DEV Community: Muhammad Danish

Touch Grass Relay: Pocket Your Phone, Run with Friends, Reach God Mode

Muhammad Danish — Sun, 11 Oct 2026 03:18:00 +0000

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

What I Built

Most fitness apps demand your eyes: flashing rings, complex pace graphs, and turn-by-turn prompts that force you to look down at your screen while running through nature.

Touch Grass Relay turns that inside out. It is a collaborative, real-time running web application designed specifically so your phone stays in your pocket the entire time:

Squad Rooms (Frictionless Joining): One runner creates a squad room and shares a 6-character code or QR code. Teammates join in 3 seconds from their mobile browsers—no app store downloads, no registrations, and no paywalls.
Shared Anime Power Levels: Distance is pooled collectively across all runners in real time to unlock squad transformations:
- 0 km: Base Form
- Awakened: Cyan pulse (Initial limit break)
- Kaioken: Crimson surge (Limits broken)
- Super Syana: Golden aura unlocked
- Syana 2: Violet lightning crackles
- God Mode: Neon pink final form unlocked (Thresholds scale dynamically whether you run solo, in pairs, or in full squads).
Hands-Free Audio Coaching: The Web Speech API announces milestone updates and tier transitions straight to your earbuds: "Aura rising! 10 km combined. Kaioken unlocked!"
Pocket Mode (OLED Power Saver): Once the run starts, the app enters Pocket Mode. The screen goes pure #000000 (drawing near-zero power on modern OLED screens) and engages the Screen Wake Lock API to prevent mobile operating systems from freezing background GPS and WebSockets in your pocket. A 1-second hold-to-unlock gesture prevents accidental fabric friction touches.
Post-Run Battle Story & Badges: At the end of the session, the squad receives a generated battle story, geographical territory coverage computed via Turf.js convex hull algorithms, and individual achievement badges (Top Contributor, First Blood, Iron Will).

Demo

Repository: https://github.com/danishjk2156/Run
Built-in Multi-Runner Simulator: You can test the full live experience right from your desk without stepping outside:

  # Inside server/
  npm run fake-gps

This spins up a 4-runner squad (Goku, Vegeta, Piccolo, Gohan) streaming realistic running GPS telemetry through WebSockets, triggering live tier ascensions and audio cues.

Code

danishjk2156 / Run

🏃‍♂️⚡ TOUCH GRASS RELAY (RUN)

Pocket Your Phone. Run with Friends. Reach God Mode.

A mobile-first, collaborative running web application designed so your phone stays inside your pocket the entire time. Distance is pooled in real-time across your squad to unlock collective anime power transformations, coached hands-free through your earbuds.

Architecture • Features • Workflow • Use Cases • How to Run • Tech Stack

📖 Overview

Most fitness and tracking apps demand your eyes: flashing rings, complex pace charts, and turn-by-turn prompts that force you to look down at your screen while running through nature.

Touch Grass Relay flips this dynamic inside-out:

Zero Screen Glancing: Put on your headphones, start the relay, lock into Pocket Mode, and slide the phone into your pocket.
Shared Anime Transformations: Runners in the same room pool their distances in real-time. Hit collective distance milestones to ascend from Base Form all the way…

View on GitHub

How I Built It

The entire project is built with open web standards and open-source tooling:

📱 Client (Mobile Web / PWA)
├── React 18 + Vite + Tailwind CSS
├── Screen Wake Lock API (keeps GPS & WebSockets active without screen wake)
├── Web Speech API (hands-free audio cues in earbuds)
├── MapLibre GL JS + OpenStreetMap (100% free vector map tiles)
└── Socket.io Client (low-latency coordinate streaming)

🖥️ Server (Node.js + TypeScript)
├── Express + Socket.io Server
├── Zod (strict validation for every incoming GPS batch)
├── Turf.js (Haversine geodesic distance & convex hull area calculations)
├── Tier & Badge Engine (idempotent threshold triggers with squad scaling)
└── In-memory room store (PostgreSQL & Redis migration ready)

The 5-Stage GPS Anti-Cheat Pipeline

To eliminate stationary GPS drift and cheating (cars/bicycles), every coordinate batch passes through a strict validation gate:

Accuracy Gate: Points with error radius > 25m are rejected.
Speed Gate: Speeds > 25 km/h are rejected.
Drift Gate: Discards stationary jitter < 3m.
Teleport Gate: Rejects sudden GPS jumps > 500m.
Rate Limit: Enforces clean point spacing (minimum 3 seconds).

