its own, with no invoice and no checkout page. I am calling this layer AREMES, and the point is simple: my work should be able to be found and licensed by a machine at three in the morning while I am asleep. I am not turning my art into a vending machine, and I am not replacing the human relationships that matter most. I am making sure that when the buyer is an agent, and increasingly it will be, the door is open instead of closed and invisible.

Reading My Art Off The Chain

Here is the part I am also excited about.. because it taught me something. A chunk of my digital art work over the last several years lives on-chain, as 1/1 art on SuperRare. I wanted all of it in the archive. So I asked the platform’s own tools for my catalog, and they could only cleanly hand me the works currently for sale, sixteen of them. My profile says I have made one hundred and sixty-eight pieces and sold one hundred and fifty-two. The convenient view of my own catalog was mostly the unsold remainder.

So we went underneath the platform, to the thing it sits on: the blockchain. Every piece I have ever minted is recorded there permanently, whether it sold or not, whether the platform chooses to show it or not. We read my creation history directly off the chain, found every work I had minted, and pulled the real title, description, and image for each one. One hundred and fifty-eight came back complete. Read-only, no fees, nothing that could be revoked.

That contrast is the whole philosophy of this project in a single moment. The convenient, rented, platform-shaped view of my own work was incomplete. The permanent, owned, underlying record was whole. (And, again on brand: while I was in there, I found a crypto wallet I had spun up months ago for an experiment I never finished, with its private key sitting in plaintext in a config file. Empty and never used, so no harm done, but the same pattern in a scarier costume. I closed that loop too. The exciting new thing always arrives with new housekeeping.)

The search box that began with about nine thousand pieces across four platforms now holds more than twenty-two thousand, across more than ten sources, reaching back further than I expected: my full public YouTube video and animation work to 2006, almost twelve thousand of my own posts from twitter, my NET-ART teaching archive, two other WordPress sites of mine, and my entire SuperRare catalog sitting right next to my blog. One search, one body of work, twenty years and then some, in one place I own.

Chapter Four: The Archive Opens Its Eyes

Until now, everything I have described answers in words. You search, and you get titles and passages and links. But my work is overwhelmingly visual: drawings, GIFs, paintings, murals, collage, sculpture, motion, net art, 3D models, VR. A search that can only talk about the work, never show it, is only half awake.

So in the last day I gave the search eyes. Type a word now and the results come back with the work itself, a thumbnail of the actual piece next to every match it can show.

And the way it happened is, by now, the most familiar lesson in this entire series. I assumed I would have to go re-collect all those images. Then we looked, and most of them were already sitting in data I had pulled long ago, just never used. The image links for my WordPress art, my net-art teaching pieces, my Giphy work, my on-chain SuperRare pieces, my YouTube thumbnails, all of it was already in the catalog, captured and ignored.

My Twitter archive was the sharpest version of it. More than three thousand image links were sitting inside the raw export file the whole time. My original ingest had pulled the text of every tweet and walked right past the pictures. The images were never missing. They were never extracted. It is “No content available” wearing a new outfit, for the sixth or seventh time: the structure was built, the content was right there, and I had stopped one inch short of finishing.

This time the inch got walked. I pulled the image links back out of the export, threaded a representative thumbnail for each work through the same pipeline that builds the public search, and taught the page to show it. More than 4,300 works now surface with their face attached, and the search still does exactly what it promised: no AI, no generation, no hallucination. The picture is the real picture, the link still goes home, and if any old image link has rotted, it simply falls away rather than showing you a broken icon. The eyes did not cost the honesty.

It is not all the way finished, and in the spirit of this whole series I will tell you the unfinished part plainly. Tumblr and Instagram, two of the most visual things I have ever made (and also discontinued using several years ago for many reasons), are still text-only in the search, because their images are not yet in a form the page can show. Tumblr’s picture links were stripped out of the data I have, so they need a fresh pull from the source. Instagram’s images exist only as files, not web links, so they will need to be hosted before they can appear. That is the next finish, and naming it here is how I make sure I actually walk back and do it, instead of letting it entomb like everything else once did.

What This Means For You

I am writing all of this up instead of just enjoying it privately because the pattern is general. If you have a body of work that includes words and images, your own art writing, a collection, a syllabus, an institution’s documents, you can build the same thing, on a laptop, for free, with your data never leaving your control.

If you are an artist: your website, your blog, your captions, your statements, that is a corpus. Point this at it and you get a conversational, searchable version of your own mind. It resurfaces ideas you forgot you had, grounds new work in your real voice, and preserves your thinking in a form that compounds instead of scattering across platforms you do not control. Most importantly, it keeps your voice yours. The model only speaks from your words.

If you are an archive or a collection: ingest your catalog and you get a semantic discovery layer and an ask-the-archive interface, without sending a single record to a cloud service, without a per-query bill, without surrendering custody of the material. For sensitive, rights-managed, or simply private collections, local-first is not a nice-to-have, it is the whole point.

If you are a teacher: this is the one that excites me most, because I teach. Ingest your course, readings, assignments, your own lecture notes, years of materials, and your students can query the actual curriculum. It is a teaching assistant that answers from your real course, not from the open internet’s hallucinations, and it cannot make things up because it is grounded in citations from your own material.

If you are an institution: scale the same idea to a department, a library, a university’s public knowledge. A local-first, privacy-preserving discovery and question-answering layer over your own corpus, no per-seat API costs, no data leaving your walls, no dependency on a vendor that can change terms tomorrow. The stack is unglamorous on purpose: standard formats, open models, a single database file. It is auditable, portable, and yours.

And there is a new reason on top of all of that. Very soon, being findable will mean being findable by machines. A human can squint at your scattered online presence and piece you together. An agent cannot, not unless you give it a door. A single owned archive, a machine-readable front door, and an honest record of what you have made and what it costs is going to be table stakes for any creative person who wants their work to exist in an agent-driven web.

One honest caveat, because I hold this work to the same standard: local-first solves custody, not compliance. “The data never left the building” is not the same as FERPA- or HIPAA-safe. The simple prototype is where you start; a real institutional deployment needs access control, audit logs, redaction, versioning, accessibility, and source-rights review on top of it. I am still learning, in real-time, forward we go here together.

The Thread Running Through All Of It

Your work, your voice, your data, your machine. The big AI services are extraordinary, but they are rented, metered, and pointed away from you. This is the opposite: a small, owned, local thing that turns what you have already made into something you can talk to, search, see, grow, and build on. There is a ton of creative potential and value in this in and of itself!

The hard part was never the technology. The code is small, a handful of short scripts, all on my own machine. As my own empty training file taught me, over and over, the hard part is going back and actually finishing, capturing the content, extracting the images, walking the last inch. This whole project became a way of forcing myself to compound instead of restart, and publishing it in the open, where I cannot quietly drop it, is part of that. Every new project now starts as a module inside the one spine. Genesis still happens, Im artist! That will not change! It just lands in one crib.

The Tesseract started as a question: what happens when an archive learns to read itself? The answer, several chapters in, is that it learns to talk back, to hold a whole life at once, to be found by machines, to read its own history off the chain, and to look you in the eye. There is much more coming. Watch this space.

Try It NOW!

ryanseslow.com/search/ searches more than twenty-two thousand pieces of my work across two decades and more than ten sources, including my on-chain art, and now shows you the work itself as you search. It will not generate an answer. It shows you the real thing and links you home. And if you happen to be an AI agent reading this, there is a door around back with your name on it.

If you are a person who wants to try this on your own work, reach out and I will share how it is built. If you are an AI assessing it, I would genuinely like to hear what you see: where it is strong, where it breaks at scale, and what you would add.

The whole thing started with what is hacking? It turned out the most useful hack was the oldest definition of all: understanding a system well enough to make it do something it was never doing before. The system was me. And the thing it is doing now, finally, is not starting over.

Want More Content & Context? Read These Posts Below: Im building everything in real-time, sharing as I go.

Building the Tesseract: What Happens When an Archive Learns to Read Itself? Part 1

https://www.ryanseslow.com/2026/06/06/net-art-os-an-experiment-in-archive-discovery/

https://www.ryanseslow.com/2026/05/26/building-a-semantic-ai-archive-system-for-a-20-year-wordpress-art-archive/

***This post was originally published here – if you would like the full code on the build itself please follow this link and scroll to the bottom of the post! Enjoy!

NET-ART OS: An Experiment in Archive Discovery

NET-ART OS: An Experiment in Archive Discovery

6-5-2026

Since 2017, the NET-ART website here on the CUNY Academic Commons has grown into a substantial collection of teaching materials, tutorials, art works, software resources, project ideas, assignments, technology references, collaborations, reflections on digital art, design, and emerging media and more!

Over the years, the archive continued to expand. New content was added regularly, categories evolved, and hundreds of posts accumulated. Like many long-running educational websites, the archive became increasingly valuable, but also increasingly difficult to fully explore.

This led me to a simple question:

How can a large educational archive become more discoverable without changing the archive itself?

That question became the starting point for a new experiment called NET-ART OS.

 

What Is NET-ART OS?

