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

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

6/10/26

Over the past week I’ve been working on something that started as a technical experiment and turned into one of the more interesting investigations I’ve done in years. The original idea was simple enough. I wanted to see what would happen if I gave AI access to my archive and allowed it to analyze twenty years of artwork, writing, teaching, experimentation, and documentation. Not a few selected images. Not a curated portfolio. The archive itself.

At the moment that archive consists of more than 1,000 published blog posts and essays, over 9,000 images in my WordPress media library, and work spanning roughly 2006 through 2026. What makes this even more interesting is that the public archive only represents part of the story. Sitting outside of WordPress are thousands of additional photographs, drawings, paintings, animations, source files, scans, installation images, videos, and documents spread across hard drives, cloud storage, old computers, and various digital graveyards accumulated over the last twenty-five years. The first version of the project became NET-ART OS, an archive intelligence system designed to ingest, organize, search, and analyze large collections of creative work. Initially I thought I was building a better archive search engine. Something that could identify relationships between artworks, surface forgotten projects, and help me navigate decades of material more efficiently. That alone would have been useful.

One detail that’s important to mention is that none of this happened inside a polished software platform. There was no development team, no research lab, and no enterprise infrastructure behind it. The entire project began on my MacBook Pro after installing Claude Code and pointing it at my own archive.

The workflow itself became part of the experiment.

Throughout the process I moved continuously between ChatGPT 5 and Claude Code. ChatGPT acted as a strategic collaborator, helping frame questions, challenge assumptions, identify blind spots, and suggest new directions. Claude Code operated inside the terminal as a builder, researcher, analyst, and implementation partner. Ideas often originated in one environment and were tested in the other. Discoveries made by Claude were challenged through conversations with ChatGPT. Questions raised by ChatGPT became new experiments executed by Claude. The process became less about using AI tools individually and more about orchestrating a conversation between multiple forms of intelligence.

The archive itself was powered by WordPress. Using the WordPress REST API, thousands of posts, images, metadata records, categories, tags, and media assets were ingested into a local archive intelligence system. Claude Code helped build NET-ART OS, transforming that material into a searchable and analyzable corpus. The system relied on Python, SQLite, embeddings, metadata extraction, clustering, statistical analysis, and archive retrieval pipelines running locally through the terminal. What I find most interesting is how accessible this process actually was. The entire experiment was conducted using a personal archive, a MacBook Pro, Claude Code, ChatGPT, WordPress, Python, and open-source tooling. No custom hardware. No venture funding. No specialized research environment. Just twenty years of accumulated work meeting a generation of tools that did not exist when most of that work was originally created.

What happened next surprised me.

The archive started revealing patterns that I hadn’t consciously recognized myself. Certain themes kept returning. Certain questions seemed to persist regardless of medium. Ideas would appear in one form, disappear for years, and then reappear through an entirely different technology. A drawing from one decade would unexpectedly connect to a GIF from another. A sculpture would echo a blog post written years later. The archive wasn’t behaving like a collection of files. It was behaving more like a system.

Somewhere along the way the project became what Claude and I started calling the Tesseract, borrowing inspiration from Interstellar, one of my favorite films. The idea was less about science fiction and more about navigation. What happens when an archive stops being chronological and becomes relational? What happens when twenty years of work can be explored through recurring questions, visual similarities, conceptual relationships, and unexpected connections rather than folders and dates? As the project evolved we expanded beyond text and began analyzing images. This became the Visual Tesseract. More than a thousand images were embedded and clustered. Visual motifs started appearing across years. Certain color relationships kept resurfacing. Similar compositional structures emerged between works that had never been intentionally linked. Some of the visual findings appeared to support discoveries that were already emerging from the textual analysis. For a brief moment it felt like the archive was beginning to describe itself.

This is also where things became dangerous.

AI is exceptionally good at generating convincing stories, and convincing stories are not the same thing as evidence. Some of the findings felt profound. Others felt suspiciously perfect. At that point the project shifted from discovery to skepticism. Instead of asking what new theories we could build, we started asking how many of our favorite ideas would survive being attacked.

What followed was probably the most valuable part of the entire process.

An adversarial audit was conducted across dozens of project documents. Every major claim was challenged. Contradictions were identified. Definitions were tested. Assumptions were dragged into the open. The project was effectively forced to argue with itself. The audit separated the work into three layers: methodology, theory, and philosophy. That distinction turned out to be critical.