Why Does Open Innovation Matter?

Zero Data Extraction & Surveillance: Commercial fitness platforms hoard and monetize location telemetry, selling personal heatmaps to data brokers. Touch Grass Relay operates on private ephemeral room channels; coordinates belong to you and your squad, not ad networks.
Zero App Store Taxes & Gatekeepers: By using open web standards (WebSockets, Geolocation API, Screen Wake Lock API, Web Speech), there are no 30% developer fees, no approval queues, and no Apple Developer Account restrictions. Anyone with a smartphone browser can run together instantly.
Zero Proprietary API Lock-In: Maps are rendered with MapLibre GL and OpenStreetMap instead of expensive closed map APIs. Audio runs client-side via Web Speech, requiring $0 in cloud TTS subscription fees.
Community Moddability: Because the tier ladder, badges, and story generators are open-source and modular, run clubs can fork the codebase to build custom themes (e.g., sci-fi ranks, trail challenges, or marathon training milestones).

Prize Categories

Hacktoberfest Week 1: Touch Grass Challenge

📚 Title: Learning Ledger — An AI That Remembers What You Learn from Scientific Papers and Tells You When It Becomes Wrong

Muhammad Danish — Mon, 05 Oct 2026 16:21:54 +0000

Primary Tags: #devchallenge, #hacktoberfest, #ai, #opensource

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built Learning Ledger for my close friend and lab partner who spends 20+ hours a week reading academic preprints in computer science and bioinformatics.

Last semester, they experienced a researcher’s worst nightmare: weeks before submitting a thesis proposal, they found out that a primary empirical baseline cited across their work had been quietly retracted due to irreproducible data. They had hundreds of reading notes stored across Notion and markdown files, but no system in their workflow was tracking whether the scientific beliefs they had recorded months ago were still true.

When researchers and students read literature, they formulate beliefs:

"LoRA reduces trainable parameters by 10,000x with comparable accuracy"
"Molecule X inhibits Receptor Y in vitro"

Yet science evolves rapidly: papers get retracted, errata and corrections are issued, or the student slightly misinterprets or exaggerates the author’s exact text.

Learning Ledger is an intelligent research memory system that remembers what you learn from scientific papers and actively alerts you when it becomes wrong. It indexes academic PDFs, lets students ask grounded questions with verbatim citations, verifies that recorded beliefs strictly entail source quotes using typed decision models (Jev 1.13), and continuously monitors literature against retractions, errata, and confidence decay so you know when to unlearn or revise.

Demo

The application runs locally with a modern Scholar Edition dashboard (http://localhost:8000) built with Swiss Modernism 2.0 design principles:

Core Features:

🏛️ Student Scholar Interface: Editorial layout featuring Newsreader serif typography, gold leaf accents, and a subtle millimeter-grid notebook texture.
🎴 Study Cards & Active Recall Mode: A revision toggle that hides verbatim quotes and prompts students: "Can you cite the proof?", allowing active recall testing before revealing the supporting quote.
⚖️ Jev 1.13 Decision Gate: Evaluates quote-to-claim entailment via typed probability ($0.0 - 1.0$), ensuring students don't exaggerate findings.
🛡️ Continuous 4-Layer Audit: Scans claims against a local 300MB Retraction Watch SQLite database, Crossref update-to errata, and field-velocity temporal decay.
📋 One-Click APA Citations: Copies formatted academic citations directly to the clipboard.

(Add your deployed URL or video demo link here if available)

Code

danishjk2156 / learning-ledger-AI

📚 Learning Ledger (AI)

An intelligent research memory system that remembers what you learn from scientific papers and tells you when it becomes wrong.

