Vellum — a private, on‑device screenshot assistant powered by Gemma 4

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

What I Built

Vellum is a tray‑resident macOS app that turns any screenshot into a conversation. Hit ⌘⇧1 to drag a region, or ⌘⇧2 to grab the whole screen — the capture pops open in a chat window, the image is described and OCR'd in the background, and you can immediately ask follow‑up questions about what's on screen.

The interesting bit: Vellum runs Gemma 4 locally on Apple Silicon by default. No screenshot ever leaves the machine. There's no account, no API key, no cloud round‑trip — just a global hotkey and a vision‑language model living in your menu bar.

How I actually use it: over a few days I capture screenshots of things I read on the internet — articles, threads, diagrams, half‑finished thoughts — and let them pile up. Then one evening I sit down and review the whole stack. Because every capture is OCR'd by Gemma 4, the pile is fully searchable, and the review session turns into something more useful than a screenshot graveyard:

Capture now, think later. Grab anything interesting with ⌘⇧1 and keep reading — no context switch, no "where do I save this".
OCR everything automatically. Gemma 4 reads the text out of each image on capture, so I can search across days of captures by what was in them, not just when I took them.
Review in one sitting. An evening pass over the gallery surfaces patterns I'd never have spotted scrolling live.
New blog‑post ideas. Asking Gemma about a cluster of related captures regularly hands me an angle for something I want to write.
Organize with tags. Group captures into themes so the next review starts already sorted.

A few quality‑of‑life touches that make the review session flow:

One‑click "Copy for Slack / JIRA". From the capture's action bar, Vellum puts the image and a ready‑to‑paste blurb on the clipboard — Gemma 4's description plus the tags, formatted for each tool (Slack mrkdwn or Jira markup). Paste straight into a thread or ticket.
"Copy with shadow" for a polished share. One click composites the raw screenshot onto a transparent canvas with padding, rounded corners and a soft drop shadow, then drops it on the clipboard — the kind of framed shot you'd otherwise reach for a separate tool to make.
Plain "Copy image" for everything else.

Each action also has a ⌘1…9 shortcut, so the whole review‑and‑share loop stays on the keyboard.

Source + install script (one curl line for Apple Silicon): https://github.com/arunkant/vellum

Demo

Screenshots:

Install on macOS (Apple Silicon):

curl -fsSL https://www.arunkant.com/vellum/install.sh | bash

How I Used Gemma 4

Vellum bundles llama.cpp's llama-server as an extraResource and, on first use of the local provider, downloads:

gemma-4-E4B-it-Q4_K_M.gguf — Google's vision‑capable Gemma 4 E4B‑it, quantized to Q4_K_M (~5 GB).
mmproj-BF16.gguf — the multimodal projector that lets Gemma 4 actually look at pixels.

Both come from Unsloth's GGUF mirror of the official google/gemma-4-E4B-it. The server is launched lazily the first time the user takes a screenshot, then kept warm for the rest of the session.

The request flow is a regular OpenAI‑compatible vision call to localhost — Gemma 4 happily accepts an image part next to a text part:

// src/ai/local.ts
await net.fetch(`${serverEndpoint()}/v1/chat/completions`, {
  method: 'POST',
  body: JSON.stringify({
    model: 'gemma-4-e4b',
    messages: [{
      role: 'user',
      content: [
        { type: 'text', text: req.prompt },
        { type: 'image_url', image_url: { url: imageToDataUrl(req.imagePath) } },
      ],
    }],
    max_tokens: req.maxTokens ?? 2000,
    temperature: 0.2,
  }),
});

The launcher arguments are deliberately conservative so it fits comfortably alongside a browser and an IDE on a 16 GB MacBook:

// src/ai/llama-server.ts
const args = [
  '-m', modelPath(),
  '--mmproj', mmprojPath(),
  '--host', '127.0.0.1',
  '--port', String(port),
  '-c', '8192',
  '--no-webui',
];

Two prompts drive everything:

Auto‑analysis — a short instruction asking Gemma 4 to describe the app/page and pull out the most important visible text, runs the moment a capture lands.
Chat — every user message is forwarded with the original image still attached, so Gemma can ground each answer in the pixels rather than its own prior summary.

The same VisionProvider interface is also implemented by an OpenRouter‑backed provider, so users who want a beefier hosted model can swap in one without touching the rest of the app. Gemma 4 is the default, and it's the only path that works fully offline.

Multimodal Insights

A few things I learned shipping Gemma 4 to end‑users:

The 4B/E4B vision model is genuinely shippable. It comfortably reads UI text, error dialogs, and code editors, and an 8k context (-c 8192) is plenty for a single screen capture.
The multimodal projector is the easy thing to forget. Without --mmproj, the model will silently treat the image like noise. Vellum downloads it alongside the weights and refuses to start the server if either file is missing.
Q4_K_M is the right tradeoff for laptops. It keeps the whole thing under ~5 GB on disk, leaves headroom for a browser, and the quality cost on screenshot tasks is invisible to me in practice.
I run Gemma at a low temperature: 0.2. Screenshot Q&A wants literal, deterministic answers rather than creative ones, so a low temperature is the safer default.

Why Gemma 4 for this app

I built Vellum around a habit: capturing things I read over days, then reviewing the pile in one sitting. That only works if every capture is OCR'd and searchable the moment I take it — and I'm not about to stream days of half‑formed reading habits and private dashboards to a third‑party endpoint just to make that happen. An open‑weights vision model I can bundle and ship inside an Electron app is the only way that problem actually gets solved: it runs the moment I hit ⌘⇧1, with no account, no API key, and no network hop. Gemma 4 E4B‑it is the first model in this size class where the OCR and the follow‑up answers are consistently good enough that I stopped reaching for the cloud fallback.

Tech stack

Model: google/gemma-4-E4B-it (Q4_K_M GGUF, BF16 mmproj) via Unsloth
Runtime: llama.cpp llama-server, bundled per‑arch as an Electron extraResource
App shell: Electron + Vite + TypeScript, packaged with Electron Forge
Storage: SQLite for screenshots + AI results + chat history; images on disk
Auto‑update: GitHub Releases + a tiny installer script served from GitHub Pages

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