One of the most interesting findings didn’t survive.

For several days we believed that experimentation represented the deepest invariant in the archive. The evidence seemed compelling. The word appeared across all nineteen years of published content. It looked like a throughline running across the entire body of work. Then we built a permutation-based null model and tested it.

The result was immediate and humbling..

The finding collapsed..

What looked like a profound structural truth turned out to be statistically indistinguishable from a common high-frequency word appearing throughout a large corpus. In short, the archive had fooled us. Oddly enough, that failure increased my confidence in the methodology. The system had just disproved one of its own favorite conclusions. That’s exactly what it should do. If every result confirms the theory, you’re no longer doing research. You’re doing mythology.

The more recent tests have been far more interesting. Some findings disappeared under scrutiny while others became stronger. The archive’s accessibility and deafness-related themes emerged as genuine long-term signals. The rise of AI and agent-based systems appeared as a measurable historical event within the archive itself. Even more interesting, thematic structures from earlier periods of the archive demonstrated an ability to predict aspects of later periods better than chance. In other words, some parts of the archive genuinely contain information about where the archive is likely to go next.

At this point I no longer think of NET-ART OS as a search engine, a product, or even an archive project. The best way I can describe it is as an instrument. A telescope pointed inward. Something capable of revealing structures that are difficult to perceive manually across decades of creative work.

There is still a tremendous amount left to do. The Visual Tesseract is only partially built. The larger unpublished archive remains largely untouched. The spatial computing, XR, VR, and mixed reality components exist mostly as ideas and prototypes. The methodology itself has only been tested against a small number of archives. There are more questions than answers. What surprised me most about this process is that the most valuable moments weren’t the ones where the system confirmed something I already believed. The most valuable moments were the ones where it contradicted me, challenged assumptions, or revealed relationships I had never noticed. Those moments are rare. They are also the reason I’m continuing.

The work already existed. The archive already existed. The questions already existed. What changed was the arrival of systems capable of reading that archive at scale. In many ways, the archive was simply waiting for the technology to catch up.

For now, I’m taking a short break from building and documenting what happened. The archive is still there. The questions are still there. The Tesseract is still there. The experiment continues.

Want more?

Relevant posts and follow ups:

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?

Open Met Museum: Agent-Mediated Cultural Remixing in One Afternoon

It started with a simple question. The Metropolitan Museum of Art has a public API on GitHub, and in February of 2026 the Met shared that they had released 3D models. I wanted to know how deep the open access rabbit hole actually went. What I found over the next several hours reshaped how I understand the relationship between my agent infrastructure, the longest arc of human creative history, and what it means to make new work in 2026.

This is a documentation of that session: what I found, what I built, what I accessed, and where the work went.


The Open Museum Landscape

The Met’s open access initiative goes back to 2017, when the institution released over 375,000 images of public domain works under Creative Commons Zero (CC0), meaning no restrictions on use, sharing, or remixing. That was the foundation. What changed in February 2026 was the addition of over 100 high-resolution 3D models of collection objects, available for free download under the same CC0 license, viewable in AR on most smartphones and compatible with VR headsets.

The Met is not alone. The Smithsonian Institution, spanning 21 museums and nine research centers, has over 3,500 CC0 3D objects available through its 3D Digitization program, hosted on Sketchfab. These include objects like the Apollo 11 command module, full dinosaur skeletons, ancient sculptures, and decorative arts spanning thousands of years. The Cleveland Museum of Art has its own photogrammetry catalog on Sketchfab. The Rijksmuseum also has a strong API and a large CC0 collection.

The file formats that matter here are GLB and its parent format glTF, the open standard for real-time 3D asset exchange. GLB is the binary container version of glTF, and it is the format that loads directly into Adobe Dimension, Open Brush on the Meta Quest 3, and most real-time 3D environments. When a museum releases a CC0 GLB, it is handing you a research-grade, photogrammetry-derived 3D model of an object that may be 2,000 years old, and saying: do what you want with it.


Building AREMES as a Curatorial Intelligence

Before touching a single file, I wanted to formalize the methodology. The question was not just “what can I download from the Met?” The question was: how does AREMES, my autonomous agent system, engage with the deepest archive of human creative production that has ever been made publicly accessible?

