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check out i build Mindora — AI Mental Wellness Companion that feel comforting, memorable, and professional. https://dev.to/adibanaushad0/when-your-mind-feels-heavy-you-dont-have-to-carry-it-alone-mindora-ai-mental-wellness-6ik

ABIBA NAUSHAD — Sun, 18 Oct 2026 07:00:30 +0000

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

ABIBA NAUSHAD

Oct 18

When your mind feels heavy, you don’t have to carry it alone ♡,Mindora : AI Mental Wellness Companion

#devchallenge #hf26challenge #opensource

7 min read

When your mind feels heavy, you don’t have to carry it alone ♡,Mindora : AI Mental Wellness Companion

ABIBA NAUSHAD — Sun, 18 Oct 2026 06:56:20 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Meet Mindora — AI Mental Wellness Companion, a voice-first, privacy-focused daily emotional reflection and stress-decompression web app powered by local Google Gemma (via Ollama) and ElevenLabs voice synthesis.

The Problem It Solves

Students, freelancers, and remote workers frequently experience late-night burnout, academic overwhelm, and decision paralysis. However:

Traditional therapy is expensive and not always accessible at 2:00 AM when exam or deadline anxiety hits.
Generic cloud AI chatbots often feel cold, dump overwhelming walls of text, or raise serious privacy concerns when users share personal struggles.

Mindora bridges the gap as a non-clinical mental-wellness companion (explicitly designed not to replace a psychiatrist or diagnose disorders). Instead of a blank chat box, Mindora guides users through a structured, soothing 4-Step Decompression Journey in a warm pastel-green and cream interface:

Step	Stage Name	What Happens	User Value
Step 1	Welcome & Setup	User selects language (English, Hindi, or Hinglish) and mode (Voice + Text or Quiet Text).	Establishes clear non-clinical boundaries & 1-click session privacy.
Step 2	Emotional Check-In	6 gentle, skippable cards assessing Mood, Stress Frequency, Triggers, Sleep, Daily Impact, and Support Goal.	Produces a qualitative reflection (never fake clinical stress scores).
Step 3	Gemma + ElevenLabs Voice Session	User speaks or types their concern. Gemma crafts a warm, grounded reply and ElevenLabs speaks it aloud via a pulsing Voice Orb.	Validates feelings, asks one gentle question at a time, and avoids text overload.
Step 4	Closure & Grounding	Interactive 4-4-4 Box Breathing Pacer, 1 manageable next step, and an optional closing journal prompt.	Users leave the session calmer with a single concrete action item.

Who Would Pay For It? (The Side Hustle & Monetization Model)

Mindora is designed as a sustainable B2C Freemium & B2B Campus/Workspace Wellness Micro-SaaS:

Tier	Target Audience	Pricing	Key Features Included
Free Tier	Students & Everyday Users	$0 / mo	Daily 6-card emotional check-in, local Gemma reflection, browser TTS voice, & 4-4-4 box breathing tool.
Mindora Calm Pro	High-Stress Professionals & Creators	$7 / mo	Unlimited ElevenLabs ultra-realistic soothing voices, multilingual voice reflection (English/Hindi/Hinglish), & private encrypted local journal exports.
Mindora Campus / Team	Universities, Bootcamps & Remote Startups	$199 / mo	Anonymous campus/team stress-trigger heatmaps (zero personal chat logs stored) + custom local helpline integrations during exam/launch weeks.

Demo

Live Demo / Video Walkthrough:

Key Screens & Experience

Soothing Non-Clinical Aesthetic: Designed with Alabaster Cream (#FAF8F5) and Sage Green (#6B9080) to avoid the cold, sterile feel of hospital-white apps.
Built-In Crisis Safety Pathway: Before any message reaches the AI model, FastAPI screens for urgent distress or self-harm indicators. If detected, normal AI advice pauses immediately and surfaces a 24/7 Human Crisis Helpline Banner (including India's Tele-MANAS 14416, Vandrevala Foundation, and 988).

