Glyph · 2026

Glyph

closed source
  • AI
  • Creative
  • Canvas
  • Next.js
Glyph workflow: a reference image, a character fingerprint, a prompt and an output joined on a canvas
glyph.pics
2026

A node-based image workspace for character-consistent generation. Lock a character's identity once, then branch into scenes and styles on one canvas, instead of re-prompting a chat and watching the face drift.

read how it was built ↓
about.txt
role
Founder & Full-Stack
for
Glyph
stack
AI, Creative, Canvas, Next.js
when
2026

How Glyph was built01 / 08

The problem

Ask an image model to change a jacket and the face, framing and scene change too. Text prompts cannot hold identity. The first commit that day was titled as a character consistency system, and it was built around one idea: separate what you keep from what you change.

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The brief

Illustrators, brand teams and game studios need the same character in many scenes and styles. Chat-based image tools redraw the whole picture on every edit, so identity drifts. Glyph treats a character as a thing you build once and then reuse, with the parts you keep kept apart from the parts you change.

facts.txt
Problem
Character identity drifts between generations
Audience
Creators, brand teams and visual studios
Model
Free tier plus paid credit plans
Surfaces
Web canvas, character builder, API in progress
Role
Founder and full-stack
Timeline
April 2026

How I approached it

  1. 01
    Separate keep from change

    The fingerprint holds what must not move. The prompt holds what should. A generation is built from both, with the locks written out explicitly.

  2. 02
    A graph you can read

    Every step is a node with typed connections, so you can see how an output was made and rerun any branch with a different scene or style.

  3. 03
    Keep heavy edits out of the web app

    Background removal, masks, inpainting and upscaling run in a separate Python worker and through fal.ai, so the Next.js app stays responsive.

  4. 04
    Meter everything

    Each model has a credit cost set against its real provider cost, charged through an idempotent ledger so a retry never bills twice.

  5. 05
    Run it as a business from day one

    Billing, roles, sharing and a private admin console were built in the same fortnight as the canvas.

A look at Glyph

the workflow graph
the workflow graph
the problem
the problem
pinned as nodes
pinned as nodes
free sign-up
free sign-up

How it is different

General image tools are chat boxes: you describe a change and the model redraws everything. Other character tools save a reference image, but a reference is not a description of what must stay fixed.

  • A structured fingerprint, not just a reference photo, so identity is something the system can check.
  • A canvas of reusable nodes instead of a linear chat, so a locked character feeds many scenes.
  • Many models under one roof, from image to video and 3D, billed through a single credit balance.
  • Free to start with the full model catalogue, with paid plans that raise credits and billing controls.

What it runs on

What got in the way

What came out of it

Glyph is live with a free tier and paid plans. These numbers describe what was built. There are no usage figures on this page.

What I learned

  1. Describe identity as data. A fingerprint the system can reason about beats a reference image and a hopeful prompt.
  2. Do the money first. Credits and idempotent billing were easier to build before the features that spend them.
  3. Keep the public app clean. Moving admin into a private deployment removed a whole class of risk.
  4. A graph makes AI work legible. You can see, and rerun, how any output came to be.

Glyph: build a character once, generate every version after

A node-based image workspace with identity locks, credit billing and models for images, video and 3D.

Try Glyph

Every account starts free with the full model catalogue and 150 monthly credits.

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Overview