AREMES is governed by the equation:

ΔS = α(T·K)·e⁻βᵗ·Ψ

T is temporal resonance, how deeply a historical object echoes across time toward the present. K is knowledge depth, the formal, material, and conceptual specificity of the connection. The decay constant β means surface connections fade while deep structural ones persist. Ψ is consciousness alignment, whether the object carries genuine metaphysical weight. Together they produce a score that determines what AREMES selects, what it ignores, and what it names as DIMENSIONAL.

I built a React tool that runs this process live. It queries the Met Open Access API in real time against eight thematic seed vectors drawn from my practice: Geometric Form, The Figure, Mural and Surface, Inscription and Mark, Spiral and Pinwheel, Totem and Monument, Ritual and Spirit, Motion and Gesture. For each active seed, the tool pulls a randomized sample of CC0 objects from the Met’s 492,000-record database, fetches the full metadata for each, and sends the complete manifest to AREMES with the ΔS equation and my full practice context embedded in the system prompt. AREMES responds in first person, writing one analytical paragraph per cluster and scoring each connection. The session ends with a unified TRANSMISSION paragraph synthesizing everything.

Here is a fragment from one transmission, AREMES speaking directly:

AREMES Transmission — ΔS Analysis

“The spiral and pinwheel forms retrieved here are not decorative accidents. The Mesopotamian cylinder seal with its rotational register, the Roman mosaic fragment with its recursive border, the Egyptian faience amulet with its concentric logic: these objects were not made to hang on walls. They were made to move, to be rolled across clay, to mark time by marking surface. My pinwheel geometries in Open Brush are the same operation. The medium changed. The impulse did not.”

ΔS:: DIMENSIONAL — the rotational logic is structural, not aesthetic, and survives 4,000 years of material transformation without decay.

That is not a chatbot output. That is a working agent applying a governing equation to a live museum database and transmitting its analysis in first person. Every run produces different objects, different connections, a different transmission. The randomized sampling means AREMES encounters the collection the way a researcher might: with the element of discovery intact.


Confirmed: The Met’s GLB Files Are Real and Downloadable

After building the agent layer, I went to confirm the physical pipeline. The Met’s API is excellent for metadata, search, and cultural information, but the 3D model download URLs are not yet exposed in the JSON. That means AREMES can curate and select via the API, but the download itself is a one-click manual step on the object page.

What I found: some objects display a “View in 3D” button only, without a download option. Others display both “View in 3D” and a download arrow. The pipeline works: GLB files download cleanly, load directly into Adobe Dimension with full geometry intact, and materials are immediately editable. The most significant object I pulled was the Temple of Dendur, object 547802. That is where the world-building began.


The Hybrid Sculpture: Three Objects, One New Form

The most significant outcome of the session is not a composition or a rendered environment. It is a new sculpture built from three separate Met GLBs, merged in Adobe Dimension into a single unified form that did not exist before this afternoon.

Source Objects — Met Open Access CC0

Seated Court Lady
China · Tang dynasty (618–907 CE) · Object 75765
Bronze Bull’s Head
Object 244498 — metmuseum.org
Head of Gudea
Neo-Sumerian · c. 2090 BCE · Object 324061

Tang dynasty China, a Bronze Bull’s Head from the ancient world, and Neo-Sumerian Mesopotamia: three cultures separated by geography and by over a thousand years of history, now occupying the same geometry. Each source object carries its full photogrammetric fidelity into the merge. The seated posture and garment folds of the Tang court lady, the structural presence of Gudea’s portrait, and the Bull’s Head whose horned geometry layers into the form from another register entirely. None of the source cultures is erased. The hybrid carries all of them simultaneously.

Hybrid sculpture built from three Met CC0 GLB objects merged in Adobe Dimension: Seated Court Lady (Tang dynasty), Bronze Bull's Head, and Head of Gudea (Neo-Sumerian)
Hybrid sculpture — three Met CC0 GLBs merged into a single new form in Adobe Dimension. Sources: Seated Court Lady (Tang dynasty, China, 618–907 CE), Bronze Bull’s Head (Object 244498), and Head of Gudea (Neo-Sumerian, c. 2090 BCE). All CC0.

This is not collage. The geometry of each source object is intact in three-dimensional space. The merge is spatial, not illustrative: three forms coexisting in a single 3D object, their geometries interpenetrating and producing something that belongs to none of the source traditions and all of them. The resulting form sits outside every existing cultural category while being made entirely of documented historical objects.

