---
title: Ready to Submit?
emoji: 👁
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 6.18.0
python_version: '3.12'
app_file: app.py
startup_duration_timeout: 45min
pinned: false
license: mit
short_description: Evaluates your HF Space for Build Small Hackathon
tags:
  - track:backyard
  - sponsor:nvidia
  - sponsor:openbmb
  - achievement:offbrand
models:
  - nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
  - JetBrains/Mellum2-12B-A2.5B-Instruct
  - openbmb/MiniCPM5-1B
---

# 👁 Ready to Submit?

**The app is the question.** Point it at any Space in the
`build-small-hackathon` org and it checks the entry rules from the
[official field guide](https://build-small-hackathon-field-guide.hf.space/)
— then a small model (your pick, under 32B of course) writes you a grounded,
actionable review.

## The idea

The hackathon has six entry rules, two tracks, four sponsor prizes, six
achievement badges and six judged bonus awards — and the difference between
"submitted" and "eligible" hides in README frontmatter tags like
`track:backyard` and `achievement:offgrid`. Ready to Submit? automates the
pre-flight check: it verifies the verifiable (deterministically, via the HF
Hub API) and lets a small model handle the judgment calls (track fit, prize
opportunities, README polish), grounded in the machine-verified facts so it
can't make things up.

## How it works

1. **Grounded checks (no LLM):** fetches the target Space's metadata, README
   and source via the public Hub API; parses the frontmatter tags against the
   canonical ids from the field guide's own source; finds demo-video and
   social-post links; detects every Hub model referenced by the app and looks
   up its real parameter count against the 32B cap (and the ≤4B Tiny Titan
   bar).
2. **Small-model review:** the checklist + facts + rules digest go to the
   reviewer model you picked, which streams back fixes, track-fit reasoning,
   and the prizes/badges the Space could claim but hasn't.

## Tech

- **Models (pick your reviewer):** NVIDIA Nemotron 3 Nano 4B (default —
  3.97B params, a hybrid Mamba-Transformer that even fits the Tiny Titan
  bar), JetBrains Mellum 2 12B-A2.5B Instruct, OpenBMB MiniCPM5 1B.
- **Runtime:** `gr.Server()` on ZeroGPU — plain FastAPI routes serve a
  custom HTMX frontend (no stock Gradio components anywhere), and the
  review streams through a Gradio-queued endpoint via `@gradio/client`,
  `transformers` + bf16, `TextIteratorStreamer`.
- **Custom UI:** hand-rolled pastel re-skin of the field guide's
  woodblock-press design language — paper grain, dashed-ring stamp badges,
  self-hosted Archivo/Spline Sans Mono, htmx swaps with a friendly loading
  stamp. That's the `achievement:offbrand` story.
- **Grounding:** rules and canonical tags extracted from the field guide
  Space's source of truth (`src/lib/data/content.ts`, `src/lib/readme.ts`),
  embedded as the reviewer's system context; checks (including Codex commit
  attribution and per-model parameter counts) are deterministic Hub API
  calls, so the model can't invent facts.
- **Honesty:** every AI review ships with a disclaimer — double-check
  against the official field guide regardless of what the app says.

## Links

- 🎬 Demo video: [demo.mp4](https://huggingface.co/spaces/build-small-hackathon/ready-to-submit/resolve/main/demo.mp4)
- 📣 Social post: https://x.com/amphetamarina/status/2065435918509441045