refern. VS ViewForge

refern.'s answer

I'm an artist, and too often I was frustrated: I couldn't find the right pictures, I struggled to organize and keep track of my reference images, and I was juggling too many apps when all I wanted to do was draw. I wanted one tool that could do it all, with every component well integrated. So I made refern.

ViewForge's answer:

ViewForge came out of agency work. Normal View was building for a parts retailer running roughly 12,000 SKUs who needed fitment search, and every app we evaluated stored the merchant's compatibility data in the vendor's own database.

That is a strange trade when you look at it directly. Fitment data is genuinely expensive to produce — it is weeks of work — and the merchant would not own the result. It would belong to whichever app happened to be installed that year.

Shopify metaobjects made a different answer possible: write fitment as native structured data inside the merchant's own store. The theme reads it, the Storefront API queries it, Admin GraphQL exports it, and it is still there after an uninstall. That decision is what the rest of the app is built around.

Everything else came from real catalogs rather than a roadmap. Eight verticals exist because a tractor catalog is not Year/Make/Model. Smart Parse exists because that retailer had already written fitment into 12,000 product titles, and nobody was ever going to retype them.

refern.'s answer

The flow of collecting references for art usually goes like this:

find references and inspiration online -> save and organize those images -> use them on their own or in a moodboard -> reuse them for the next project or share them with others

Normally that means a different app for each step. refern does all of it in one place, with every part built to work together.

ViewForge's answer:

Three things. (1) Data ownership: ViewForge writes fitment as Shopify metaobjects native to your store — most competitors store fitment in their own database. (2) 8 verticals out of the box: auto, motorcycle, tractor, marine, power equipment, bicycle, printer, electronics — most competitors are automotive-only. (3) Smart Parse: extract fitment automatically from your existing product titles and descriptions instead of re-typing everything.

refern.'s answer

Most tools solve only one part of the reference workflow, so artists end up juggling several. refern combines them in one app: Eagle-style organization and search, a PureRef-style infinite canvas, and an Obsidian-style graph of how your references connect. It never copies your files, so it doesn't double your disk usage. It runs on Windows, macOS, and Linux, works fully offline, and is a $30 one-time purchase with no subscription. Built in Rust, it stays fast even on libraries with hundreds of thousands of images.

ViewForge's answer:

Data ownership. Fitment lives in your Shopify metaobjects, so uninstalling does not take your compatibility data with it. Convermax, EasySearch and PartFinder all keep it in their own databases, and getting it back depends on their export tooling on the day you cancel.

Cost at the low end. The search widget, the compatibility table on the product page and the saved-vehicle garage are all on the free tier, up to 50 products, with no expiry. EasySearch puts the table and the garage behind its $75/month Premium plan. Convermax starts at $250/month.

Automotive and non-automotive coverage. Eight built-in templates, and fully custom templates from $19/month, for catalogs that do not decompose into Year/Make/Model at all.

refern.'s answer

Individual artists and creative professionals: illustrators, concept artists, digital painters, designers, and photographers. They collect and organize large libraries of visual references, build moodboards, and study from reference. refern is built for solo creators rather than teams, spanning hobbyists and students through working professionals.

ViewForge's answer:

Shopify merchants whose customers need to know whether a part fits before they will buy it — and who do not have an engineer on staff to build that themselves.

Concretely: auto and truck parts retailers, powersports and motorcycle dealers, tractor and agricultural parts sellers, marine and outboard suppliers, small-engine and power equipment stores, bicycle and e-bike component shops, printer supply merchants, and electronics accessory sellers.

Catalog sizes run from a few dozen products on the free tier up into the tens of thousands; it is running in production on a catalog of roughly 40,000 SKUs. The common thread is not the industry — it is that "does this fit my thing" is the question deciding the sale.

refern.'s answer

refern is a Tauri v2 desktop app with a Rust backend and a React 19 (with React Compiler) plus TypeScript frontend. Data is stored locally in SQLite (rusqlite) with FTS5 full-text search. The interface uses Tailwind CSS, Zustand, and TanStack Query, with motion/react for animation. The infinite canvas and graph view are built on react-three-fiber and three.js. Thumbnailing, perceptual hashing, and color and visual similarity run in Rust with rayon and SIMD.

ViewForge's answer:

• TypeScript
• Shopify metaobjects, as the fitment data store
• Shopify theme app extensions for the storefront components: fitment search, saved-vehicle garage, compatibility table, product-page fit notice
• Shopify Storefront API, for querying fitment from the theme
• Shopify Admin GraphQL API, for writing and exporting fitment records
• NHTSA vehicle database, for VIN decoding
• ACES and PIES XML parsing, for automotive catalog import