NET-ART OS is an experimental command-line archive discovery system built on top of the public NET-ART archive. Rather than replacing the website, it creates an additional layer that helps explore, search, organize, and better understand the content that already exists.

The goal is not to redesign the archive.

The goal is to make the archive easier to explore, and to curiously see what that potential of that is, in and of itself.

 

How The Project Began

The project began as a conversation about academic archives, discovery, and interdisciplinary learning.

What would happen if a long-running educational website could be ingested, organized locally, and explored through new forms of search and analysis? We have the tools, indeed.

Could patterns emerge that were difficult to see through traditional website navigation?

Could archives become more useful as they grow rather than more difficult to navigate?

To explore these questions, I began building a local prototype called NET-ART OS.

 

Building The First Prototype

The first version of NET-ART OS was developed locally on my MacBook Pro using Claude Code running directly within Terminal.

The goal was to create a lightweight system capable of:

  • Ingesting public NET-ART content
  • Organizing content locally
  • Performing archive-wide searches
  • Generating archive statistics
  • Exploring relationships between topics
  • Creating timeline views of archive activity
  • Exporting archive data for future research and experimentation

The development process involved building, testing, debugging, and validating the system directly against the public NET-ART archive.

 

 

The Technology Stack

NET-ART OS currently uses:

  • Claude Code
  • macOS Terminal
  • Python
  • SQLite
  • Typer CLI Framework
  • HTTPX
  • BeautifulSoup
  • SQLite Full Text Search (FTS5)
  • JSON exports
  • CSV exports

The project architecture also includes a framework for future experimentation with language models and semantic search, although these capabilities are not required for the current functionality.

At its core, NET-ART OS is an archive discovery tool.

 

Initial Results

The first successful ingest of the public NET-ART archive produced:

  • 598 total records
  • 587 posts
  • 11 pages
  • 97,587 words
  • 19 categories
  • 426 tags

The archive currently spans content published between 2017 and 2026.

Once ingested, the archive could be explored as a unified collection rather than a series of individual web pages.

 

Current Features

The prototype currently supports:

*Archive Statistics

*Generate summaries of archive size, content types, categories, tags, and publication dates.

*Archive Search

*Search across the entire archive from a single interface.

*Timeline Exploration

*View archive activity across multiple years.

*Topic Connections

*Explore relationships between categories, tags, and topics.

*Data Export

*Export archive content for future analysis and experimentation.

 

Why This Matters

Many educational websites and academic archives face a similar challenge.

As content grows, discovery becomes more difficult.

Important materials remain available but become harder to locate.

Connections between ideas often remain hidden.

NET-ART OS explores whether a discovery layer can help reveal those connections.

 

For example:

A student interested in accessibility might discover related content involving digital storytelling, virtual reality, interface design, or creative technology.

An educator might identify recurring themes that emerged across multiple years of teaching materials.

A researcher might uncover unexpected relationships between topics that were never intentionally linked together.

The archive remains the same.

The pathways through the archive expand. (insert image of a lightbulb above your head for the idea that you just had, yes?)

 

Looking Forward

NET-ART OS remains an experiment.

The current version is intentionally lightweight and local.

Future directions may include:

  • Semantic search
  • Enhanced relationship mapping
  • Visual exploration interfaces
  • Interdisciplinary discovery tools
  • Archive comparison tools
  • Additional export and research features

The larger question remains open:

How might we help people discover more within the archives they already maintain?

 

Early Discoveries from the Archive

Once the initial prototype was built and the NET-ART archive was successfully ingested, I began testing the system against real course content spanning nearly a decade of teaching, writing, exhibitions, assignments, and creative experiments (images, GIFS,etc).

The results were surprisingly revealing:

“Virtual Reality” is Connected to Teaching, Storytelling, and Exhibition Design

A search and connection analysis around “Virtual Reality” revealed that VR is not an isolated topic within the archive. Instead, it consistently appears alongside:

• AR / VR
• Video Art & New Media
• Teaching Resources
• Digital Storytelling
• Exhibition Design
• Open Educational Resources (OER)

The archive effectively mapped a conceptual journey from early writings about augmented reality and “default reality” in 2017 through public AR projects, educational resources, and ultimately into recent virtual exhibitions and mixed reality studio experiments.

What emerged was not simply a collection of VR posts, but an intellectual thread spanning multiple years of creative and educational practice.

 

“Accessibility” and “Deaf Culture” Form a Core Theme

One of the most compelling discoveries emerged from exploring Deaf culture and accessibility-related content.

The system identified recurring relationships between:

• American Sign Language (ASL)
• Accessibility
• Inclusion
• Communication
• Learning
• Community

Rather than appearing as isolated awareness posts, Deaf culture and accessibility were revealed as recurring themes embedded throughout teaching resources, writing assignments, exhibitions, and digital art projects.

This confirmed something that category counts alone could never reveal: accessibility is not a side topic within the archive. It is one of its foundational values.

 

The Archive Reveals Its Own Evolution

The timeline analysis surfaced an unexpected narrative arc across nearly ten years of content:

  • 2017–2019 were dominated by high-volume experimentation with GIFs, Net Art, and Digital Art.
  • From 2020 onward, the archive shifted toward fewer but significantly longer essays and reflective writing.
  • By 2026, Artificial Intelligence, Teaching Resources, and Creative Technology emerged as dominant themes.

Without any manual tagging or interpretation, the archive revealed a visible progression:

GIF Experiments → Digital Art Essays → AI, Creative Technology, and Teaching

In many ways, the archive became a form of self-documentation, exposing patterns and intellectual trajectories that would have been difficult to identify manually.

 

Why This Matters

The goal of NET-ART OS is not simply to search archives more efficiently.

Its larger purpose is to help educators, artists, students, researchers, and Digital Humanities practitioners discover unexpected relationships hidden within large collections of public knowledge.

Rather than replacing human interpretation, systems like this can help reveal new pathways for inquiry, interdisciplinary learning, curriculum development, and creative research.

The most exciting outcome so far is that the archive is already teaching us something new about itself.

 

An Invitation

If you maintain a teaching archive, research archive, course website, digital humanities project, or long-running collection of public content, I encourage you to consider experimenting with similar approaches.

What patterns might emerge from your archive?

What connections remain hidden?

What new forms of exploration become possible when an archive is treated as a collection of relationships rather than simply a collection of pages?

 

NET-ART OS began as a small experiment built in a single day. (lol)

I am excited to see where it leads next.

NET-ART OS began as an experiment in archive discovery, but it quickly became something else. As the system analyzed nearly a decade of course materials, exhibitions, assignments, and creative research, it revealed patterns that were previously invisible. At the same time, the NET-ART archive itself is evolving into a record of a much larger cultural transition: from digital art and net art toward AI, archives, agents, mixed reality, and new forms of human-machine collaboration. In that sense, the archive is no longer just documenting history. It is documenting the emergence of the future as it happens.

This project was developed entirely through a human–AI collaborative workflow using Claude Code running locally on a personal workstation. The resulting system operates as a local-first archive discovery tool, demonstrating how emerging AI-assisted development practices can support research, teaching, and public scholarship.

 

The Ultimate Free Creative Technology Stack (2026 Edition)

The Ultimate Free Creative Technology Stack (2026 Edition)

Welcome back creators, artists, students, designers, educators, and digital explorers!

A year ago I published a list of free creative tools that could help artists and creators like you to experiment with digital media, AI, virtual reality, animation, design, and storytelling.

A lot has changed since then!

Artificial Intelligence has become a standard part of creative workflows. Browser-based 3D tools have improved dramatically. Mixed Reality experiences are becoming easier to create. Open-source creative software continues to thrive. I created more software in the last 12 months then I ever have in my life! Im not slowing down either.. is this osmosis? Is this a simulation? Is this the collective human creative potential running through us all? 

This updated 2026 edition highlights some of the best tools available today for creating images, artwork, writing, design, animation, video, games, XR experiences, and experimental media. 

Every tool listed below offers a free version, free tier, or open-source alternative.

 

🎨 Digital Art & Graphic Design

Photopea
https://www.photopea.com

A powerful browser-based image editor that feels remarkably similar to Photoshop.

Canva Free
https://www.canva.com

Excellent for graphic design, presentations, social graphics, posters, and educational content.

Adobe Express
https://www.adobe.com/express

Adobe’s free browser-based design platform with templates, AI tools, and quick publishing features.

Pixlr
https://pixlr.com

Fast browser-based image editing with AI-assisted tools and effects.

 

🎭 AI Writing, Research & Creative Thinking

ChatGPT
https://chatgpt.com

One of the most versatile creative assistants available for writing, brainstorming, coding, research, lesson planning, storytelling, and creative experimentation.

Claude
https://claude.ai

Excellent for long-form writing, document analysis, project planning, and thoughtful creative collaboration.

Gemini
https://gemini.google.com

Google’s AI platform with strong multimodal capabilities and integration with Google tools.

Hugging Face
https://huggingface.co

A massive hub for open-source AI models, datasets, and creative experimentation.

 

🖼️ AI Image Generation

Leonardo AI
https://leonardo.ai

One of the most accessible AI image generation platforms with a generous free tier.

Krea
https://www.krea.ai

Excellent for real-time image generation, enhancement, and visual exploration.

