Open the rubric your instructor shared, paste your draft, and get calm, rubric-aligned feedback from a model you control — watch your aim close on the bullseye with every revision.
Your instructor shares a .ffrubric file — the same criteria your real
submission is assessed against. Open it in the app.
Paste or open your draft, then pick your engine: a free local model via Ollama (fully offline), your class server, or your own API key.
Feedback lands per rubric criterion with strengths and next moves — and a dartboard that shows your aim closing on the bullseye across drafts.
Pick a sample draft and assess it. This demo plays canned feedback so it can run in your browser — the app runs a real model of your choosing, with the same rubric-aligned result.
Rubric: Essay 1 — Critical Analysis (Argument 40% · Evidence 60%), the same sample .ffrubric you can open in the app.
The app’s engine is an ordinary Python package, feedforward-practice. If you have uv and Ollama, you can run a genuine assessment from your terminal before installing anything:
# grab the sample rubric, write a couple of paragraphs into draft.md, then: curl -LO https://michael-borck.github.io/feed-forward-desktop/sample.ffrubric uvx --from "feedforward-practice" feedforward-practice assess \ --rubric sample.ffrubric --draft draft.md --model llama3.2:3b # remote endpoint or your own key? add: --base-url … --api-key …
First run sets up the feedback engine automatically
(needs Python 3.11+). Fully offline once a local model is pulled.
All versions and release notes on the
GitHub releases page.
FeedForward Desktop is the standalone companion to
FeedForward, the instructor-controlled
feedback platform. Export any assignment’s rubric as a .ffrubric file and share it
with your cohort — students rehearse privately against your actual criteria, then submit through
your FeedForward site as usual. Practice feedback is formative guidance, never a grade, and no
student work touches a server you have to look after.