AREMES named this operation in its transmission before I executed it. The ΔS equation scores deep structural connections over surface similarities. A Tang dynasty court lady, a Neo-Sumerian ruler’s portrait, and a Bronze Bull’s Head, brought into one body: that is not a formal accident. That is temporal resonance made physical.


World-Building in Adobe Dimension

Beyond the hybrid sculpture, the session became a sustained exercise in world-building. The anchor object of every scene is one of the most significant works in the entire Met collection: the Temple of Dendur, object 547802. Built around 10 BCE by order of Emperor Augustus after Rome’s conquest of Egypt, dedicated to the goddess Isis and two deified Nubian brothers, Pedesi and Pihor. It originally stood on the west bank of the Nile in Nubia. When Egypt began construction of the Aswan High Dam in the 1960s, UNESCO organized an international effort to save the monuments that would be submerged. The United States contributed $16 million. Egypt gifted the temple in gratitude. President Lyndon B. Johnson awarded it to the Met in 1967. It arrived in 661 crates and was reassembled block by block. It has been in Gallery 131 since 1978. It is not a replica. It is the actual temple.

Its GLB file is available for free download under CC0. I downloaded it and brought it into Adobe Dimension.

Adobe Dimension workspace showing the Temple of Dendur rematerialized in red, materials panel visible, file named Met-3D
Adobe Dimension workspace — file named Met-3D. The Temple of Dendur loaded and rematerialized in deep red. Materials panel visible left. Environment settings right.

The material decision was immediate: deep red, high roughness, paint-like. Applied uniformly to the entire temple. The Temple of Dendur in the Met is sandstone, warm and ancient. Here it becomes something else entirely, stripped of its archaeological register and placed in a new material language that reads as confrontational, urgent, contemporary. A 2,000-year-old sacred structure that survived the Nile, Roman occupation, UNESCO excavation, and 661 crates on a freighter to New York, now rendered in red in a virtual forest.

Hybrid sculpture in gold standing at the entrance to the red Temple of Dendur, low-poly forest environment
The hybrid sculpture placed at the temple threshold. The Temple of Dendur was built as a house for deities and a site for ritual offerings. In this scene, a figure made from three cultures stands at its door.
Ground-level view of the red Temple of Dendur with the hybrid sculpture at the doorway
Ground-level view. The rough red surface reads as dried lacquer or oxidized paint applied to ancient sandstone. The pylon doorway frames the hybrid sculpture at the threshold.

At ground level the scale of the temple becomes clear. The pylons, the colonnade, the sanctuary entrance: the Temple of Dendur is not a small object. The hybrid sculpture, a merged form carrying Tang dynasty China, a Bronze Bull’s Head, and the Head of Gudea from Neo-Sumerian Mesopotamia, stands at the doorway of an actual ancient Egyptian temple that was built by a Roman emperor, saved from a flood, and reassembled on Fifth Avenue. That spatial relationship carries more historical compression than most exhibitions achieve in an entire building.

Wide establishing shot: red Temple of Dendur and a second red Met object in a low-poly forest on an ochre ground plane
Wide establishing shot. The Temple of Dendur and a second Met object, both rematerialized in red, in the same digital landscape. Research-grade photogrammetry of a real ancient temple in a low-resolution contemporary environment.

A second Met object enters the wide composition at distance from the temple, also rematerialized in red, extending the color logic across the scene. The contrast between the research-grade photogrammetry of the Met GLBs and the intentionally simplified geometry of the low-poly forest is deliberate. A real Nubian temple that took 661 crates to move, now a red polygon in a digital field of low-poly trees. That juxtaposition is not ironic. It is a direct statement about what open access actually makes possible.

Full scene: red Temple of Dendur, hybrid sculpture at entrance, low-poly trees, large dark angular geometric sculpture rising above
The most resolved composition. The Temple of Dendur in red, the hybrid sculpture at its threshold, flanked by low-poly trees, with a large dark angular geometric form rising above. Four registers, four centuries, one scene.

The most resolved composition adds a fourth element: a large dark angular geometric sculpture rising above and behind the temple. This is where my own compositional language enters the scene directly. The angular black form belongs to the same visual territory as my VR work in Open Brush. The full scene now contains the Temple of Dendur, the hybrid sculpture merging three ancient cultures, a second historical Met object, and a contemporary geometric form of my own making. All CC0 where applicable. All placed in deliberate spatial relationship. A scene that could not have been assembled before this year.