🌿 How to Use Mindora (Visual Representation)

Interface	Steps

	Step 1: Welcome & Preferences

	Step 2: Emotional Check-In

	Step 3: Share & Voice Companion

	Step 4: Summary & Relaxation

🌿 How to Use Mindora (Quick User Guide)

Step / Feature	What You Do	How Mindora Helps Your Feelings
Step 1: Welcome & Preferences	Select your language (English, Hindi, Hinglish) and mode (Voice + Text or Quiet Text), then click Begin Emotional Check-In.	Sets a safe, private, non-clinical space in the language and style you feel most comfortable with.
Step 2: Emotional Check-In	Tap through 6 skippable cards (Mood, Stress Frequency, Triggers, Sleep, Daily Impact, and Support Goal).	Helps you name your emotions and what's bothering you without forcing you to type a long paragraph.
Step 3: Share & Voice Companion	Type, click a starter chip, or tap the Mic (`🎙️`) to share what's on your mind, then click Wrap Up Session → when ready.	Gemma validates your feelings and suggests one small strategy at a time while ElevenLabs speaks it aloud in a calm voice.
Step 4: Summary & Relaxation	Read your Gentle Session Reflection, check off 1 Manageable Next Step, and optionally write in the Closing Journal Prompt.	Turns overwhelming stress into one tiny, realistic action item so you leave with a clearer mind.
Right Panel: 4-4-4 Calm Breathing	Click Start/Pause Breathing Exercise and follow the expanding circle (Inhale 4s → Hold 4s → Exhale 4s).	Physically slows your heart rate and grounds you at any moment during your session.
Top & Left Controls: Privacy & Safety	Click Clear Session to wipe your data instantly, or click Need Urgent Helpline Numbers? if in severe distress.	Gives you 1-click privacy control and immediate access to 24/7 human crisis helplines.

Code

AdibaNaushad / mindora_health

🌿 Mindora — AI Mental Wellness Companion

A comforting, voice-first AI reflection space powered by Google Gemma (via Ollama) and ElevenLabs.

✨ Overview

Mindora is a supportive AI mental-wellness companion designed to help people unpack everyday stress—such as academic pressure, career uncertainty, relationship friction, or low confidence—through a gentle, structured journey and comforting spoken voice conversation.

Instead of acting like a cold chatbot or making clinical diagnoses, Mindora guides users through a complete 4-step emotional grounding flow:

Welcome & Session Preferences (Language & Voice/Text mode)
Non-Diagnostic Emotional Check-In (6 skippable reflection cards)
Empathetic Voice Conversation (Powered by Gemma for reasoning + ElevenLabs for realistic human speech)
Session Closure & Grounding (Interactive 4-4-4 box breathing, 1 manageable next step, and a closing journal prompt)

🛡️ Ethical Boundaries & Safety Guardrails

Mindora is intentionally built as a mental-wellness companion, NOT a psychiatrist, therapist, or medical device.

No Fake Clinical Scores: Instead…

View on GitHub

(Or view the repository directly at: https://github.com/AdibaNaushad/mindora_health.git)

How I Built It

Mindora is built around a clean, modular full-stack architecture where every technology has one clear responsibility:

Layer	Technology Used	Role in Mindora
AI Brain (Open-Weight LLM)	Google Gemma (`gemma2:2b`) via Ollama	Runs locally to interpret the user's 6 check-in answers + chat history, validate emotions, and suggest 1 small, realistic action step.
AI Voice (Text-to-Speech)	ElevenLabs API (`eleven_flash_v2_5`)	Converts Gemma's written empathy into warm, human-like spoken audio with ~75ms ultra-low latency (with Web Speech API fallback).
Backend Coordinator	Python + FastAPI + HTTPX + Pydantic	Exposes `/session/check-in` and `/session/chat`, runs crisis keyword screening, formats Gemma system prompts, and keeps API keys secure.
Frontend Interface	React 18 + Vite + Tailwind CSS + Lucide	Modular component architecture (`WelcomeStep`, `CheckInStep`, `ChatStep`, `SummaryStep`, `SidebarRight` breathing orb).