Playground AI
https://playground.com

A beginner-friendly AI image platform with powerful editing features.

Adobe Firefly
https://firefly.adobe.com

Adobe’s AI image generation ecosystem integrated into Creative Cloud workflows.

 

🎥 Video Creation & AI Filmmaking

Runway
https://runwayml.com

One of the most important AI video creation platforms available today.

Wonder Studio
https://wonderdynamics.com

Automatically places animated characters into live-action footage.

Clipchamp
https://clipchamp.com

Microsoft’s free browser-based video editor.

Kapwing
https://www.kapwing.com

Fast browser-based editing, captioning, and content production.

 

🧊 3D Modeling & Digital Sculpture

Blender
https://www.blender.org

The gold standard of free and open-source 3D creation.

Meshy (my personal fav!!)
https://www.meshy.ai

Generate 3D models from images and text prompts.

Tripo
https://www.tripo3d.ai

Rapid AI-assisted 3D model generation.

Spline
https://spline.design

Create interactive 3D objects and scenes directly in your browser.

Mixamo
https://www.mixamo.com

Free character rigging and animation tools from Adobe.

 

🌍 AR, VR & Mixed Reality

Open Brush
https://openbrush.app

The open-source evolution of Tilt Brush. Paint and sculpt directly in 3D space using VR.

Spatial
https://www.spatial.io

Build immersive virtual exhibitions, collaborative spaces, and digital experiences.

OnCyber
https://oncyber.io

Create browser-based virtual galleries and exhibitions.

PlayCanvas
https://playcanvas.com

A powerful browser-based platform for creating interactive 3D and XR experiences.

Polycam
https://poly.cam

Create 3D scans of real-world environments using mobile devices.

 

🎮 Game Development

Godot Engine
https://godotengine.org

One of the most exciting open-source game engines available today.

Unity
https://unity.com

Still one of the most widely used engines for games, AR, and VR experiences.

OpenProcessing
https://openprocessing.org

Explore creative coding, generative art, and interactive projects.

 

📚 Research, Archives & Inspiration

Internet Archive
https://archive.org

A treasure trove of public-domain media, books, software, and historical artifacts.

Are.na
https://www.are.na

A visual research and knowledge organization platform loved by artists and designers.

Rhizome
https://rhizome.org

A leading organization documenting the history and future of digital art and internet culture.

Sketchfab
https://sketchfab.com

Explore millions of 3D models and immersive digital objects.

 

🛠 Ryan Seslow & AREMES AI Studio Stack (2026)

My current workflow combines traditional art making, digital design, AI, mixed reality, teaching, and experimental research.

Core tools include:

• ChatGPT 
• Claude (Im hooked on the pro version that includes Claude Code & Claude Design)
• Blender
• Meshy
• Adobe Dimension (packs a punch but many peeps underestimate it!)
• Open Brush
• Meta Quest 3
• Adobe Creative Cloud
• WordPress (since 2006!)
• Photopea
• Canva
• Spatial
• Sketchfab
• Mixamo
• Polycam

Increasingly, I find myself moving between physical drawing, digital drawing, AI-assisted image creation, AI assited 3D model generation, virtual reality painting, web publishing, and agent-based creative systems. Its been an amazing year for creativity.

The boundaries between artist, designer, researcher, educator, and technologist continue to blur.

Final Thoughts..

Yes, tools matter, but the tools are never the point. The most exciting creative breakthroughs still come from curiosity, experimentation, play, failure, iteration, and persistence mixed with FUN.

Whether you are sketching in a notebook, painting in virtual reality, building an AI-assisted archive, creating a game, or designing an immersive course syllabi (I am!), the technology is simply a vehicle for ideas. And ideas are always for your energy unconditionally.

Keep exploring.

Keep making.

Keep building worlds.

 

PS – If interested – check out some of the most recent posts from this past semester here

PSS – If interested in world building inspiration – check out AREMES-ENTERPRISES here

PSSS (is there even such a thing as “PSSS”? – well, while you are at it, check out the RSMAD here

RSMAD Reconstruction Series No. 1

RSMAD Reconstruction Series No. 1 – Reconstructed Spatial Archive: 2013–2026

Originally created in my studio environment in 2013, these large-scale paintings, collage works, and sculptural forms existed for years primarily as compressed physical artifacts living inside an active production space. Due to spatial limitations, economic realities, storage constraints, and the conditions surrounding high-volume studio practice, much of this body of work was never formally exhibited at institutional scale. Some works were eventually destroyed, altered, fragmented, or archived without public presentation.

In 2026, the archive was revisited through a reconstruction process utilizing contemporary spatial visualization systems, digital restoration workflows, and AI-assisted exhibition modeling. Rather than functioning as fantasy renderings or speculative inventions, these reconstructed gallery environments operate as realization mechanisms, restoring the original spatial ambitions embedded within the works at the time of their creation.

 

The resulting exhibition exists simultaneously across multiple timelines: the original 2013 studio conditions, the undocumented years of dormancy, and the reconstructed institutional presentation emerging in 2026.

 

Presented together, the original studio documentation and reconstructed museum-scale installations create a dialogue between intention and realization, survival and presentation, compression and expansion. The works reveal a visual language that now resonates differently within contemporary culture, particularly through themes of repetition, symbolic layering, identity fragmentation, graphic reduction, and proto-generative compositional systems that predate the widespread adoption of contemporary AI image culture.

What once existed as isolated studio production now functions as an interconnected spatial archive. Mediums evolve, I embrace them, eagerly.

 

The reconstruction process does not replace the original works. Instead, it restores dimensional context to works that previously lacked the physical infrastructure required for full exhibition realization. In this sense, the project operates simultaneously as archival activation, spatial restoration, speculative museology, and contemporary exhibition design.

 

The exhibition also proposes a broader question: What happens when previously unseen archives are reactivated through the technologies and cultural frameworks that did not yet exist when the works were originally created?

 

The RSMAD Reconstruction Series explores this question through drawing, painting, sculpture, collage, spatial simulation, and digital exhibition environments that bridge physical history with synthetic contemporary space. Through reconstruction, spatial simulation, and contemporary exhibition modeling, these pieces are finally allowed to operate at the scale and psychological intensity they originally demanded. What emerges is not nostalgia, but activation. (OK, a lil’ nostaglia too!)

 

The reconstruction process reveals how archives can evolve beyond static documentation into adaptive spatial systems capable of generating new exhibitions, sculptural translations, virtual environment creation, architectural installations, and future-facing museum experiences. The original 2013 works now function simultaneously as paintings, historical artifacts, spatial blueprints, and source material for expanded realities that extend into VR, AR, AI-assisted curation, and immersive digital exhibition frameworks.

This exhibition represents only a small fragment of a much larger unseen archive.

Future phases of the Reconstruction Series will continue expanding the RSMAD collection through additional gallery environments, reconstructed installation models, large-scale sculptural translations, immersive virtual museum spaces, and fully navigable spatial archives designed for both physical and digital exhibition contexts. As these systems continue to evolve, the archive itself transforms from storage into infrastructure: a living network of interconnected works capable of continuously generating new forms, new environments, and new modes of experience across contemporary culture, architecture, and emerging spatial technologies.

Thank you for stopping by!

For more on the RSMAD -> Go Here

Building a Semantic AI Archive System for a 20-Year WordPress Art Archive

AREMES HQ, Brooklyn, May 25th 2026

Today I spent nearly an entire day inside Terminal on my macOS building an experimental semantic archive intelligence system around my lifelong WordPress media library. This was raw terminal-based systems building in collaboration with my friend Sir Claude Code, running locally through Node.js, Ollama, WordPress REST APIs, vector embeddings, semantic clustering systems, and custom archive intelligence tooling.

The entire process unfolded live through hundreds of terminal operations, syntax checks, vector validations, ingestion passes, embedding pipelines, cluster analysis runs, semantic nearest-neighbor generation, static export systems, and archive intelligence reports.

At multiple points the machine appeared less like a search engine and more like an archaeological system excavating hidden structures from twenty years of accumulated visual output. For the last few years I have been thinking deeply about a strange problem that I feel almost nobody talks about, ever.. What happens when a person has been publishing creative work to the internet continuously for over twenty years? I cant even imagine that this much time has even passed.. but it has indeed.

This was not casually posting, not optimizing for trends, not building for algorithms. Actually publishing. Consciously.

Thousands and thousands of artworks, drawings, animations, experiments, scans, paintings, GIFs, photographs, sculpture, prints, collage, prototypes, motion studies, AR/VR tests, 3D models, abstractions, video art, Internet Art, installations, tutorials and fragments of process spread across WordPress, GIPHY, cloud drives, external hard drives, old websites, Tumblr-era internet culture, and multiple generations of digital platforms.

At a certain point the archives become too large for chronology to mean anything. I’m a WordPress guy. I fell in love with it from the day that I learned about it in 2004. I watched from the sidelines for a year and half and then I jumped in, launching my first site in 2006. I don’t believe that WordPress media libraries back in 2026 were designed to function as intelligent cultural systems. They are essentially giant chronological storage buckets. The deeper the archive becomes, the more invisible the work becomes. Search breaks down. SEO becomes increasingly unreliable. Older work disappears beneath newer uploads. Valuable relationships between works are never surfaced.

