You'll learn a simple trial we run before letting any new software near a finished piece, so a tool earns its place rather than just arriving with a trend. By the end you'll have a short set of questions to ask of any program, from a sketching app to a generative model, before it touches your working files.
Hand & Machine8 min read
Hand-Mixed Pigment vs AI-Generated Color Palettes: What Changes
Materials & Meaning9 min read
Why Texture Still Matters When Images Are Infinite
Looking Closely8 min read
A Checklist for Spotting AI Assistance in Finished Artwork
Studio Practice8 min read
Building a Reference Library: Sketchbooks Versus Prompt Logs
You'll see what tends to go missing from a finished piece when the preparatory drawing is generated rather than drawn by hand, from awkward anatomy decisions to the small corrections that teach you something. We tested this on three subjects to find out what the shortcut actually costs.
You'll learn exactly where an AI upscaler can help a print workflow, such as preparing a digital file for a large plate, and where it starts to work against the medium. We tested it across linocut, etching and screen print to see which stage benefits most.
You'll get a workable way to describe which tools touched your piece and at what stage, written in plain language rather than vague disclaimers. We show you drafts from our own statements so you can see what specific crediting actually looks like.
You'll learn a short sequence for examining any painted surface up close, from raking light checks to looking at edges where colors meet. We use this with students before they ever discuss style or meaning.
You'll follow the same small figure made three ways, hand-built in clay, modeled in 3D software, and generated through an AI image-to-mesh tool, to see what each method asks of you. We compare the time, the control and the surprises each route produced.