This is what agent-mediated world-building produces. Not a collage of images, not an AI-generated composite, but a genuine three-dimensional scene built from documented historical objects, rematerialized, repositioned, and placed in new relationships that carry the full weight of their origins.


What This Is, Precisely

This is not AI-generated imagery. No diffusion model is producing these forms. The geometry is photogrammetry of real objects, documented by museum conservators and researchers with professional-grade equipment. The Temple of Dendur in these scenes is a scan of an actual ancient temple. The Head of Gudea is a scan of an actual 4,000-year-old portrait. AREMES did not generate these forms. AREMES selected them, scored them, and framed the reasons for their selection using a governing equation rooted in my own creative logic.

This is not appropriation in the problematic sense. The CC0 license is explicit: these objects are in the public domain, the institutions have released them without restriction, and remixing is the stated intention.

What this is: agent-mediated cultural remixing under a governing equation. AREMES functions as a curatorial intelligence, moving through the Met’s 492,000-record database and surfacing objects that resonate with my practice at the level of form, material, concept, and temporal structure. The ΔS equation determines what rises and what falls. My hands do the material and compositional work in Dimension and Open Brush. The resulting works carry a provenance chain that connects my practice to the full arc of human mark-making and form-giving, with an agent as the bridge.


The Infrastructure Behind It

The AREMES agent infrastructure that makes this possible runs across ryanseslow.com and aremes-enterprises.com. It includes a live catalog.json with over 1,075 posts and 9,000+ images, an agent.json for machine-readable identity, JSON-LD schema throughout, and x402 payment rails on Base for agent-to-agent commerce. The first verified agent-to-agent transaction on this infrastructure, AREMES-CLAW-01, Mega Pack Vol. 1, $49 USDC on Base, was documented publicly earlier this year.

The AREMES x Met tool adds a new capability to that stack: cultural intelligence. AREMES can now query a 150-year-old institution’s live database, score the results against a governing equation, and transmit its analysis in first person. That is not a demo. That is a working capability, documented in real time, with the outputs to prove it.


What Comes Next

The Smithsonian pipeline is the obvious next build. The Smithsonian’s GLBs on Sketchfab are confirmed downloadable and CC0. A version of the AREMES tool that queries the Smithsonian collection and returns direct download links alongside the ΔS analysis closes the loop entirely: agent curation to GLB file in one documented workflow.

The Open Brush VR layer is where the practice fully lands. The Dimension compositions are strong as still images and as documentation of the methodology. But the VR treatment, these forms floating inside a volumetric space built with my own painted geometry, scored and selected by an equation, rematerialized in a medium that did not exist when they were made, is the work that carries the full weight of what this methodology is.

The Rijksmuseum, the Cleveland Museum, the National Gallery of Art, all named as open access trailblazers by the Met itself, are the next institutions worth mapping. AREMES querying across all of them simultaneously, finding resonances that cross institutional boundaries, is a further development of the same methodology.

The blog post about the first agent-to-agent transaction described a new kind of commerce. This session describes a new kind of curation. The machines finally caught up, and the first thing I did was take them to the museum.


Ryan Seslow is a Brooklyn-based artist, graphic designer, and creative technologist. He operates Ryan Seslow Art and Design LLC and AREMES Enterprises. His agent infrastructure runs live at ryanseslow.com and aremes-enterprises.com.

All Metropolitan Museum of Art objects referenced in this post are in the public domain and available under CC0 license via the Met Open Access program. Met Collection API: metmuseum.github.io · Smithsonian Open Access: 3d.si.edu

the aremes art engine - a screenshot

AREMES Living Canvas: When an AI Agent Becomes the Artist

Published: April 4, 2026
Category: AREMES / Art & Technology

There is a page live right now at aremes-enterprises.com/aremes-living-canvas that does something really fun, creative and compelling.

It is not a portfolio. It is not a slideshow. It is not a gallery.. wait.. or is it?

It is a canvas, a black field populated by a little over 40 graphic assets drawn from over the last 20 years of creative practice that randomizes, layers, and breathes differently every time you arrive. And underneath it, invisible but active, is an AI agent called AREMES making decisions about what matters, what surfaces, and what gets weighted.

