DEV Community: Anurag T

Down-Chorus

Anurag T — Sun, 11 Oct 2026 12:46:39 +0000

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

What I Built

Dawn Chorus Pro is a lightweight tool for hikers and birdwatchers that gets you off your phone and into nature.

The Problem

When people go birdwatching, they stare at their screens trying to identify calls in real-time. This ruins the walk, drains phone battery, and sound classifiers often make mistakes (confusing wind or car horns with rare birds).

The Solution: "Touch Grass First, Check Later"

During the walk: Keep your phone in your pocket, record the morning bird sounds, and just enjoy the hike.
After the walk: Upload your acoustic log (BirdNET CSV).
Smart Filtering: The app checks each detection against local seasonal and morning-hour data, dropping false positives and keeping only verified birds.
Listen & Learn: Click any bird on an interactive visual timeline to hear genuine calls from Xeno-canto.
AI Field Journal: Google's Gemma 3 1B model writes a clean, naturalist summary of your hike. Screen time is kept to just 1 minute after a 45-minute outdoor walk!

Demo

GitHub Repository: https://github.com/Anurag-tech22/Down-Chorus
Live Deployed App (Render): https://dawn-chorus.onrender.com


bash
git clone https://github.com/Anurag-tech22/Down-Chorus.git
cd Down-Chorus
docker compose up --build
Open http://localhost:8000 and click "Try the sample walk".

Code

  
    
      
      
        Anurag-tech22
       / 
        Down-Chorus
      
    
    
      
    
  
  
    

Dawn Chorus Pro





Dawn Chorus Pro is a lightweight web application for analyzing ambient bird sounds. It processes audio recordings through open-source acoustic analysis tools, verifies detections against regional/seasonal priors, and generates a field journal summary.


Core Features




Priors Verification: Filters raw acoustic CSV outputs against geographic, seasonal, and temporal metadata to reduce false positives.

Audio Integration: Fetches and streams reference audio directly from the Xeno-canto database for verified species.

Automated Field Journal: Summarizes verified detections using a background language model service or falls back to a deterministic template.

Client-Side Rendering: Generates SVG timelines and interaction logic entirely in vanilla JavaScript, avoiding heavy charting dependencies.



Architecture







Input: User uploads a CSV containing timestamped acoustic detections.



Verification Pipeline: FastAPI backend parses the CSV and applies filtering rules defined in app/species.py.



LLM Integration (Optional): Verified metadata is passed to a background language model service to synthesize a human-readable journal entry.



Presentation:…




  


  View on GitHub




Main parts of the project:

app/species.py & app/verify.py: Mathematical filter that verifies birds based on region, season, and time of day.

app/journal.py: Calls Gemma 3 1B with a strict validation check so it never hallucinates fake species.

app/main.py: Fast, secure FastAPI backend with audio streaming and rate limiting.

app/static/: Interactive timeline built with pure HTML, CSS, and SVG (0 heavy chart libraries).

render.yaml: Infrastructure blueprint for Render.

  
  
  How I Built It


Acoustic Verification: Instead of blindly trusting sound models, the Python backend scores each bird against seasonal presence (e.g., is this bird actually here in June?) and morning chorus activity.

Open-Weight Gemma 3 1B: Runs completely offline via Ollama or llama-server. It takes raw bird names and writes a warm journal entry. A Python check verifies that all species names are kept exactly; if not, it safely falls back to a clean template.

Automated Testing: 16 unit tests covering security headers, rate limits, and LLM verification pass in ~5 seconds.

  
  
  Why Does Open Innovation Matter?


Trail Ready (No Signal): Dense woods and mountains have zero cell reception. Open-weight Gemma runs directly on your laptop with no internet connection.

Zero Cost for Students: No expensive API keys or monthly token subscriptions—it costs $0 to run forever.

Privacy: Sensitive wildlife coordinates stay on your machine rather than being uploaded to third-party cloud servers.

My Agent Session

Built with pair-programming assistance from Antigravity to architect the priors filter, containerize Gemma, and configure Render blueprints.

Prize Categories


Best Use of Gemma ($200)

Uses Google's open-weight Gemma 3 1B (gemma3:1b / GGUF).