How Gemma & ElevenLabs Work Together

Context-Rich Check-In (/session/check-in): When a user finishes the 6 check-in cards (e.g., Mood: Overwhelmed, Trigger: Studies & Exams, Sleep: Staying up too late, Goal: Help breaking down a problem), FastAPI packages these parameters into a structured context header for Gemma.
Empathetic Prompt Engineering (/session/chat): Gemma is guided by a strict system prompt:
- Never diagnose medical conditions or act like a doctor.
- Keep responses concise (2–4 warm sentences) so they sound natural when spoken aloud.
- Validate the user's feelings first, then ask only ONE gentle follow-up question or offer one tiny next step.
Instant Spoken Comfort: FastAPI sends Gemma's response text directly to the ElevenLabs TTS endpoint (eleven_flash_v2_5 with voice EXAVITQu4vr4xnSDxMaL), base64-encodes the audio stream, and returns both text and audio to React so the Voice Orb speaks immediately.

Why Does Open Innovation Matter?

1. Having Someone to Listen When No One Else Is Around

Mental health struggles rarely follow a convenient 9-to-5 schedule. The heaviest emotional friction—exam panic, deadline burnout, relationship heartache, or the quiet ache of loneliness—almost always arrives at 2:00 AM when everyone else is asleep and there is nobody to call.

In those moments, staring at a traditional, silent AI chatbot feels transactional and clinical. It feels like talking into an empty void.

By combining Google Gemma's empathetic reasoning with ElevenLabs' natural voice synthesis, Mindora shifts the entire dynamic. It transforms a cold text window into a presence. When someone is spiraling, hearing a calm, soft voice acknowledge their pain ("I hear how exhausted you are right now, and it makes complete sense that this feels heavy") activates our natural auditory grounding system. It no longer feels like a synthetic chat; it feels like having a compassionate, non-judgmental companion who is genuinely listening, understanding, and gently speaking back. That auditory warmth breaks the paralysis of isolation when someone needs it most.

When building a mental-wellness application, trust and privacy are everything. Users will not honestly share their academic fears, family conflicts, or insecurities if they worry their most vulnerable thoughts are being logged to train a proprietary corporate model.

Open innovation—specifically Google's open-weight Gemma models running locally via Ollama—made three critical things possible that a closed API alone could not:

Local-First Emotional Privacy: The core reasoning engine (gemma2:2b) runs 100% locally on the user's or campus's own hardware with zero third-party data retention.
Zero-Cost Experimentation & Accessibility: Students and indie builders can run unlimited empathetic reflection sessions locally without worrying about per-token LLM billing spiking during exam season.
Predictable Safety Guardrails: By pairing an open-weight local model with a transparent Python FastAPI safety layer, we have deterministic control over crisis keyword interception and system instructions.

My First User

I tested Mindora with fellow university students dealing with late-night study pressure and project deadlines.

What stood out to them: They loved that Step 2 (the 6-card check-in) meant they didn't have to stare at a blank chat box wondering how to start explaining their stress—clicking "Overwhelmed", "Studies & Exams", and "Staying up too late" let Gemma immediately greet them with a spot-on reflection.
The Voice Difference: Hearing the response spoken aloud in a calm voice while watching the 4-4-4 breathing orb on the right panel made the app feel like a grounding companion rather than another text-heavy productivity tool.

Prize Categories

Best Use of Gemma: Mindora uses Google's open-weight Gemma (gemma2:2b) model served locally via Ollama as its core empathetic reasoning engine—powering both the personalized check-in reflection (/session/check-in) and context-aware, non-diagnostic wellness conversations (/session/chat).
Because it runs locally, users can safely share their heaviest mental health struggles without fear of privacy loss.
Best Use of ElevenLabs: Mindora gives our local Gemma wellness companion a warm, natural human voice using the ElevenLabs Text-to-Speech API (eleven_flash_v2_5), transforming written reflections into comforting spoken guidance complete with an interactive Voice Orb and Play/Pause/Replay controls.
This is the core of how Mindora helps with loneliness—instead of reading a cold chat window at 2:00 AM, users hear a soothing, empathetic voice that provides immediate auditory grounding and makes them feel truly understood when no one else is around.

Turn your neighborhood into a daily adventures map with a cozy AI companion 🌸

ABIBA NAUSHAD — Sat, 10 Oct 2026 19:21:01 +0000

Meet Outside.exe: an offline-first app that intercepts your screen time, uses Gemma to generate a custom real-world quest, and rewards you with a happy little pet.

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

What I Built

Every productivity and habit-tracking app I’ve tried feels like a spreadsheet yelling at me. I didn't want another hustle-culture tracker. I wanted a gentle nudge to look away from my screen.

