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| title: whatfirst small | |
| emoji: 🗂️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: true | |
| license: apache-2.0 | |
| models: | |
| - ggml-org/Qwen2.5-VL-3B-Instruct-GGUF | |
| tags: | |
| - track:backyard | |
| - achievement:offgrid | |
| - achievement:llama | |
| - achievement:fieldnotes | |
| - achievement:tiny-titan | |
| thumbnail: >- | |
| https://cdn-uploads.huggingface.co/production/uploads/67a39e9465c06c95d452bd6d/Z_pvYNAWsx5oR24zkUFZg.png | |
| # whatfirst · small | |
| **Dump everything on your mind — get back what to do *first*, with the math shown.** | |
| ### [▶ Try the live demo](https://huggingface.co/spaces/build-small-hackathon/whatfirst-small) | |
| 🤏 **3B params** (≤ 4B — *Tiny Titan*) · 🔌 **runs 100% offline** — no internet required · 🦙 llama.cpp | |
| [📣 Launch post](https://x.com/tbd_ntbd/status/2066222870657692128) · [what-first.com](https://what-first.com) · Apache-2.0 | |
| A small **local** vision-language model (Qwen2.5-VL-3B, ~2 GB, running on | |
| llama.cpp) reads a messy brain-dump or a photo of a to-do list and turns each | |
| line into a structured task — impact, readiness, effort, deadline. A | |
| **deterministic, transparent scoring engine** then ranks them and tells you the | |
| one thing to start now, showing every number behind the call. No cloud, no API | |
| keys, runs on a laptop. | |
| Built for the [Hugging Face Build Small hackathon](https://huggingface.co/build-small-hackathon) | |
| (Backyard AI track). | |
| 📓 **Field notes:** [an honest write-up of what worked and what didn't](submission/whatfirst-small-writeup.md) — | |
| the small-model story, including where a 3B model wobbles and how the design absorbs it. | |
| ## Demo | |
| [](https://huggingface.co/spaces/build-small-hackathon/whatfirst-small/resolve/main/demo/out/whatfirst-small-demo-loud.mp4) | |
| ▶ **[Watch the demo video](https://huggingface.co/spaces/build-small-hackathon/whatfirst-small/resolve/main/demo/out/whatfirst-small-demo-loud.mp4)** · **[Try the live Space](https://huggingface.co/spaces/build-small-hackathon/whatfirst-small)** | |
| <!-- Rendered as a clickable poster + links so the demo works on GitHub (which strips raw <video>) | |
| and on Hugging Face alike. --> | |
| ## Why this exists | |
| Deciding *what to do first* is a real, daily problem — and most "AI to-do" apps | |
| answer it with a black box. This one keeps the AI where it earns its keep (turning | |
| vague human language into structured fields) and makes the prioritization itself | |
| **legible**: two competing scores (do-it-now vs. de-risk-first), an urgency curve | |
| that explodes as a deadline nears, a quick-win boost for short, high-impact tasks, and | |
| deadlines treated as a hard constraint rather than a number folded into a blob. | |
| The problem — and the prioritization approach — come from | |
| **[what-first.com](https://what-first.com)**, a full web app the same team built | |
| in June 2026. There, a frontier cloud model (Claude) does the language work — | |
| reading your tasks and proposing their impact, readiness, and effort — and a | |
| deterministic engine ranks them. This entry asks a smaller question: can a **3B | |
| model running offline on a laptop** do that same language work? The ranking | |
| engine here is a clean-room Python reimplementation with its own tests, not a | |
| copy of the original. | |
| ## How it works | |
| ``` | |
| brain-dump / photo ──▶ Qwen2.5-VL-3B (llama.cpp, localhost) ──▶ structured tasks | |
| │ | |
| score.py (deterministic) | |
| │ | |
| ranked list + "do this first" | |
| ``` | |
| - `score.py` — the scoring + deadline-ranking engine (pure standard-library math). | |
| - `llm.py` — client for the local llama.cpp server (brain-dump parse, image | |
| extract, single-task re-score). Each call is grammar-constrained to a JSON | |
| object; every model output is re-clamped before scoring. | |
| - `prompts.py` — the system prompts that ask for strict-JSON output and define | |
| the scoring scales. | |
| - `app.py` — the Gradio UI: capture, ranked table, and sliders to correct any | |
| score and re-rank live. | |
| ## Run it locally | |
| ```bash | |
| docker build -t whatfirst-small . | |
| docker run -p 7860:7860 whatfirst-small # first boot downloads ~3.3 GB (model + vision projector) | |
| ``` | |
| Then open http://localhost:7860. On a CPU-only box, expect a few seconds per task | |
| — that's the cost of staying fully on the grid-less side. Tests: | |
| ```bash | |
| python -m pytest test_score.py # or: python test_score.py | |
| ``` | |
| ## Notes | |
| - **Model:** [`ggml-org/Qwen2.5-VL-3B-Instruct-GGUF`](https://huggingface.co/ggml-org/Qwen2.5-VL-3B-Instruct-GGUF) | |
| (Q4_K_M + f16 mmproj), ≤ 32B and laptop-runnable. | |
| - **Off the grid:** all inference is local llama.cpp over localhost; nothing | |
| leaves the box at runtime. | |
| --- | |
| 📓 **See the full story in our [field notes](submission/whatfirst-small-writeup.md)** — | |
| what worked, where a 3B model wobbles, and how the design absorbs it. |