Runs 100% locally and offline on personal hardware.

Includes Python guardrails against hallucinations.
Best Use of Render ($200)

Configured via render.yaml using Render's multi-service blueprint.

Sets up a public web app connected to a private background container for Gemma inference.


<!-- Thanks for participating! -->

Dawn-Chorus

Anurag T — Sun, 11 Oct 2026 12:40:56 +0000

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

What I Built

Dawn Chorus Pro is a lightweight tool for hikers and birdwatchers that gets you off your phone and into nature.

The Problem

The Solution: "Touch Grass First, Check Later"

During the walk: Keep your phone in your pocket, record the morning bird sounds, and just enjoy the hike.
After the walk: Upload your acoustic log (BirdNET CSV).
Smart Filtering: The app checks each detection against local seasonal and morning-hour data, dropping false positives and keeping only verified birds.
Listen & Learn: Click any bird on an interactive visual timeline to hear genuine calls from Xeno-canto.
AI Field Journal: Google's Gemma 3 1B model writes a clean, naturalist summary of your hike. Screen time is kept to just 1 minute after a 45-minute outdoor walk!

Demo

GitHub Repository: https://github.com/Anurag-tech22/Down-Chorus
Live Deployed App (Render): https://down-chorus.onrender.com/

Run It Locally in 1 Step (Offline):


bash
git clone https://github.com/Anurag-tech22/Down-Chorus.git
cd Down-Chorus
docker compose up --build
Open http://localhost:8000 and click "Try the sample walk".

Code

  
    
      
      
        Anurag-tech22
       / 
        Down-Chorus
      
    
    
      
    
  
  
    

Dawn Chorus Pro





Dawn Chorus Pro is a lightweight web application for analyzing ambient bird sounds. It processes audio recordings through open-source acoustic analysis tools, verifies detections against regional/seasonal priors, and generates a field journal summary.


Core Features




Priors Verification: Filters raw acoustic CSV outputs against geographic, seasonal, and temporal metadata to reduce false positives.

Audio Integration: Fetches and streams reference audio directly from the Xeno-canto database for verified species.

Automated Field Journal: Summarizes verified detections using a background language model service or falls back to a deterministic template.

Client-Side Rendering: Generates SVG timelines and interaction logic entirely in vanilla JavaScript, avoiding heavy charting dependencies.



Architecture







Input: User uploads a CSV containing timestamped acoustic detections.



Verification Pipeline: FastAPI backend parses the CSV and applies filtering rules defined in app/species.py.



LLM Integration (Optional): Verified metadata is passed to a background language model service to synthesize a human-readable journal entry.



Presentation:…




  


  View on GitHub




Main parts of the project:

app/species.py & app/verify.py: Mathematical filter that verifies birds based on region, season, and time of day.

app/journal.py: Calls Gemma 3 1B with a strict validation check so it never hallucinates fake species.

app/main.py: Fast, secure FastAPI backend with audio streaming and rate limiting.

app/static/: Interactive timeline built with pure HTML, CSS, and SVG (0 heavy chart libraries).

render.yaml: Infrastructure blueprint for Render.

How I Built It

Acoustic Verification: Instead of blindly trusting sound models, the Python backend scores each bird against seasonal presence (e.g., is this bird actually here in June?) and morning chorus activity.

Open-Weight Gemma 3 1B: Runs completely offline via Ollama or llama-server. It takes raw bird names and writes a warm journal entry. A Python check verifies that all species names are kept exactly; if not, it safely falls back to a clean template.

Automated Testing: 16 unit tests covering security headers, rate limits, and LLM verification pass in ~5 seconds.

Why Does Open Innovation Matter?

Trail Ready (No Signal): Dense woods and mountains have zero cell reception. Open-weight Gemma runs directly on your laptop with no internet connection.

Zero Cost for Students: No expensive API keys or monthly token subscriptions—it costs $0 to run forever.

Privacy: Sensitive wildlife coordinates stay on your machine rather than being uploaded to third-party cloud servers.

My Agent Session

Built with pair-programming assistance from Antigravity to architect the priors filter, containerize Gemma, and configure Render blueprints.