Outside.exe is a cozy, pastel-themed "cottage garden" web app designed to intercept doomscrolling and replace it with tiny, AI-generated real-world "Side Quests."

Instead of an overwhelming dashboard, the app opens with a frosted-glass blur. It asks you to check in with yourself: How are you feeling? What’s your energy level? How much time do you have?

You click "✨ I'm bored — give me a quest," and the AI writes a custom, hyper-specific micro-adventure just for you, like:

"Find a colorful flower and admire it."
"Notice three things moving in the wind."
"Give a passing cloud a silly name."

Once the quest generates, the blur fades away to reveal your garden. As you complete steps, you can use the inline camera feature to snap a tiny Polaroid-style photo memory for each task directly inside the row. Earning XP levels up your companion—a waddling cat named Mochi (or a dog named Bean) who lives in your garden and does a happy little jump when you check off a task. It doesn't require GPS or a map. It gives you a prompt, lets you snap a photo, and gets out of your way.

Demo

🌿 Try it out:

Code

💻 Check out the repo: https://github.com/AdibaNaushad/outside.exe.git

AdibaNaushad / outside.exe

Outside.exe 🌸

Replacing doomscrolling with IRL side quests using local Gemma 🐾
Turn your neighborhood into a cozy RPG where every real-world flower found levels up your garden companion.

🌿 Overview

Most mobile and web apps are built around retention loops, push notifications, and endless feeds designed to keep eyes on screens. Outside.exe flips the game loop upside down: it uses open-weight AI to turn the physical world into an offline questing ground.

Instead of an overwhelming dashboard, the app opens with a frosted-glass check-in. Tell it your mood, energy, and available time, and Google's Gemma 2B writes a short set of real-world activities. Complete tasks, log photo memories directly on each task row, and watch your companion celebrate your offline breaks.

🗺️ How It Works (Adventure Flow)


┌──────────────────────┐      ┌─────────────────────────┐      ┌───────────────────────────┐
│   1. The Check-in    │      │    2. Local Inference   │      │    3. The Garden Hub      │
│  • Mood:

…

View on GitHub

How I Built It

Outside.exe is a full-stack application relying on a lightweight local AI.

Frontend: Built with React and Tailwind CSS. It uses LocalStorage to persist your XP and garden state. I used modern CSS tricks (like mix-blend-mode: multiply) to make JPG sprites blend seamlessly into the garden background, and custom @keyframes to make the pets waddle and jump without needing complex animation libraries.
Backend: A Python FastAPI server receives the user's mood and time parameters and constructs a strict prompt.
AI: The backend calls Google's Gemma 2B (gemma:2b) running locally via Ollama. I enforced a strict JSON output structure so the React frontend can perfectly parse the title, description, steps, and XP rewards.

Real Session SampleHere is a live generation recorded during local

📸 App Experience & Flow

App Experience	App Flow

	Step 1: Selected Mood

	Step 2: Active Side Quests

	Step 3: Generated Quest By AI

	Step 4: Choose Your Buddy

	Step 5: Companion Reaction

	Step 6: Garden Companions

	Step 7: More Adventures & Evidence Collected

🛠️ System Architecture

Stage	Component	Technology	Role
User Intake	Blurred Onboarding Modal	React, CSS `backdrop-filter`	Blocks the dashboard to isolate intention and collect time/energy states.
Inference Engine	Local Gemma Pipeline	`gemma:2b`, Ollama runtime	Synthesizes physical constraints into real-world objectives in structured JSON.
API Transport	Microservice Bridge	FastAPI, Pydantic, CORS	Ingests client preferences, manages fallback schemas, and validates model output.
Feedback Loop	Animated Blossom Screen	Tailwind CSS (`animate-bounce`)	Clean, zero-dependency transition state while the model streams output.
Dashboard	Dynamic Quest Board	React State, CSS Flexbox	Renders actionable quest milestones, photo buttons, and XP progress bars.
Evidence Capture	Inline Proof Log	HTML5 Media Capture API	Captures live camera evidence per step (`e.stopPropagation()` isolated).
Companion State	Garden Mascot	CSS Transforms & Keyframes	Tracks pet level and executes celebratory animations on completion.