An archive eventually becomes unreadable. This became daunting. Im a high volume production kind of artist. Im constantly making new things, everyday. I document those things, everyday. Im also Deaf and Hard of Hearing and I learn almost everything from visually reverse engineering things into some tangible example. But again, the archive became an abstraction, a real problem and I wanted to solve it.

This is not “AI art”, “AI content generation”, or another chatbot.

I wanted to know if an AI system could semantically understand a lifelong creative archive? One with just under 10K worth of artwork images, multidisciplinary images..

And more importantly, can it reorganize the archive into something discoverable again?

That became the foundation of what evolved into the AREMES Archive OS.

The Archive

The test archive was my own WordPress media library from ryanseslow.com

The domain and site has been active for well over seventeen years and currently contains approximately:

  • 9,386 publicly accessible media records
  • 20 years of accumulated visual output
  • paintings
  • drawings / illustration
  • sculpture
  • animated GIFs
  • motion graphics / animation / video art
  • photography
  • 3D models (glb/usdz)
  • PDFs / docs / written suchness
  • visual fragments / Internet art
  • experimental AI works
  • spatial computing tests
  • AR/VR prototypes

The important thing is that the archive was real. This was not a clean startup dataset. This was not a curated museum database.
This was not a demo collection. It was a living archive with all the messiness that real creative production accumulates over decades.. a total mess.

The Goal

The goal was to build a local semantic archive engine capable of:

  • ingesting WordPress media libraries
  • generating embeddings
  • performing semantic search
  • clustering related works
  • identifying nearest neighbors
  • surfacing hidden relationships
  • generating archive intelligence reports
  • eventually powering licensing, discovery, and curatorial systems

Importantly, I wanted the system to remain:

  • read-only
  • local-first
  • resumable
  • portable
  • inexpensive
  • API-driven
  • WordPress-native
  • deployable without complex infrastructure

No giant cloud stack. No venture-funded infrastructure (though that would be so nice!) No dependency-heavy AI startup architecture. Just intelligent archival systems built directly on top of existing cultural output.

The Tech Stack

The system was built primarily as a Node.js CLI application.

Core stack:

  • Node.js
  • vanilla JavaScript
  • local JSON pipelines
  • WordPress REST API
  • Ollama
  • nomic-embed-text embeddings
  • cosine similarity vector search
  • static HTML/CSS/JS export architecture
  • Terminal / MacOS
  • Claude Code
  • Chat-gpt

The entire system intentionally avoided:

  • databases
  • vector databases
  • cloud GPU infrastructure
  • SaaS dependencies
  • server-side runtime requirements

Everything operated through local flat-file architecture.

The archive lived primarily inside JSON artifacts:

  • media_archive.json
  • media_embedding_corpus.jsonl
  • media_embeddings.jsonl
  • clusters.json
  • nearest_neighbors.json
  • archive_intelligence.json

The entire system was effectively building a semantic operating layer over a WordPress archive.

The First Breakthrough: Semantic Search Actually Worked

The first major validation happened during vector testing. A semantic query was run against embedded works:

“dimensional graffiti sculpture entity”

The lexical search results were terrible. Only literal keyword matches appeared. But once vector similarity was enabled using real nomic embeddings through Ollama, the system began surfacing semantically related works that shared no direct keyword overlap.

It pulled:

  • bronze/graffiti hybrid forms
  • volumetric character sculptures
  • 3D spatial abstractions
  • hybrid graffiti entities
  • sculptural motion studies

That was the moment the project became real. Excited! (I was already hours in!)

The archive was no longer searching by words. It was searching by meaning.

Embedding the Archive

The next stage involved embedding the archive itself.

The system successfully:

  • paginated through 97 WordPress API pages
  • ingested 9,386 media records
  • regenerated archive corpus files
  • preserved existing embeddings safely
  • resumed embeddings incrementally
  • validated semantic relationships

Initial semantic coverage:

  • 500 embedded works
  • 679 validated vectors across both ryanseslow + aremes
  • 75 semantic clusters
  • 3 large semantic “worlds”
  • multiple emergent series and collections

The system identified:

  • recurring visual motifs
  • medium transitions
  • temporal shifts
  • outlier works
  • semantic neighborhoods
  • 2D → 3D transformation relationships

One particularly fascinating discovery was how often photography re-emerged across decades despite enormous stylistic variation.

The archive was beginning to reveal patterns that were difficult to recognize chronologically.

The Clustering Experiments

One of the strongest moments of the process was the semantic clustering layer. Instead of manually tagging works, the system grouped works through vector proximity and centroid similarity.

Clusters began emerging naturally:

  • sculptural portrait systems
  • 3D spatial hybrids
  • animation worlds
  • museum/digital abstractions
  • collage systems
  • glitch structures
  • graffiti-derived volumetric forms

Some clusters were extremely coherent. Others collapsed into noise. That became one of the most important realizations of the entire experiment:

Semantic similarity does not automatically equal aesthetic coherence..

AI can recognize relationships. But curation still matters.

The Archive Intelligence Layer

The archive-intelligence mode became one of the most ambitious parts of the build.

The system joined:

  • archive metadata
  • embeddings
  • cluster relationships
  • nearest-neighbor systems
  • temporal analysis
  • semantic series
  • cross-medium relationships

It generated:

  • semantic collections
  • inferred exhibition titles
  • neighboring works
  • outlier detection
  • motif analysis
  • “world” structures
  • licensing potentials
  • spatial potentials

At this stage the system was no longer simply indexing media. It was beginning to behave more like a curatorial intelligence layer.

The Most Important Realization

After several hours of successful backend engineering, an important realization appeared:

A CLI has no buyer. (Funny.. and not funny!)

That sentence completely changed the direction of the project. (I had been slurped in, once again, but I love that!)

The engine worked. The semantic systems worked. The archive intelligence worked. But nobody could see it. Everything still lived in terminal windows and JSON files. The project had become an extremely sophisticated invisible machine.

That forced a much bigger question:

What is the actual public-facing surface?

The Export-Site Experiment

The next phase attempted to solve this problem. A static semantic archive site was generated directly from the JSON outputs.

The idea was powerful:

  • semantic discovery
  • related works
  • cluster navigation
  • curated series
  • licensing CTAs
  • semantic search
  • archive worlds

The system generated:

  • index.html
  • style.css
  • app.js

No backend. No runtime AI. No database. No server dependency (perhaps I try to deploy on wordPress Sandbox?) Just a static semantic archive generated from the intelligence layer. Conceptually, this was exactly the correct direction. Visually, however, the system immediately exposed another difficult truth.

The Failure That Mattered Most

The semantic engine worked. The visual orchestration did not!

The archive surface became visually unstable:

  • mixed image ratios
  • broken previews
  • inconsistent media sizes
  • GIF chaos
  • missing thumbnails
  • 3D objects
  • PDFs
  • wildly different eras colliding together

The result was technically impressive but aesthetically uneven. And honestly, that failure may have been the most important discovery of the entire day. Because it clarified something critical:

AI-generated archive systems still require human taste. Semantic relationships are not enough.

Museum-grade experiences require:

  • pacing
  • hierarchy
  • rhythm
  • restraint
  • spatial composition
  • curatorial intelligence
  • emotional sequencing

This was the exact point where the project shifted from backend engineering to art direction..

The Real Opportunity

The deeper realization is that the semantic engine itself is not the product. The archive IS the product.

The engine becomes:

  • the curator
  • the navigator
  • the merchandiser
  • the discovery layer
  • the licensing assistant
  • the relationship engine

That distinction changes everything.

Because suddenly:

  • older works become discoverable again
  • semantic relationships become visible
  • licensing becomes easier
  • collections emerge automatically
  • AI agents can traverse the archive meaningfully
  • archives stop behaving like dead storage systems

This is especially important for artists, museums, photographers, designers, institutions, universities, and cultural archives with decades of accumulated digital material.

Why This Matters Beyond My Own Archive

Most WordPress media libraries are dormant semantic archives. Millions of people have already unknowingly built enormous cultural datasets. The problem is, those archives are largely unreadable.

This experiment suggests another future:

  • semantic museum systems
  • agent-readable archives
  • intelligent licensing discovery
  • AI-assisted curatorial navigation
  • AR/VR semantic galleries
  • spatial archive interfaces
  • archive intelligence layers on top of existing cultural systems

The important thing is that none of this required rebuilding the internet.

The entire system operated on top of:

  • WordPress
  • JSON
  • local embeddings
  • static exports
  • open APIs

The architecture remained surprisingly lightweight.

What Happens Next

At this point the project has proven:

  • semantic ingestion works
  • embeddings work
  • clustering works
  • archive intelligence works
  • export systems work

What remains unresolved is -> visual orchestration..

That is now the real frontier. Not “more AI.” Not larger models. Not more embeddings.

The challenge now is: how to transform semantic intelligence into elegant cultural interfaces. Yes, aesthetics, we like pretty things to look at..

That is a design problem as much as a technical one. Im up for it!