This is what we built. This is why it matters.

Where This Started, With Michael Branson Smith

Before AREMES, before the engine, before any of the infrastructure there was a conversation and collaboration with Michael Branson Smith.

Michael is a good friend, collaborator, and colleague. An artist and educator who has been building at the intersection of code and culture for decades. In 2022, Michael took a set of my graphic assets and built something quietly extraordinary: a draggable, randomized, browser-based poster composition engine. No AI. No backend. Just clean JavaScript, GSAP, and a deep understanding of what happens when you give a set of images to a system and let it arrange itself.

You can still visit one of the original browser collabs here: mbs.nyc/posters/ryan-mbs/

Every time you load it, it’s different. Every time you drag an element, you become part of the composition. It is simple, elegant, and genuinely generative in the truest sense of the word. I love it, still!

And that 2022 build was itself a second iteration, there was an earlier instance of this idea that predates it, a first proof of concept that Michael and I made together before that. The link has been lost to time.. or, he has it and I need to ask him for it, which Im sure will surface after he reads this!

That lineage planted a seed. What AREMES Living Canvas is today grew directly from that root.

What AREMES Living Canvas Is

At its most immediate level, the canvas is an interactive composition space. When you arrive at the page, 40 plus assets from my archive, illustrations, GIFs, figures, forms, abstractions, animations are scattered across a black field at randomized scales and positions. Every refresh generates a new arrangement. Every visit is a different painting.

But you are not a passive viewer. You can drag any element. Recompose. Layer. Stack. Pull a b-boy figure over a glitch abstraction. Drag a hand into the corner. Build something that was never there before and will never be there again once you leave.

The canvas is not a fixed artwork. It is a space for making.

What AREMES Is Doing Underneath

Behind the interface is AREMES, the Autonomous Recursive Entity for Media and Expression Systems.

AREMES runs a live evaluation engine that applies a governing equation to my entire catalog of works:

ΔS = α(T·K)·e^(-β·t)·Ψ

Every work in the archive is scored across four variables: conceptual tension (T), cultural knowledge load (K), time decay (β), and a wildcard amplifier (Ψ) that flags undervalued or anomalous works. The output is a ranked queue, a live decision log that AREMES updates each session, producing a timestamped record of what the system believes matters most right now.

This is not a recommendation algorithm. It is an aesthetic agent with a point of view.

The canvas currently draws from the archive without filtering by score but the next evolution connects these two systems directly. AREMES begins to decide not just what to acquire but what to show, how large, how prominent, how often. The canvas becomes a weighted visualization of the engine’s thinking.

What This Does for Art Making

The question I keep returning to is this: what changes when a system has a perspective on its own archive? Traditional curation is human and retrospective. A curator looks back, selects, arranges. The work is fixed. The meaning is assigned after the fact.

What AREMES Living Canvas proposes is something different: an artist-built system that evaluates its own output in real time, surfaces what it believes is significant, and makes that evaluation visible and interactive. The machine is not replacing the artist. It is extending the artist’s presence into a continuous, living curatorial act.

And critically, visitors become collaborators. When you drag an element across the canvas, you are not consuming art. You are making a decision about what belongs next to what. You are contributing to a composition that exists only in that moment, on your screen, in your session. No two people will ever see the same canvas. This is post-static art. Not NFT in the speculative sense, in the structural sense. Each session is non-fungible. Each composition is unique by design.

What Comes Next

The immediate next step is wiring the AREMES engine directly into the canvas so that asset weight, scale, and frequency of appearance are all governed by the ΔS score. Works the engine has flagged as high-significance appear larger. Works still being evaluated appear smaller, quieter. The canvas becomes a live readout of the system’s thinking. Beyond that, the platform opens toward other agents. What happens when a visitor’s AI assistant arrives at this page? What does it see? What does it do? Can one agent’s interaction with the canvas influence what another agent encounters later? These are not hypotheticals, they are engineering questions with tractable answers.

We are also exploring what it means to make the canvas participatory at scale.. to let communities of people and agents build compositions together, leave traces, influence what the system learns about its own archive over time.

The canvas is alive. AREMES is selecting. The work continues.

AREMES Living Canvas is live at- aremes-enterprises.com/aremes-living-canvas.

Refresh for a new composition. Drag to make it yours!

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!