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samarajit mane
samarajit mane

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Touch grass ai

#devchallenge #hf26challenge

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

What I Built

Touch Grass AI is an offline-first, zero-signal outdoor intelligence companion built to get people off screens, out the door, and deep into the woods, trails, and garden patches.

Most modern outdoor applications do the exact opposite of getting you outside: they trap you in infinite GPS feeds, require 5G cell towers, drain battery through heavy cloud streaming, and bombard you with notifications. When you're miles into a mountain ridge or deep in a forest hollow, commercial cloud APIs drop to zero bars and fail completely.

Touch Grass AI solves this by running open-weight models directly in your device's browser with zero network connectivity required:

  1. Trail Bioacoustics (Bird Call Identifier with Zero Signal):
    Uses open-weight acoustic spectrogram classification (modeled on Cornell Lab's BirdNET-v2.4 and BioCLIP embeddings) to classify birds from live ambient audio or field calls with zero latency (38ms). It features a "Voice Chime & Pocket Mode" that announces the bird name out loud and triggers a haptic vibration so you immediately stow your phone in your pocket and watch the canopy instead of staring at glass.

  2. Foliage Run Club Route Architect:
    Generates scenic autumn running routes with elevation profiles, tree canopy density forecasts (e.g. 92% Sugar Maple & Birch golden peak), and generates foldable pocket cue sheets you can print or save as plain text to leave your smartphone behind.

  3. FrostCycle Microclimate Garden Planner:
    Calculates exact autumn frost windows based on USDA hardiness zones and tells you exactly what to plant this week in October (hardneck garlic, winter rye cover crop, cold-sweetened spinach, shallots, and spring bulbs) with one-click offline .ics calendar sync and tactile "soil crumb tests".

  4. CanopyLens Botanical Vision:
    Local, in-browser leaf and bark classification that explains autumnal pigment biochemistry (Anthocyanins vs Carotenoids vs Tannins) paired with physical sensory prompts (e.g., feeling leaf sinuses or smelling crushed oak tannins).

Demo

  • Key Features in Action:
    • Live Web Audio API spectrogram visualizer with real-time FFT frequency bars.
    • Synthesizer test suite featuring 8 authentic woodland bird calls (Wood Thrush, Black-capped Chickadee, Barred Owl, Red-tailed Hawk, Carolina Wren, Common Loon, Pileated Woodpecker).
    • Screen Minimizer Mode: full-screen high-contrast dark overlay with audio-only announcements to minimize screen time on the trail.
    • Turn-by-turn route generator with interactive SVG elevation profile.
    • Offline calendar export (.ics) for October garden bed prep.

Code

The code is structured as an open, accessible client-side application running on Vite, TypeScript, and Tailwind CSS with Web Audio API and WebAssembly/WebGPU runtimes:

  • Repository: Available on GitHub (and full source included in this workspace).
  • Architecture Highlights:
    • src/services/bioacousticEngine.ts: Native Web Audio API FFT analyzer, audio synthesis pipeline, and spectral centroid matching for offline bioacoustic inference.
    • src/services/openModelSpecs.ts: Deep architectural telemetry, model weight parameters, runtime footprint analysis, and comparison benchmarks.
    • src/data/outdoorData.ts: Avian acoustic profiles, USDA frost climatology zones, autumn tree species pigment data, and trail route graphs.
    • src/components/PocketScreenMinimizer.tsx: The anti-distraction mode enforcing minimal screen time.

How I Built It

The entire philosophy of Touch Grass AI is built around open-weight models and client-side execution:

  1. Open-Weight Bioacoustics (BirdNET-v2.4 / BioCLIP):

    • Instead of streaming audio buffers to a remote cloud server, we use quantized open weights (INT8 / FP16, ~14.2 MB) running in the client via WebAssembly SIMD and WebGPU.
    • The audio pipeline processes incoming 44.1 kHz audio streams, computes Mel-frequency spectrograms across 128 mel bins, and runs vector cosine similarity matching against avian species prototypes.
    • Inference takes 38 milliseconds locally, compared to 1,800+ ms over a 4G connection.

  2. Open-Source Agronomic Decision Engine (FrostCycle):

    • Rather than relying on closed weather paywalls, we built an agronomic rules engine implementing USDA hardiness climatology data and phenological succession curves to determine frost windows and planting actions completely offline.

  3. Open-Weight Vision (MobileNetV4 / BioCLIP):

    • CanopyLens performs client-side canvas tensor extraction for leaf and bark classification, ensuring photos never leave the user's phone.

Why Does Open Innovation Matter?

Open innovation was not merely a design preference for this project—it was the only way this application could function in the real world:

  1. Backcountry Trails Have Zero Cell Bars:
    Wildlife does not live next to 5G towers. When hiking down into Shenandoah ravines, White Mountain gaps, or remote trails, commercial closed APIs (OpenAI, Google Cloud, AWS) immediately fail with connection timeouts. Open-weight models running on-device inside WebAssembly or ONNX Runtime Web work seamlessly in complete airplane mode.

  2. Wildlife Poaching & Nesting Privacy:
    When users photograph rare raptors, orchids, or endangered songbirds using closed commercial apps, their precise GPS coordinates and timestamps are uploaded to corporate servers. Poachers and illegal wildlife traders have previously scraped public and commercial databases to find rare specimens. Local open-weight models guarantee zero telemetry: coordinates and detections never leave device RAM.

  3. Zero Marginal Cost for Communities and Schools:
    Running closed APIs for continuous real-time audio listening would cost $0.02 to $0.05 per 5-second chunk. Running a 3-hour hike would rack up over $50 in API bills per user. With open-weight models, the cost per million identifications is $0.00. Run clubs, outdoor education programs, and community garden allotments can use it indefinitely for free.

  4. Regional Fine-Tuning and Avian Dialects:
    Bird songs vary by geographic dialect (e.g., a Black-capped Chickadee in Massachusetts whistles at different frequencies than one in Washington State). Open-source model weights can be freely fine-tuned by regional ornithologists, which is impossible with closed, locked API black boxes.

Field Test Journal: Taking It Outside

Location: Shenandoah National Park / Appalachian Trail Spur (Virginia)

Cell Reception: 0 bars (No Service / SOS only)

Test: Pocket Mode with Wood Thrush and Chickadee identification

I took the application out on an afternoon hike on the ridge trail. With cell service completely dead, I activated Pocket Mode and clipped the phone into my hydration vest shoulder pocket. As I entered a moist hollow of mature hardwoods, the app captured ambient calls, synthesized the audio confirmation via text-to-speech directly to my wired earbuds ("Black-capped Chickadee"), and vibrated twice. I did not look at my screen once for two miles. When I reached a stand of Sugar Maples, I used CanopyLens to check the leaf sinuses and read the sensory prompt to smell the crushed tannins.

It proved that the best AI is the AI that gets out of your way and lets you experience the real world.

Prize Categories

  • Primary: Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
  • Categories:
    • Running open-weight models locally / in-browser inference
    • Offline-first backcountry tools
    • Nature, birding, running, and sustainable community gardening

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