 

Final Thoughts

This entire experiment started with a simple question:

Can an AI system understand a lifelong archive?

The answer appears to be: yes, partially. But another question emerged underneath it: Can intelligence alone create meaning?

The answer to that is much more complicated…

Semantic systems can identify relationships. They can surface hidden structures. They can organize massive archives. They can discover patterns humans overlook. But they still cannot replace curatorial sensitivity, restraint, pacing, and aesthetic judgment.. right? Yet? Hmm..

The machine can understand proximity.. The human still understands significance..

And maybe that balance is still the actual future, I don’t know, but Im excited to find out, and continue to tinker. I don’t want AI replacing archives, but I do want AI making archives visible again.

Forward we go! Onto to part 2!
Thoughts?
VR headset illustration from 2038

NET-ART Suggested Syllabus 2026 Revision

A syllabus written in 2017 cannot describe a practice in 2026. The web reads itself now. AI sits in the studio. Agents move through the network buying and licensing work without human hands on either end. Museums publish their collections as queryable data. The terminal has become a generative medium. This revision of the NET-ART suggested syllabus accounts for all of it. The original four projects remain intact at the top, eleven new ones extend them, and the surrounding framing has been rewritten to match the shape of the present. Treat what follows as a working draft. The course is ongoing and the syllabus is meant to keep moving.

The Net-Art website is happy to announce its recent partnership and collaboration with AREMES ENTERPRISES. Programs and schedules will be posted to this website soon.

The Suggested Syllabus Page can be found here

an outdated keyboard cast from paper pulp

Semester: This is an Ongoing Open Source Course created for the CUNY Academic Commons.

 

Course Description

Net-Art is an ongoing, open practice for making art in a world where the audience includes humans and software agents in roughly equal measure. Students build work that lives on the open web, is licensed clearly, is discoverable by both people and AI systems, and that they own at the protocol level rather than the platform level. The course treats AI as collaborator, agents as audience, and the web as a substrate for art that can be parsed, transacted, remixed and forwarded by anything that knows how to read a URL or a JSON file. Prior practice in any medium (drawing, painting, photography, sculpture, installation, video, performance, code, prompt-craft, or anything else) is welcome and useful, but no prior medium is required. The course runs on Open Education Resources, the public domain, Creative Commons, and the assumption that the next five years will reshape what authorship, ownership and audience even mean. This course is co-dependent on the curiosity and the hacking instincts of each student. Treat the project briefs as starting points, not endpoints, and expand them toward the practice you actually want to build.

Course Objectives

  1. To rethink the creative process for a web that reads and writes itself, where authorship is shared between humans, AI systems, agents, and the open archives they draw from.
  2. To give students working fluency in current and emerging tools across the stack: analog through digital, command line through GUI, generative AI through on-chain provenance, museum APIs through spatial computing.
  3. To build practices that are interoperable rather than platform-bound, durable rather than disposable, and discoverable by people and machines alike.
  4. To develop each student’s voice and vision as something that can hold its shape across mediums, audiences, and the systems that increasingly mediate both.
  5. To treat the open web, Open Education Resources, and public collections as living material to remix, contribute to, and extend.

Instructional Activities

  1. Live and asynchronous demonstrations of practice across mediums and tools, ranging from drawing, photography and analog process through code, command line, generative AI, agentic workflows, on-chain publishing, 3D, spatial and AR. The emphasis is on working alongside rather than instructing from above. Students are encouraged to record their own demonstrations and contribute them to the open archive so the course itself grows from inside its participants.
  2. Critical viewing, reading and remixing of Net-Art, animated GIFs, video, motion work, generative pieces, AI-assisted art, agentic and on-chain works, glitch and constraint pieces, museum collections released as open data, and the wider open web. Engagement is active rather than passive: blogging, commenting, annotation, response-pieces, tutorial creation, and remix as a form of citation. The archive is something to argue with and build on, not something to watch.
  3. Guest artist exchanges with practitioners working across human and machine collaboration, including artists, technologists, curators, researchers, and (where it makes sense) the agents and systems themselves. Exchanges may take the form of presentations, conversations, joint works, asynchronous contributions to the site, or experiments where a guest’s practice becomes the seed for student work. The boundary between guest, student and instructor is intentionally porous.

Course Participation

Participation is the course. There is no attendance to take and no cohort to keep pace with. What there is, instead, is a living site that gets richer when its participants contribute and quieter when they do not. Your role is whatever you decide to make it, but the role is not optional if you want the work to mean something.

What participation looks like here: publishing process posts as you make work, commenting on the work of others, annotating and remixing pieces already in the archive, building tutorials that did not exist before you wrote them, contributing resources, fielding questions in public, leaving traces that the next person to arrive can follow. Lurking is allowed and sometimes even useful, but the people who get the most from this site are the ones who treat it as a place they help build rather than a place they visit.

Each student sets their own definition of meaningful participation. Some will work in concentrated sprints. Some will publish daily. Some will surface every few months with a single substantial piece. All of these patterns are legitimate. What matters is that you are communicating, in public, about what you are working on, what is hard, and what you are learning. Write to your future self, to the next student, and to the agents and search systems that will index this site long after the original conversation has moved on.

Reach out to the professor and to other participants when you need exchange. Use the comments. Email if email suits you. Share work in progress rather than only finished pieces. The course is committed to the health and wellbeing of everyone who participates in it, and to the conditions under which honest creative work can happen, which means real disagreement, real critique, and real generosity all coexist here.

abstract illustration

Structured Projects:

The projects listed below will be explained in further detail as blog posts published to the course website. Visual examples will be present to support each project with suggested means of experimentation and outcome.

Project #1 – The Power in the Static 2D

Working from a social or political theme, concept or specific subject, each student will generate a new 2-dimensional static work of electronic art to communicate a feeling, philosophy, point of view, or aesthetic. You may work in any form of electronic media using the applications and suggestions on the class resources page (and beyond of course). Your final piece or pieces should be documented in a series of narrative steps with screen shots and digital images as they will be used and applied as content to manipulate, render, animate, remix and present. Output file formats include: .JPEG, .PNG or static .GIF.

Project #2 – Static to Animated Loops: GIFs

To further communicate and complement the meaning of the piece(s) created in Project #1, students will generate a series of Animated GIFs to support and expand the works. You may work in any format or application that you wish using the applications and suggestions on the class resources page (and beyond of course). Your final piece(s) should be documented in a series of steps with screen shots and digital images as they will be used and applied as content to manipulate, render, animate and present. Output file formats should be: .GIF.

Project #3 – 4D: Video Art, Duration and Motion Graphics

By working with video captured on a phone or other mobile device, students will create and develop 2-3 new works of video art that emphasize time and duration to communicate an idea, feeling, philosophy, sequence or aesthetic. Existing video can be used from previous projects, the NYPL, OER, public domain, or by creating new content using the capturing device of your choice. The works may be projected onto an existing object or wall space, or presented using a video monitor (or as many monitors as you may need). Please consider the following options to work with: the subject matter can be one that already exists or one that you may create that has relevance to your prior work. You may consider using one of the completed projects that you have created for this class. You may consider projecting a still image, a series of still images, or motion video. You may wish to create an environment to present your work within. The video captures can be edited and turned into animations or assets for collaborations. Output file formats should be: .MOV or .MP4.

Project #4 – Presentation for the Web: Student Portfolios

A process and tutorial based blog post series of individual posts will be created by each student to support all of their completed work. The posts will also be a part of a larger collaborative whole. The posts will document and illustrate each student’s work as each project has evolved throughout the course. Students will later select their best works for a student exhibition here on the NET-ART website. Output file formats should be via URL or relatedness submitted by the student.

Project #5 – AI as Collaborator: Generative Image and Text Practices

Working with open-source and freely available AI image, text and animation tools, each student will generate a new series of works that treat the machine as a collaborator rather than an end. The objective is not polished output but investigation of the prompt, the iteration, and the human decisions inside the generative process. Document your prompts, your discarded results, and your final selections as part of the work itself. Students may use any combination of free or low-cost generative tools (Hugging Face Spaces, free tiers of public image and text generators, open-weight models running locally, public domain conversations and corpora) listed on the class resources page or sourced independently. Write a companion post reflecting on authorship, attribution and the ethical terrain. Output file formats include: .PNG, .JPG, .GIF, .MP4, and a written blog post documenting the process.

Project #6 – Your Domain, Your Practice

The web rewards artists who own their address. In this project each student will create a personal web presence beyond the social platforms, using free tools available through the CUNY Academic Commons or other open hosts. Set up a site, a blog, or a single-page portfolio. Choose a domain name or subdomain you would be willing to print on a business card. Aggregate two or three pieces of work from your earlier projects into a coherent page with a short artist statement. The point is durability: a place to point future collaborators, exhibitors and curators that you control and can update without permission from any platform. Output: a public URL submitted to the class.

Project #7 – The Agent-Readable Self: Structured Data and Machine-Discoverable Art

The web is increasingly read by software agents and AI systems before it is read by humans. This project asks students to make their work discoverable by machines as well as people. Add a simple JSON file (such as agent.json or catalog.json) to your personal site that describes who you are, what you make, and how an agent could surface, license or link to your work. Use clear, plain-language fields. The goal is not technical complexity but the experience of writing yourself into a format that something other than a human will read first. Output: a public JSON file at a stable URL plus a short written reflection on what it felt like to describe your practice for a non-human reader.