Live Dashboard • Architecture • Flowchart • Tech Stack • Installation • Git Push Guide

📌 The Problem

When students, graduate researchers, and engineers read scientific literature, they synthesize key findings into mental models or notes:

"Model X reduces fine-tuning parameters by 10,000x with zero accuracy loss"
"Molecule A inhibits Target B in vitro"
"Algorithm Q scales quadratically rather than exponentially"

However, scientific knowledge is not permanent:

The Retraction & Replication Crisis: Thousands of peer-reviewed papers are retracted, withdrawn, or flagged every year due to data manipulation, ethical lapses, or flawed methodologies.
Post-Publication Errata & Amendments: Authors routinely publish corrections and errata via Crossref that alter experimental parameters, margins of error, or code baselines.
Semantic Drift & Human Overstatement: When students summarize…

View on GitHub

Learning Ledger is 100% open-source under the MIT License.

How I Built It

[ Research PDF ] ──► pypdf parser ──► Academic Watermark Stripper
                                               │
               Header-Based Semantic Chunking ──┘
                             │
                             ▼
  ChromaDB Vector Store ◄─── Gemini embedding-001 (Dense Vectors)
       │
       ├─► Student Question ──► IBM Granite Classifier (Local Ollama / HF)
       │                              │
       │                              ▼
       └─► Grounded Retrieval ──► Gemini 3.8 Flash (Verbatim Citations)
                   │
                   ▼
     User records: "Claim X backed by Quote Y"
                   │
                   ▼
    Jev 1.13 Decision Gate (TypeSafe SDK via OpenRouter)
        └─ Noul(claim_support) > 0.50 ──► Stored in SQLite Ledger (Active)
        └─ Noul(claim_support) <= 0.50 ──► Overstatement Alert (Suspect)
                   │
                   ▼
    Continuous Multi-Source Audit (Retraction Watch + Crossref API + Cascade Decay)

Agent Tool Harness (openharness-ai): All user actions (drop_paper, ask_question, learn_claim, revalidate_claims, check_doi) are defined as typed Pydantic tool schemas in src/tools.py, enabling autonomous tool-calling and agent orchestration.
Local Model Routing (IBM Granite): Student questions are classified locally by IBM Granite (granite3-dense:2b via Ollama or cnmoro/granite-question-classifier on Hugging Face). This instantly routes small talk away from the vector database, keeping the pipeline snappy and offline-friendly.
Typed Decision Gate (Jev 1.13 via typesafe-sdk): Instead of asking an LLM to generate unstructured prose that requires brittle regex parsing, Jev returns typed mathematical evaluations:
- Noul ($0.0 - 1.0$): Determines whether the verbatim paper quote strictly supports the student's claimed finding without overstatement.
- Choice: Determines retrieval routing (local_retrieve, llm_synthesize, stop).
4-Layer Continuous Integrity Engine:
- Retraction Watch SQLite Index: Full local offline database generated from the Crossref Retraction Watch dataset (~300MB).
- Crossref REST API: Queries the live update-to endpoint for freshly published errata.
- OpenAlex & Unpaywall: Resolves open-access PDFs, citation counts, and publication version history.
- Field-Velocity Confidence Decay: Models disciplinary aging rate ($\text{Decay} = \frac{1}{1 + \text{velocity} \times \text{age}}$), decaying fast-moving ML papers ($0.50/\text{year}$) faster than pure mathematics ($0.05/\text{year}$).

Why Does Open Innovation Matter?

In scientific education and research, academic reproducibility and trust are paramount.

Closed, proprietary AI systems treat knowledge as black-box text generation. When a commercial model cites a retracted paper or hallucinates a benchmark number, it offers zero visibility into why. Furthermore, locked APIs force students to upload sensitive or unpublished thesis research to third-party clouds.

Open innovation made Learning Ledger possible:

Open Science Data: Crossref and Retraction Watch open datasets allow students to run millisecond-fast integrity audits completely offline without recurring API costs.
Local Inference for Student Privacy: Running local query classification on-device (via IBM Granite and Ollama) ensures draft research questions never leave the student's machine.
Verifiable Probabilities Over Black Boxes: Combining open-source agent harnesses (openharness-ai) with typed decision gates replaces "vibe-based" answers with verifiable numbers.

Prize Categories

Main Category: Build for a Friend (Education & Student Researcher Integrity)
Open-Source AI Track: Best Use of Open-Source AI, Local Inference & Agent Harnesses (openharness-ai, IBM Granite, ChromaDB)