README.md

metadata

title: loosecanvas
emoji: 🕸️
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Local AI that turns talk into a trust-tagged map
models:
  - unsloth/gemma-4-26B-A4B-it-qat-GGUF
suggested_hardware: l4x1
startup_duration_timeout: 2h
tags:
  - build-small-hackathon
  - track:wood
  - achievement:offgrid
  - achievement:llama
  - achievement:offbrand
  - achievement:fieldnotes
  - gradio
  - knowledge-graph
  - local-llm
  - llama.cpp
  - gemma
  - cytoscape
  - off-the-grid

loosecanvas 🕸️

Talk through an idea. Watch it become a map — and decide which connections are real.

The problem

LLMs assert. They hand you fluent text where a hard fact, a hedge, and a confident hallucination all look identical — so you can't tell what to trust, and the "knowledge" you build with them quietly rots. A knowledge graph an AI fills in on its own is just a prettier version of the same problem.

The value prop: an AI that proposes, but never decides

loosecanvas is a local, co-created understanding map. You chat; a small local model proposes concepts and connections; you accept, reject, or edit every single proposal. Each claim carries four independent trust fields that never collapse into one — origin (frozen at birth), claim_type, support_state, review_state — so a model's guess is permanently marked a guess until you say otherwise. Export drops unreviewed model-inferred claims, so the map you take with you is one you actually vouched for. Messy thinking in; a trustworthy, co-owned map out.

TL;DR for judges

• Track: 🌲 Thousand Token Wood — a small local model is the whole point.
• Off the grid: zero cloud API calls. 100% local inference via llama.cpp. base_url is hardcoded to localhost; the API key is a dummy not-needed.
• Small model, load-bearing: Gemma 4 26B-A4B (~25B active params, GGUF, QAT) — no fine-tune, stock Unsloth weights. The small-model constraint forces the good UX: the human is in the loop because the model shouldn't be trusted blind.
• Off-brand UI: a real custom Svelte 5 + TypeScript Cytoscape.js Gradio component, not a chatbot with a decorative graph beside it.
• Field notes: a genuine build write-up ships with the submission (see links).

Features

• Magic build from a paragraph. Paste or write messy thoughts, press Send, and watch the graph assemble live as the model streams concepts and edges.
• Trust-tagged everything. Model proposals land with an amber "awaiting review" badge. Accept, reject, or edit — origin is never silently upgraded.
• Color the clusters. Community detection groups related ideas at a glance.
• Find a hidden connection. Ask the model to surface a surprising cross-domain link, articulated in plain language — then judge it.
• Trust-gated export. Unreviewed guesses are dropped on the way out.

Demo

• Live Space: https://huggingface.co/spaces/build-small-hackathon/loosecanvas (goes live when transferred to the org and made public)
• Demo video: TODO

Architecture / system flow

A turn is a negotiation, not autocomplete:

You chat ─▶ LangChain create_agent (local llama.cpp endpoint, streams tokens + tool calls)
        ─▶ agent tools accumulate proposed actions
        ─▶ validator + reducer DECIDE (pure: actions → GraphPatch + ScenePatch)
        ─▶ graph repository + renderer adapter ─▶ RendererPatch ─▶ Cytoscape.js canvas

The LLM proposes; the validator and reducer decide. Rejected actions return a reason string the model can self-correct from. Three layers stay separate: the portable product artifact (graph + claims), the runtime/control plane, and the frontend (which owns positions and pan/zoom). The trust model is enforced in code, not by prompting.

Tech stack

Layer	Choice
Model	Gemma 4 26B-A4B-it-qat (GGUF, ~25B active params) — stock Unsloth
Inference	llama.cpp OpenAI-compatible server, 100% local (127.0.0.1:8080)
Agent loop	LangChain create_agent, streaming tools
Backend	Python 3.13, FastAPI + Gradio, uvicorn (0.0.0.0:7860)
Frontend	Svelte 5 + TypeScript, Cytoscape.js — custom Gradio component
Deploy	Single self-contained Docker Space (llama-server + app, one image)

Hackathon badges

Tag	Why it's earned
track:wood	A small local model (Gemma 4 26B-A4B) is the core of the product.
achievement:offgrid	Zero cloud API calls; all inference is local llama.cpp.
achievement:llama	llama.cpp is the inference runtime.
achievement:offbrand	Real custom Svelte 5 + Cytoscape.js component, not default Gradio UI.
achievement:fieldnotes	A genuine published build write-up ships with the submission.

Submission links

Item	Link
Live Space	https://huggingface.co/spaces/build-small-hackathon/loosecanvas (on transfer)
Demo video	TODO
Social post	TODO
Field notes	submission/FIELD_NOTES.md (in source repo)
Source	GitHub — Joshua Sundance Bailey
Team	@joshuasundance

Run it / deploy

This Space runs a single self-contained Docker image: llama-server loads the GGUF in-container on 127.0.0.1:8080, and uvicorn serves the app on 0.0.0.0:7860. The model repo (unsloth/gemma-4-26B-A4B-it-qat-GGUF) is public — no token needed.

suggested_hardware: l4x1 is advisory; pick Nvidia L4 in Space Settings to run on GPU. startup_duration_timeout: 2h covers the runtime model download (~13.6 GB) plus cold-GPU load. To run locally instead, see the source repo's README.md.

Built for the Build Small Hackathon — Thousand Token Wood track.