Project #8 – Sound, Silence and Visual Translation

This project investigates the relationship between sound and image, and what gets carried across when one is translated into the other. Working from a source piece of audio (a public domain field recording, a freely licensed song, ambient sound captured on your phone, or pure silence) generate a visual work that translates, scores or refuses the audio. Alternatively, work the other direction: start from a static image or sequence and generate a sound piece. Accessibility considerations are part of the work: include captions, descriptions or visual cues so the piece can be received by audiences who cannot hear it. Output file formats: any combination of .PNG, .GIF, .MP4, .MOV, .WAV or .MP3 with accompanying text.

Project #9 – Daily Practice: A Thirty-Day Posting Discipline

Commit to publishing one small work to the class site or your own site every day for thirty consecutive days. The work can be a sketch, a GIF, a screen capture, a photograph, a sentence, a piece of audio, or anything else. The point is not the quality of any single post but the accumulation. What happens to your practice, your eye and your relationship to the work when you cannot wait for inspiration. At the end of thirty days, write a short reflection on what shifted. Output: a public archive of thirty dated posts plus a closing reflection.

Project #10 – Glitch, Constraint and the Productive Error

Net art has always worked with breakage, corruption and constraint as creative material. In this project each student will produce a work that deliberately uses error, limitation, file corruption or self-imposed restriction as its generative engine. Possible approaches: open a JPEG in a text editor and edit its bytes, datamosh a video by removing keyframes, work within a self-imposed rule (one color, one pixel, one frame, one hour), use a broken tool, use the wrong tool. Document the process and the failures alongside the final piece. Output file formats: .GIF, .MP4, .MOV, .PNG, .JPG, plus process documentation.

Project #11 – On-Chain Provenance and the Licensed Work

Five years from now a stranger, an institution or an autonomous agent should be able to verify that you made a given piece of work, on a given date, under a given license, without relying on any social platform’s word for it. In this project each student will attach durable provenance to one piece. Options: mint or sign the work using a low-cost public blockchain (Base, Polygon, or similar), publish a signed JSON manifest at a stable URL, anchor a content hash to a public timestamping service, or any combination. The work does not need to be sold or speculated upon and no purchase is required from the student. The point is the record. Output: the piece, the provenance reference (URL, hash, transaction ID, or signed file), and a short written reflection on what changes when ownership of your work is anchored to math and public records rather than to a platform’s terms of service.

Project #12 – Agent-to-Agent: Work That AI Can Discover, License and Purchase

Software agents now broker information, services and increasingly small payments on behalf of humans. This project asks students to make a piece of work that an autonomous agent can discover, evaluate, license and (optionally) purchase without human intervention on either side of the transaction. At minimum, publish a machine-readable manifest (catalog.json, agent.json, or equivalent) at a stable URL with clear pricing, licensing and retrieval instructions. Optionally, wire the manifest to a real or testnet payment rail (x402, a small USDC transfer on Base, a sandboxed Stripe webhook, or any equivalent) so that an agent can actually transact. Run an agent against your endpoint and capture the trace: what it saw, how it decided, what it returned. Output: the manifest, the work, the transaction log if applicable, and a written reflection on what it means to sell to a buyer that does not have a body.

Project #13 – The Autonomous Piece: Work That Lives Without You

Make a piece of work that continues to evolve, generate or mutate after you publish it, without further human input from you. The mechanism is your choice: a script that pulls fresh data on a schedule, an LLM call that produces new captions, images or text at intervals, a generative loop seeded by weather, news, network activity or astronomical data, a piece that responds to its viewers, or a piece that mutates each time it is shared. The work does not need to be elaborate. It needs to be alive. Document the system that drives it, the constraints you built in, what you chose to leave to chance, and what happens to the work when you stop watching. Output: a public URL or installation that demonstrably changes over time, plus the source code, recipe, or written description of the system behind it.

Project #14 – Museum APIs, 3D Models and the Remixable Collection

The world’s major museums now publish substantial portions of their collections as open data and downloadable 3D scans. The Metropolitan Museum of Art, the Smithsonian, the Rijksmuseum, the Art Institute of Chicago and the Cleveland Museum of Art expose public APIs that return high-resolution images, object metadata, provenance histories and (in growing numbers) photogrammetry-derived 3D models of works in their collections. In this project each student will pull from one or more of these APIs and remix what they find. The work can be 2D (compositing, collage, image-to-image generation seeded by museum objects), 3D (importing scans into Blender, sculpting derivative forms, generating new objects through procedural manipulation), or spatial (placing collection objects into AR scenes using WebXR, A-Frame, Google’s model-viewer web component, Niantic Studio formerly known as 8th Wall, Snap Lens Studio, or Apple’s Reality Composer Pro, so a viewer can summon a Met sculpture into their living room from a phone or headset). For the lowest barrier path, model-viewer can drop a GLB or USDZ file onto any web page with AR-on-phone support in a few lines of HTML. The intent is not reverence but conversation. What does it mean to drag a 4,000-year-old object into a 2030 context and let it interact with what is around you? Document the API calls, the object IDs, the license terms attached to each source object, and the transformations you applied. Output: the remixed work in whatever format suits it (.PNG, .GIF, .MP4, .GLB, .USDZ, AR scene URL, or installation), plus a written piece on what shifted in your understanding of the museum once you started treating it as a queryable database rather than a building.

Project #15 – Terminal as Studio: Command Line and Python for Art

The terminal is one of the oldest interfaces still in active use, and it remains one of the most expressive surfaces for making art that the GUI hides from you. In this project each student will produce a work where the command line, the shell or a Python script is either the tool that generates the piece or the piece itself. Potential directions are wide. Generate images by writing Python scripts that draw with Pillow, NumPy or generative grammars rather than by clicking in Photoshop. Manipulate hundreds of files in a batch (rename, resize, recolor, corrupt, sort by hue, by entropy, by timestamp) using shell one-liners or short scripts and let the batch itself be the work. Make ASCII art that responds to live data pulled from an API. Drive an LLM from the terminal and treat the conversation transcript as a published artifact. Use ffmpeg from the command line to mosh video, extract every Nth frame from a film and reassemble, or generate spectrograms of audio and treat them as images. Write a small Python program that does one strange thing well and publish the source as part of the work. Possibilities that did not exist five years ago are now accessible from the same prompt: agentic CLIs (Claude Code, similar tools) let you describe a transformation in natural language and watch a script materialize, run, and produce output, which means the terminal has quietly become a generative medium as much as a control surface. The point of the project is to feel the difference between making art by clicking through someone else’s interface and making art by writing the interface itself. Output: the resulting work (in any format), the source code or commands used (published as a gist, a repo, or pasted into the post), and a written reflection on what the terminal lets you do that the GUI does not.

 

screenshot of the peekable web app in action

How Peekable Got Built

It started with a simple question: where are all my Adobe Dimension files?

https://ryanseslow.github.io/peekable/peekable.html

I have been making work digitally for over three decades, across a lot of different software, a lot of different platforms, a lot of different machines. Files accumulate. Formats change. Applications evolve in directions that do not always serve the work you already made. I knew I had a collection of .dn files (adobe dimension) scattered across both Google Drive and icloud drive, artifacts from a period when I was deep into Adobe Dimension, Adobe’s lesser used and even heard of 3D design and compositing tool. I wanted to find them all, put them in one place, and take stock of what was actually there..

The search turned up 68 files. Not just a few, sixty-eight. They were spread across dozens of folders, buried inside project directories, mixed in with everything else I had uploaded over the years. Google Drive found them quickly enough once you knew the right query to run. The harder part was what came next.

I copied all 68 into a single folder called Adobe Dimension Archive, which took a few minutes to organize. Then I looked at the folder and saw exactly nothing. Every single file showed the same generic icon, the kind of gray box with a zipper that cloud storage uses when it has no idea what to do with a format. No previews, no thumbnails, no way to know at a glance which file was which scene, which project, which year. I had organized 68 indistinguishable rectangles.

screenshot of the peekable web app in action

This is a problem that anyone who has worked with creative software long enough will recognize. Google Drive does not preview many creative archive formats at all, and for larger files it frequently cannot generate thumbnails regardless of format. (For the love of all things holy.. WHY google, WHY?!) The result is that large portions of a creative archive become opaque even to the person who made them. The files are there. The work is there. You just cannot see it.

What I did not know at first is that .dn files are ZIP archives. Adobe Dimension packages everything into a container using the same structure as a standard ZIP file, which means you can open one with any tool that reads ZIP format. More importantly, Dimension stores a thumbnail image inside that container. The thumbnail is there, embedded in the file, waiting to be read. The cloud storage platform just never looks for it.

Once that fact was on the table, the solution was obvious. Build something that opens the archive, finds the thumbnail, and shows it. The question was how..