Prize Categories


Best Use of Gemma ($200)

Uses Google's open-weight Gemma 3 1B (gemma3:1b / GGUF).

Runs 100% locally and offline on personal hardware.

Includes Python guardrails against hallucinations.
Best Use of Render ($200)

Configured via render.yaml using Render's multi-service blueprint.

Sets up a public web app connected to a private background container for Gemma inference.


  
  
  Community Wisdom


References that helped shape this project:

Running Local LLMs with Docker and llama.cpp - for small containerized inference.

Environmental Audio Pipelines in Python - for acoustic verification principles.

<!-- Thanks for participating! -->

OmniGuard AI

Anurag T — Sun, 04 Oct 2026 14:38:33 +0000

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

What I Built
I built OmniGuard AI — a free, open-source tool that detects scams, phishing messages, and fake UPI payment requests before your family falls for them.

I made it for my parents and grandparents. They keep getting fake WhatsApp messages like "Your bank account is blocked, click here immediately." They never know if it is real or a scam. Now they just paste the message and OmniGuard AI tells them instantly — is it safe or dangerous, why it was flagged, and exactly what to do next.

It works in 10 languages including Hindi and Marathi so my family can use it in their own language. 🇮🇳

Demo
🔗 https://kavach-web-2mkc.onrender.com

Code

Anurag-tech22 / DEV1

OmniGuard AI

OmniGuard AI is an open-source fraud detection and cyber defense platform. It is designed to identify and neutralize social engineering attacks, phishing, and financial fraud across digital channels.

Features • Architecture • Installation • API • Contributing • License

Features

Threat Core: Pattern recognition for detecting digital threats, fake KYC, lottery bait, APK droppers, and courier imposter schemes.
Threat Radar: Live scam feed, homoglyph link inspection, and QR/UPI debit analyzer.
Audio Guard: Audio waveform visualizer and speech analyzer for voice cloning detection.
Android APK Sandbox: Manifest privilege analyzer, Accessibility hijacking detector, and trojan profiler.
Dossier Export: Formats incident data into structured evidence dossiers for legal submission.
Multi-language Support: Localization in English, Hindi, Marathi, Spanish, French, German, Chinese, Japanese, Arabic, and Portuguese.
Interactive Drills: Scenario simulator for training users to identify phishing and authority impersonation.
Breach Intel: Checks compromised credentials, leaked…

View on GitHub

How I Built It
Backend: FastAPI (Python) with a custom heuristic scam detection rule engine
Frontend: React 19 + Vite — message scanner, live threat radar, practice mode, and a downloadable threat report you can file with the police
Deployed on Render using Docker multi-stage build — Node builds the React app, Python serves the backend, one single container
For AI, I built a custom open-source AI agent harness called OmniAIEngine. It automatically routes threat analysis to the best available model:

Local open-source inference — runs Qwen 2.5 1.5B via llama.cpp fully self-hosted, zero API cost
Falls back to Gemini or OpenAI if API keys are provided
If nothing is available, uses the built-in heuristic rule engine
This means the app works completely offline with open-weight models and needs no paid API at all.

Why Does Open Innovation Matter?
My family cannot afford a paid security product. With open source, I built something completely free that anyone in India can use. No subscriptions, no black box — you can see exactly why something was flagged, which builds real trust with non-tech users.

Open-weight models like Qwen running via llama.cpp made it possible to run real AI inference without sending anyone's private messages to a third-party cloud. Privacy-first and free — that is what open innovation made possible here.

Prize Categories
🏆 Render — Best Use of Render

DEV Community: Anurag T

Down-Chorus

What I Built

The Problem

The Solution: "Touch Grass First, Check Later"

Demo

Anurag-tech22 / Down-Chorus

Dawn Chorus Pro

Core Features

Architecture

How I Built It

Why Does Open Innovation Matter?

Dawn-Chorus

What I Built

The Problem

The Solution: "Touch Grass First, Check Later"

Demo

Run It Locally in 1 Step (Offline):

Anurag-tech22 / Down-Chorus

Dawn Chorus Pro

Core Features

Architecture

Community Wisdom

OmniGuard AI

Anurag-tech22 / DEV1

OmniGuard AI

Features