🧪 Sample Generated Session

Parameter	Recorded Value
Selected Mood	Bored
Physical Energy	Low (Keep it easy)
Allocated Time	10 minutes
Generated Quest	A Little Pond Pause
Generated Steps	1. Find a safe, peaceful spot. 2. Notice three things moving. 3. Take five slow, comfy breaths.
Evidence Collected	Inline photo verification captured per step via device camera.
Companion Reaction	Mochi jumps (+25 XP added to level progression).

Things that broke (and what I learned):

Checkbox Chaos: When I added the inline camera to each task, clicking the camera to take a photo accidentally checked off the task box simultaneously. I learned about event bubbling and used e.stopPropagation() on the camera button to separate the actions.
JSON Hallucinations: Early on, Gemma would sometimes write conversational text before the JSON (e.g., "Here is your quest! { ... }"), which crashed my app's parser. I had to enforce strict prompting and build a fallback try/except block in Python to serve a default "Cloud Watcher" quest if the AI's formatting failed.

Why Does Open Innovation Matter?

This app handles personal data: your mood, your energy levels, and photos of your immediate surroundings. Sending that data to a closed-source, monolithic API felt wrong for a project focused on mindfulness and safety.

By using open-weight models like Gemma 2B, the entire intelligence of the app can run locally. The fallback mechanisms, the prompts, and the data stay entirely under my control. Open innovation allows us to build software that serves the user's well-being, rather than serving the user's data to a third party. It also means the app has a $0 running cost!

Running Gemma 2B locally ensures:

Complete Privacy: Mood inputs and task logs remain entirely on the local device runtime.

Zero Ongoing Costs: The inference engine runs locally or on self-hosted instances without token consumption fees or rate limits.

Deterministic Control: Open model access allows developers to tune prompt contracts and system fallbacks without risk of upstream model deprecation.

Prize Categories

Best Use of Gemma: Gemma 2B acts as the creative engine, turning basic parameters (time, mood, energy) into structured, parseable, and emotionally intelligent real-world activities on the fly.gemma:2b powers the dynamic quest creation pipeline, translating mood, time, and physical energy into concise, structured real-world quests.

How I Built StudyBuddy: A Private, Local Gemma AI Companion to Help My Best Friend Overcome Burnout

ABIBA NAUSHAD — Sun, 04 Oct 2026 19:40:28 +0000

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

What I Built with gemma

My best friend, Laiba, is one of the smartest people I know, but she faces a massive hurdle every semester: studying is just plain boring to her. The moment she opens her computer science textbooks, her motivation flatlines. She gets easily overwhelmed by endless to-do lists, and traditional productivity apps only make her feel guilty when she falls behind. I wanted to build something that didn't just track her work, but actually loved her through the process.

So, I built StudyBuddy: a completely custom, hyper-gamified, kawaii-themed study dashboard designed specifically to make Laiba smile.

Instead of a cold checklist, every task she completes earns her XP and keeps a virtual Tamagotchi-style plant happy and bouncing. If she procrastinates for over 24 hours, the plant visually wilts and begs for water. But the absolute core of the project is the StudyBuddy AI—a local AI companion powered by Google's Gemma model that sits in the corner of her screen. Whenever she feels stressed or bored, she can rant to the AI, and it replies with hyper-supportive, sweet, and motivating micro-encouragements (always ending with a cute "♡").

When I finally showed it to her, she actually teared up. She said, "It feels like someone is holding my hand while I do my DSA problems." She hasn't broken her study streak since.

Demo

🌸 Live Demo:
https://youtu.be/aAaTiMc6UKQ

Code

AdibaNaushad / StudyBuddy

🌸 StudyBuddy — Study. Grow. Repeat.

A cute, gamified study companion that turns everyday studying into a small, rewarding experience. 🌱✨

StudyBuddy is a student-focused productivity web app designed to make studying feel less overwhelming and more engaging.

Instead of treating studying like a boring checklist, StudyBuddy turns completed tasks into XP, maintains a study streak, and keeps a virtual Tamagotchi-style plant growing alongside you.

At the heart of the experience is StudyBuddy AI, a local AI companion powered by Google's Gemma model that can provide motivation, study suggestions, and simple daily insights.