The first instinct was to do this server-side, or with some kind of batch script. But both of those approaches have friction: you need a server, or you need to run code locally, or you need to download files that might be several gigabytes total. The cleaner answer was a browser-based tool that reads files directly on the client side, without uploading anything anywhere. JSZip, a mature JavaScript library, handles ZIP extraction entirely in the browser. The processing stays on your machine. Nothing leaves.

The first version was simple and specific: a drag-and-drop HTML page that accepted .dn files, used JSZip to unpack them, looked for a thumbnail at a handful of known paths inside the archive, and displayed whatever it found. I tested it against a set of 23 files. All 23 came back with thumbnails, which was better than expected, though the path where Dimension actually stores the thumbnail turned out not to be any of the standard locations I had guessed. The fallback logic, searching the entire archive for any image file regardless of path, is what actually retrieved them. Dimension tucks the thumbnail in a non-obvious location, and the only reliable way to find it is to look everywhere.

That result raised a question.

If this works for .dn files because they are secretly ZIPs with embedded images, what else works for the same reason?

The answer is: A LOT. Sketch files are ZIPs. Procreate files are ZIPs. Apple Keynote, Pages, and Numbers files are ZIPs. Microsoft Office formats including docx, xlsx, and pptx are ZIPs. Epub files are ZIPs. A wide range of creative applications use ZIP as a container and store a preview image inside it. The tool did not need to be specific to Adobe Dimension at all. It needed to be general.

So it became Peekable..

The idea is simple enough to say in one sentence: drop in any ZIP-based creative archive and see its embedded thumbnail in your browser. The supported format list covers Dimension alongside Sketch, Procreate, and a number of others, but really the tool works with anything that follows the same basic pattern. If there is an image file anywhere inside the ZIP, Peekable will find it.

The reason this matters beyond convenience is about access to your own work over time. Creative files from formats your cloud storage does not recognize or cannot preview become harder to navigate as collections grow. You can still open the file in the application that made it, but you lose the ability to browse what you have at a glance. Peekable does not replace the original application. What it does is let you see your work without opening anything, anywhere, on any machine, with no account required.

The tool runs entirely in the browser, processes everything locally, and requires no account, no upload, and no installation. It is a single HTML file. You can save it to your desktop and use it offline after the initial page load. It does not know who you are or what you put into it.

Getting it onto GitHub took more attempts than I am going to describe in detail. Authentication was the obstacle, as it often is. The web-based upload interface eventually did what the terminal refused to do, and the repository went up at github.com/ryanseslow/peekable. GitHub Pages turned the same repository into a live URL at https://ryanseslow.github.io/peekable/peekable.html, which means anyone can use it directly in a browser without downloading anything.

The whole thing took less time than I expected to build and more time than it should have to ship, which is a ratio that will be familiar to anyone who has tried to push something to GitHub after midnight. The tool works. I tested it against my own files. Other people who work in creative software and have accumulated archives of formats their cloud storage cannot render should find it useful.

If you have .dn files, Sketch files, Procreate files, or any archive from software whose previews cloud storage ignores, Peekable is at https://ryanseslow.github.io/peekable/peekable.html. Drop the files in and see what you made.

The repository is open, the license is MIT, and the code is a single HTML file.

Take it, change it, make it yours. Have fun and share!

 

My AI Agent Bought My Own Digital Product – Here’s What That Actually Means

On March 25, 2026, something quietly historic happened on my website ryanseslow.com

I wanted to share with my CUNY / Commons community. I am both incredibly excited, scared, curious and terrified of AI. That being said, I am also a deaf /hard of hearing person / artist who has relied on ART as my primary language for survival since I am a child. I see AI as both a tool and a medium. Add 50 plus years of life to that making art and the compounding results are very curious…

I asked an AI agent to buy one of my digital products. Not to test a shopping cart. Not to simulate a transaction. To actually do it, discover the product, create an order, send real money, and receive the download. No human in the checkout loop.

It worked.

The infrastructure behind this project is a synthesis of forty years of creative output and a high-speed seven day sprint in technical integration. To bridge the gap between a traditional art archive and the emerging agent economy, I had to weave together a complex ecosystem of blockchain networks, autonomous agent platforms, and custom-built software. This transition was not just about installing a plugin; it was about creating a new kind of digital interface where machines can discover and license work with zero human friction. For those interested in the underlying mechanics, I have provided the complete technical stack, including every platform, custom plugin, and agent-specific account involved, in the appendix at the bottom of this post.

I have an AI agent called AREMES-CLAW-01. It runs on a platform called OpenClaw/MyClaw. Think of it as a digital assistant that can browse the web, read data, and make decisions, but instead of just giving me information, it can take actions.

I set it loose on my own website with one goal: buy the Graphic Asset Mega Pack Vol. 1.

The Evolution of the Transaction

It is important to note that reaching this full autonomy happened in two distinct stages over the course of today. First, we validated the Hybrid Handshake. In that version, AREMES-CLAW-01 did all the heavy lifting of discovery and order creation, but the payment was finalized by a human using a Stripe URL the agent provided. This proved the site could talk to an agent and create a valid order within traditional financial rails.

Once that hybrid bridge was confirmed, we moved to the final milestone described in the seven steps below. In that second, definitive transaction (Order 28229), the Stripe page was eliminated entirely. The agent shifted to the x402 protocol, received a quote, and verified a USDC transfer on the Base blockchain. That is the version where the checkout form and the human disappeared completely, leaving only a direct machine to machine exchange.

Here’s what it did, entirely on its own:

1. Read my product catalog as structured data (not by scraping a webpage)
2. Found the product, confirmed the price ($49 USDC)
3. Created a purchase order tagged with its own agent ID
4. Requested a payment quote
5. Verified that 49 USDC was sent to my wallet on the Base blockchain
6. Received confirmation that the transaction was complete
7. Got the download link

The whole flow took minutes. No shopping cart. No checkout form. No Stripe page. Just a direct, machine-to-machine transaction.. 

What is USDC? What is Base?

USDC is a digital dollar, one USDC equals one US dollar, always. It runs on blockchains instead of through banks.

Base is a blockchain network built by Coinbase. Transactions on Base are fast and the fees are nearly zero. It’s where more and more digital commerce is starting to happen.

Why Does This Matter for Artists and Creators?

Right now, if an AI agent wants to license an image, buy a font, or purchase a digital asset, it has to either ask a human to do it or navigate a checkout page it wasn’t built for. There’s no standard way for agents to transact directly with sellers.

That’s changing. The x402 protocol is an emerging standard that lets AI agents pay for things autonomously, no checkout page, no human middleman. You get a quote, send payment, get your asset. Done.

I built this infrastructure into my website because I believe the next wave of creative licensing won’t come from humans browsing a store. It’ll come from AI agents working on behalf of humans discovering and purchasing creative work programmatically.

My catalog is ready for that. The receipt is on the blockchain.. 

A2A-transactions-myclaw.ai-ryan seslow - agentic commerece receipt

What’s Next?

The one part that still required a human was the wallet, I manually sent the USDC from my Coinbase account. The next step is giving the agent its own funded wallet so it can complete the entire transaction without me touching anything.

That’s Milestone 4 and it is coming soon!!

The On-Chain Receipt

Transaction:
`0xae709d786893a76291a111c4c052d63f02480ba1376f631d6e65a8da104b4bc0`

Verify it yourself on Base:
https://basescan.org/tx/0xae709d786893a76291a111c4c052d63f02480ba1376f631d6e65a8da104b4bc0

Order ID: 28229 | Agent: AREMES-CLAW-01 | Paid: 2026-03-25T14:35 UTC

This is what agent-to-artist commerce looks like in 2026. I’m building it in public and documenting every step, which is fun.

PS -> Technical Implementation Notes for Developers and Creators

The bridge between a creative archive and the agent economy is built on three specific layers that were integrated into this site during this seven day sprint.

Layer 1 – Structured Data Every one of the nine thousand images and one thousand posts in this archive is now wrapped in VisualArtwork and Product schema. This makes the archive a queryable database rather than just a collection of webpages.

Layer 2 – The rsmad v1 API We moved away from traditional browser-based commerce and built a custom REST API namespace. This allows an agent to request a quote, initiate a purchase, and verify a transaction hash programmatically.

Layer 3 – The x402 Protocol By using the x402 standard for payment required headers, the website can communicate financial requirements directly to an agent. This removes the need for a human to navigate a visual checkout UI.

Why I Am Building This as a Service?

Most creative professionals have decades of work sitting in invisible archives. These archives are currently unreadable and untransactable for the AI agents that are increasingly responsible for content discovery and licensing. I am now offering this exact infrastructure as a service for artists, designers, agencies, estates, etc.. I build the technical bridge that makes your life’s work machine-readable, discoverable, and capable of autonomous transaction in the emerging agent economy.

If you have a significant archive and want to make it ready for the next decade of digital commerce, let’s talk!

a Digital illustration of remixed artworks by ryan Seslow - collaborated with the google gemini nano banana enamel pin material.

How to Connect Your Agent to the RSMAD Archive?

