Algorithmic Art Gallery is a browser-based generative art project by Jonathan R. Reed. It is built around deterministic settings, visible controls, and repeatable outputs, so each image can be recreated from the same seed, traversal curve, color path, growth mode, and export choices.
The site is part of Jonathan's broader public project system: practical tools that make hidden behavior easier to inspect. Here, the hidden behavior is visual and mathematical. Instead of turning a prompt into an opaque image, the generator keeps the rule set close to the artwork.
JavaScript powers the live canvas, saved gallery interactions, previews, and export workflow. This fallback context exists for visitors and crawlers that do not run JavaScript. It explains what the interactive tool does without replacing the normal visual interface.
The generator page is the main working surface. With JavaScript enabled, it loads a React canvas interface for plotting, remixing presets, saving studies, and exporting images. Without JavaScript, the live canvas cannot run, so this text records the purpose and structure of the page.
The page starts with curated recipes because a blank control panel is hard to judge. Each recipe is a real configuration, not a decorative screenshot. Loading a preset gives the visitor a specific curve, render mode, palette, density, seed, and growth behavior to inspect before making changes.
What the generator does
The generator creates artwork from space-filling curves and related traversal systems. Hilbert, Morton, Peano, spiral, and random-walk paths can be combined with color maps, symmetry modes, growth behavior, randomness, and preview sizes. The controls are meant to stay inspectable, so a finished image is tied to settings that can be reviewed and changed.
Why repeatability matters
A saved piece is more useful when the process can be repeated. If a seed, curve, palette, and growth mode produce a strong result, the same recipe can be reopened, compared, exported, or adjusted. That makes the project closer to a small visual lab than a one-off image generator.
How the gallery fits
The gallery separates finished studies from the live controls. It gives saved outputs room to be reviewed by composition, density, color balance, and pattern behavior. The goal is to make strong variations easier to find again, not to hide the process behind a polished surface.
Where it connects
This project shares a voice with Jonathan's other tools: direct, evidence-aware, and specific about what the system can and cannot do. It connects to projects about AI, security, outages, resumes, typing practice, and developer workflow through the same habit of making systems easier to inspect.
Preset recipes
Hilbert Bloom, Magma Field, Tide Spiral, Nebula, Drift Walk, and Understory are starting points for repeatable studies. They mix trace and field rendering, different traversal systems, and different color maps, so the generator can show structured line work, dense fills, radial symmetry, random walks, and softer organic growth.
Control surface
The interactive generator exposes settings for seed, seed shape, branching factor, growth rate, randomness, color sample size, dithering, anti-aliasing, curve type, gradient map, growth mode, symmetry mode, color progression, trace density, stroke width, preview size, and final pattern size.
Export workflow
The output path is designed for reuse. A visitor can preview a composition, save a study to the gallery workflow, export a PNG or PDF, and return to the settings when a variation needs another pass. That repeatable path is why the page emphasizes parameters as much as finished visuals.
Visual method
The tool treats generative art as a visible system of choices. Space-filling curves provide the structure, growth modes change how the drawing accumulates, palettes shape the reading order, and symmetry controls decide whether the image feels architectural, organic, centered, or exploratory.