✨ Features

📚 Task Management

Add daily study tasks
Mark tasks as completed
Assign XP rewards to tasks
Track your daily progress
Organize tasks by subjects such as DSA, Python, DBMS, and TOC

🌱 Gamified Progress

Earn XP by completing study tasks
Track your study percentage
Maintain daily study streaks
Level up as you…

View on GitHub

💻 GitHub Repository:

https://github.com/AdibaNaushad/StudyBuddy.git

How I Built It

I built StudyBuddy as a full-stack web application with a heavy focus on local AI inference.

Frontend: React and Tailwind CSS, heavily customized with SVG assets, dynamic CSS filters (for the plant's mood states), and localStorage to persist Laiba's daily login streaks.

Backend: A lightweight Python Flask server that acts as the bridge to the AI.

The AI Core: I utilized Gemma 2B, Google's open-weight model, running entirely locally on my machine via Ollama. I configured the Flask backend to ping the local Ollama API (localhost:11434) using the requests library.

To make the experience feel snappy and human-like for Laiba, I implemented a keep_alive parameter in the API payload so the Gemma model stays loaded in system memory during her study sessions, eliminating cold-start delays when she needs instant motivation. I also set up a /quote route that asks Gemma to generate a fresh, unique motivational quote every single time she opens the dashboard.
The application is organized around a simple study loop:

Student → Study Tasks → XP → Progress → Plant → AI Companion
the study dashboard, task completion,
XP, streaks, plant state, and AI interaction.

For the AI component, I used Google's open-weight Gemma model
as the StudyBuddy AI companion.

Why Does Open Innovation Matter?

For StudyBuddy, open innovation wasn't just a technical choice; it was the only way this project could exist for Laiba.

Radical Privacy for Vulnerability: When Laiba is stressed out about a failed exam or venting about burnout, she needs to feel safe. Because Gemma 2B runs 100% locally on her own hardware, her personal anxieties, study habits, and chat logs never leave her laptop. There is no corporate cloud reading her data.

Zero Cost for Broke Students: As college students, we cannot afford to pay monthly subscriptions or token-usage fees for API calls. Open-weight models like Gemma mean Laiba can talk to her StudyBuddy all night during finals week without me worrying about waking up to a $50 API bill.

Offline Reliability: Campus Wi-Fi is notoriously terrible. Because this is a local inference stack, Laiba can take her laptop to a quiet park with absolutely no internet connection and her AI companion will still be there to encourage her. Open AI models turn powerful technology into a local utility.
For StudyBuddy, using an open-weight model matters because
the AI companion is meant to become part of a student's
personal study environment.

Using Gemma gives the project more flexibility over how the
AI is run and integrated. The model can be experimented with,
adapted, and served in different environments instead of
making the entire application dependent on a single closed
AI API.

This is especially useful for a study companion because
students may want greater control over their data and the
ability to experiment with the AI locally.

Open models also make it easier for developers to understand,
modify, and build on top of the technology behind their
applications.

My Agent Session

I didn't use DevRelay, but you can read my entire chat session with Gemini where we built the local Gemma integration together right here:
[View my Gemini Chat Session]https://share.gemini.google/BQf8bSC29Vbd

Prize Categories

Best Use of Gemma: I am running Google's open-weight gemma:2b model locally via Ollama to power both the interactive chat companion and the dynamic daily quote generation, serving as the motivational heart of the application.

Best Use of Gemma

StudyBuddy uses Google's open-weight Gemma model as the AI
companion behind the study experience.

Gemma is used to provide study-focused motivation,
suggestions, and progress insights rather than functioning
as a generic chatbot.

best platforms for beginners to get into open source❤️

ABIBA NAUSHAD — Fri, 02 Oct 2026 15:49:21 +0000

I genuinely love Hacktoberfest! ❤️
I feel like it’s one of the best platforms for beginners to get into open source. You get to learn by actually contributing, explore real projects, work with other developers, and slowly build the confidence to contribute more.

What I love most is that you don’t have to be an expert to start. You can learn along the way, make mistakes, ask questions, and keep improving. 🌱

Hacktoberfest makes open source feel much less scary and a lot more exciting! 🚀💻

Really excited to keep learning, contributing, and being a part of this journey! ✨