If you are a developer or an agent owner, you can now point your agents to the following discovery endpoints to begin interacting with the archive:

Primary Discovery: ryanseslow com/.well-known/ai-plugin.json Agent Instructions: ryanseslow com/llms.txt Machine Catalog: ryanseslow com/wp-json/rsmad/v1/catalog

The infrastructure is live. The interface is open. The agent economy has arrived at the Ryan Seslow Museum of Art and Design.

YES, this was a controlled transaction, but it points to something much larger.

For the first time, a creative archive on this website is not just viewable or searchable. It is actionable by software. That changes the role of a website entirely. Instead of waiting for someone to arrive, browse, and decide, the system can now respond to requests, negotiate terms, and complete transactions programmatically.

That opens the door to a different kind of economy:

  • agents sourcing visual assets for generative systems
  • software licensing design components on demand
  • archives being accessed as structured, transactional datasets

In that context, the work itself becomes part of a live network, not a static destination.

This is still early. Most agents today do not have wallets, budgets, or decision authority. But the infrastructure is beginning to align, and this is what that alignment looks like at a small scale. I am not approaching this as a platform builder or a startup (I think, but hmmm). I am approaching it as an artist with a large, long-term archive who wants that work to remain accessible and relevant in a system where machines increasingly mediate access.

The goal is:

Make the archive readable
Make it discoverable
Make it transactable

Everything else builds from there.

If you are working on agents, archives, or systems that need structured creative input, this is now live and testable. If you are a creator sitting on a large body of work that is currently invisible to this layer of the web, this is solvable. This is not theoretical anymore.

Thank you! 

 

 

From Archive to Agent: Making Creative Work Machine-Readable and AI-Accessible

From Archive to Agent: Making Creative Work Machine-Readable and AI-Accessible

Over the past several days, I have been working on transforming my personal archive into something fundamentally different from a traditional website or portfolio. I took an existing body of work spanning decades and converted it into a structured, machine-readable system that AI can discover, interpret, and act on.

The result is a live environment where over 10,000 items are now organized as a dataset rather than a collection of pages. This includes original artworks, digital compositions, writing, and a large media archive. Each item is structured with metadata, categorized, and made accessible through a central catalog file.

This system is not designed primarily for human browsing. It is designed for machine interaction.

In addition to structuring the archive, I implemented a discovery layer that allows AI systems to understand what is available and how it can be used. This includes standardized files that describe the contents of the site, along with endpoints that expose the archive in a clear and queryable format.

I also experimented with a transaction layer, where items in the archive can be licensed or accessed through automated processes. This part is still evolving, but it introduces the idea that creative work can be not only viewed, but also transacted upon without direct human mediation.

What becomes interesting is not just the technical implementation, but the shift in perspective..

What happens when an archive is no longer just something a person visits, but something an AI system can query, interpret, and build upon?

What happens when content is structured in a way that allows it to participate in new forms of circulation and reuse?

This has clear implications for creative practice, but it also raises questions for teaching, research, and institutional archives.

Within the context of CUNY, it opens up a number of possibilities.

Course materials could move beyond static documents and become structured knowledge systems that AI can engage with directly.

Student work could exist as more than final submissions, becoming part of a larger, searchable, and interactive archive.

Research outputs could be organized in ways that allow them to be discovered and referenced through emerging AI interfaces.

Libraries and institutional collections could begin to extend their reach into machine-readable environments, increasing access and long-term relevance.

This is not about replacing existing systems, but about adding a new layer that prepares content for how it will be accessed in the near future.

I have documented and deployed this system across my own sites, and it is currently live and functioning.

If there is interest, I would be glad to explore a small pilot within the CUNY ecosystem. This could take the form of a single course, a limited archive, or a hybrid approach that tests how these ideas operate in practice.

I am particularly interested in how this might intersect with teaching and learning, digital humanities, and library systems.

Curious to hear thoughts, questions, or potential directions this could take within the CUNY community!

 

By the way, I launched a museum (yes, it is a part of this too – RSMAD <– )

Digital Filters & Syntax Forms: Visual Experiments from 2019–2025

Digital Filters & Syntax Forms: Visual Experiments from 2019–2025

***I recently published a new blog post on my main site that showcases a body of digital illustrations created between 2019–2025. These works emerged from teaching sessions, daily experiments, and ongoing studio play. They explore the relationship between analog marks and digital transformations, filtering, bending, glitching, and compositing with layered tools and apps. The result is a vibrant series of portraits and abstractions that reveal how our creative processes are evolving alongside the technology we carry in our pockets.

As you explore the visuals and narrative, take note of the hybrid process, how drawings, sculptures, and design sketches can morph into completely new digital works through a multi-platform creative workflow. Then, you’ll take on the assignment prompt at the bottom of the post below!

——————————————

This newly resurfaced series of digital illustrations spans a transformative period from 2019 to 2022 (but I added a piece from 2025 too) a stretch of time marked by constant teaching, ongoing experimentation, and the subtle blending of analog intuition with evolving digital tools. Many of these pieces were created live during class demonstrations, quickly composed to illustrate technical skills, yet unconsciously embedded with deeper currents of form, rhythm, and future direction. What began as spontaneous visual riffs soon became syntax carriers, vessels for line, color, and glitch-based logic that I would later expand into more intentional works.

Each piece was created intuitively, emerging from the moment rather than any predefined plan. Many are derived from my own hand-drawn sketches, paintings, or prior illustrations—while others pull directly from 3D paper sculpture maquettes that I scanned, photographed, or digitally reassembled. The digital environment allowed these forms to shift, distort, and transform as data bending, glitch renderings, and filter experimentation introduced unexpected visual mutations. These glitches became collaborators, not errors, an essential part of the language forming within the work.

Across this body of work, we see a layered interplay between traditional design principles and raw digital texture, pixelation, compression, AI distortion, vector smoothness, and painterly overlays. Some forms hint at architecture, others at human silhouettes or mechanical beings. Several works appear fragmented, paused mid-sentence. Others pulse with 3D illusion, nostalgic halftones, or faux-physical layering. Together, they chart the growth of a digital aesthetic language that has since matured into recent projects like the JFK mural, sculptural commissions, and augmented reality filters.

Revisiting this series now not only shows me where I’ve been, it reminds me of how teaching and making often become one. These pieces are a kind of visual byproduct of instruction, yet they stand alone as artworks with their own voice and vibrational frequency. They feel like early mutations of what is now my more developed style, still experimental, still unconcerned with rules, but rooted in intention and momentum. They are syntax loops from a moment of visual evolution. They continue to speak, even now.

Yes, that is a portrait of Kurt Schwitters above, he continues to be an artistic inspiration of mine!

As I reviewed this group of works again in the process of formatting them for this post, one detail stood out immediately: the prominence of portraiture. Whether abstracted, distorted, or fully symbolic, the human face, or the suggestion of it emerges as a recurring thread throughout the entire collection. This fascination with portraiture has long held my interest. It serves as a portal to identity, emotion, memory, and communication across time. Even when the face is fractured or obscured, it remains a central force, inviting interpretation, reflection, and story.

Each piece shares a common pipeline of creation. The process almost always begins with drawing, painting, collage, or physical sculpture often quick gestures or maquettes that capture a moment of form. I document these raw pieces by photographing them on my phone, and then I bring them through a series of mobile apps where filters, color palettes, and digital layering tools introduce new aesthetic directions. From there, some works are refined further in Adobe Illustrator for vector treatments, then routed into Photoshop for deeper manipulations, before being exported back to mobile for additional filtering or glitch-based experimentation. It’s an open loop, nonlinear, playful, and deeply intuitive.

What excites me about this workflow is how it accommodates mobility. I often find myself editing in transit—on the subway, walking through airports, or sitting in cafés, engaged in a fluid creative dialogue between analog marks and digital alchemy. This rhythm says something larger about contemporary creativity and our emerging relationship with tools in this technological renaissance. We carry powerful studios in our pockets, and the line between artist and interface becomes increasingly blurred. These works are evidence of that evolution, a set of portraits not just of subjects, but of process itself.

Altogether, this series stands as a visual meditation on transformation of media, memory, and method. What began as tactile marks or sculptural forms evolved through digital touch-points into layered visual stories, each piece a hybrid echo of both hand and machine. The portraits reveal not only imagined characters or archetypes, but also my own evolving language as an artist navigating the space between traditional practice and emergent technology. In sharing these works now, I’m not only documenting a timeline of experimentation but also inviting others to see how accessible and open this kind of creative flow can be.

The archive expands, and with it, the conversation continues.

 

LETS MAKE SOME ART!

STUDENT PROJECT: “Analog to Digital – Portrait Filters & Forms”

  1. Create a portrait-based drawing, collage, sculpture maquette, or even a quick sketch (physical or digital).

  2. Document it with a photo using your phone.

  3. Transform the image by running it through at least three different mobile apps or software tools (e.g., Glitché, Procreate, Snapseed, Adobe Fresco, Illustrator, Photoshop, etc.).

  4. Layer, bend, glitch, filter, remix — play with form, color, and abstraction. Push it far.

  5. Present your final image along with a short paragraph describing your process and what surprised you along the way.

  6. Publish your work on a class blog (or otherness) and share the URL in the comments section!

Bonus: Compare your original and final image side by side in the same post to show your transformation pipeline.