🍁Wayside: Collect your walk.

Archangel — Sun, 11 Oct 2026 18:13:49 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Most outdoor AI apps demand your attention on a screen: tracking GPS routes, grinding streak counters, or squinting at maps. Wayside does the opposite. The screen is the shortest part of the experience; the walk is where the project actually happens.

Wayside turns a simple walk into an interactive specimen collection and illustrated storybook:

Head outside and observe: You go on a walk and photograph small, overlooked things along the way—a scarlet maple leaf stuck in moss, an acorn with its cap askew, an abandoned candy wrapper, or a quiet neighborhood cat.
Curate your specimens: When you return, Wayside removes the background clutter with an on-device cutout model and identifies each find with a vision-language model. Each item is mounted on a physical-style specimen card inside an interactive wooden shadowbox.
Add your field notes: Jot down personal sensory memories (what it smelled like, where it was hiding, or what you were thinking).
Weave the narrative: Choose which specimens to include, set an atmosphere (mystery, cozy, adventure, quiet, or spooky), and pick a location. A local language model weaves your finds and handwritten notes into a coherent short story.
Illustrate the cover: A local diffusion model paints a custom 512×512 watercolor storybook cover based on your specimens in their setting, binding your walk into a digital keepsake.

Wayside is built for hikers, neighborhood wanderers, naturalists, and anyone who wants a reason to put their phone in their pocket, look at the trail, and turn real-world observations into imaginative fiction.

Demo

Video: https://youtu.be/2X3cnlWAxoQ

Deployment: https://wayside-46at.onrender.com/

(sometimes deployment timeouts :( trying my best to keep it up, couldn't get the render free credits)

Local Run: Follow the Quick Start steps below to run it on your own laptop in under two minutes.

Screenshots:

Code

Check it out here: Github Repository

How I Built It

Wayside is built entirely around four open-weight models running locally on everyday consumer hardware (tested on a standard Windows laptop with an Intel Core i7 CPU and no dedicated GPU). There are zero closed AI APIs, zero subscriptions, and zero paywalls.

The Open Model Stack

Background Removal (rembg with u2netp): At just 4.4 MB, the lightweight u2netp ONNX model isolates leaves,flowers, animals and curiosities from sidewalk and grass backgrounds in under 3 seconds on CPU.
Vision Labeling (HuggingFaceTB/SmolVLM-256M-Instruct): A compact 494 MB vision-language model that inspects the isolated find, names it, and writes an atmospheric one-line description without bloating system memory.
Narrative Generation (Qwen/Qwen2.5-0.5B-Instruct): A 953 MB instruct model that weaves real find labels and human field notes into an imaginative story. It avoids generic lists, treating your items as clues and characters in a continuous narrative.
Storybook Cover Illustration (stabilityai/sd-turbo via diffusers): Operates in a two-step sequence inside a single lock:

The Qwen story model distills the story and finds into a visual scene prompt of under 28 words.
SD-Turbo generates a rich 512×512 watercolor storybook cover in just 2 inference steps (~25s on CPU).

Frontend: Built with vanilla HTML, modern CSS variables (featuring six themes: Moss, Tide, Dusk, Blossom, Honey, and Night), and native JavaScript ES modules. Zero build steps, zero npm bundle bloat.

Why Does Open Innovation Matter?

Open innovation is what made Wayside possible:

True Privacy for Real Walks: Your walk locations, outdoor memories, and personal field notes never leave your computer. Nothing is sent to commercial cloud AI vendors, data scrapers, or third-party servers.
Runs on Everyday Consumer Hardware: You don’t need an expensive cloud GPU instance or high-end graphics card. By selecting high-efficiency open models (u2netp, SmolVLM-256M, Qwen2.5-0.5B, sd-turbo), the entire pipeline runs smoothly on standard laptop CPUs.
Zero Marginal Cost: Walks should be free. With open models running on your own machine, there are no monthly token subscriptions, API rate limit paywalls, or per-photo charges.
Full Transparency & Auditability: Every model checkpoint and line of code is open-source. Anyone can inspect how their data is processed, tune prompts, or swap model checkpoints.

Prize Categories

Best Use of Render Track (deployment)
Best Use of ElevenLabs (voiceover)