PacePeep - Your AI-Powered Outdoor Mission Companion

Aditi Rai — Sat, 10 Oct 2026 17:44:26 +0000

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

What I Built

PacePeep is an AI-powered outdoor mission companion that helps people spend less time on screens and more time exploring the world. Users choose an activity, available time, mood and company preference to receive a personalized outdoor mission. It includes an interactive mission timer and optional voice guidance.

Demo

VN20261010_230120.mp4 - Google Drive

drive.google.com

Code

whoaditi / PacePeep

🌿 PacePeep — Less Screen. More World.

PacePeep is an AI-powered outdoor mission companion that turns your free time into personalized outdoor experiences. Choose an activity, set your available time, select your mood and get a mission designed to help you step away from your screen and reconnect with the world around you.

📸 Preview

Home Page

Personalized Outdoor Mission

✨ Features

Personalized outdoor missions: Choose walking, running, nature exploration or activities with friends.
Local AI personalization: Uses Gemma 3 1B through Ollama to generate mission titles, descriptions, observation prompts and reflections.
Flexible durations: Create missions for 15, 30, 45, or 60 minutes.
Mission timer: Track activities with pause, resume, skip and completion controls.
Voice guidance: Uses the ElevenLabs Text-to-Speech API to read the mission introduction aloud.
Reliable fallback: A predefined response keeps mission generation available when the local AI is unavailable.
Privacy-conscious design: Mission personalization runs locally. Optional voice…

View on GitHub

How I Built It

I built PacePeep using React and Vite for the frontend and Python with FastAPI for the backend. A deterministic mission engine selects activities and controls their durations, while Gemma 3 1B runs locally through Ollama to personalize mission titles, descriptions, observation prompts and reflection questions. I integrated ElevenLabs Text-to-Speech for spoken mission introductions and implemented fallback responses so the app remains usable when the local model is unavailable.

Why Does Open Innovation Matter?

Using an open-weight model such as Gemma lets PacePeep personalize outdoor experiences without depending on a hosted language-model API. Running inference locally gives developers more control over execution and helps keep mission personalization on the user's machine. PacePeep combines local AI with predictable application logic to turn AI assistance into a reason to leave the screen behind.

Prize Categories

Best Use of Gemma ($200): Uses Gemma 3 1B locally through Ollama to personalize outdoor missions.
Best Use of ElevenLabs ($100): Integrates ElevenLabs Text-to-Speech for spoken mission introductions.

MemoMate: I Built My Friend a second brain that remember his things

Aditi Rai — Sun, 04 Oct 2026 14:10:42 +0000

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

What I Built

I built MemoMate, a local-first AI memory assistant that helps you remember the small things you normally forget.

The idea came from a simple problem: my friend often has tasks, ideas and information scattered throughout the day but doesn't always want to maintain a complicated notes system.
With MemoMate, you can simply say:

“I have to finish my DBMS assignment tonight. I'm also thinking about using a recommendation system for our college project. Priya is working with me on the project, and tomorrow I need to send Rahul the project files.”

MemoMate extracts the important information and stores it as memories.
Later, you can ask:

“What do I need to do tomorrow?”
“What idea did I have for the college project?”
“Who is working with me on the project?” and MemoMate retrieves the relevant memory and answers.

Demo

The demo shows the complete flow: capture a memory → store it → ask a question → retrieve the relevant information.
Watch the demo video here

Code

GitHub:

whoaditi / memomate

A local AI memory assistant that remembers tasks, ideas, people and personal context.

🧠 MemoMate

My friend talks. MemoMate remembers.

MemoMate is a local AI-powered personal memory assistant built for a real friend who wanted a simple way to keep track of things they say, think about, and need to remember.

Instead of manually organizing everything into separate notes, to-do lists and contacts, MemoMate lets the user tell naturally. It extracts useful information, stores it as structured memories, and lets the user ask questions about those memories later.

📝 Tasks • 💡 Ideas • 👥 People • 🧠 Personal Context

✨ Features

📝 Task Memory — remembers things you need to do
💡 Idea Memory — captures project ideas and thoughts
👥 People Memory — remembers people mentioned in conversations
📌 Notes — stores useful personal context
🔎 Semantic Retrieval — finds relevant memories using embeddings
💬 Natural Language Questions — ask questions about your memories
🤖 Local AI — uses an…

View on GitHub

How I Built It

MemoMate is built as a local application using:

Python
FastAPI
SQLite
React
Sentence Transformers
Ollama
Gemma 3:1B

When a memory is added, Gemma extracts explicitly stated information such as tasks, ideas, and people. The information is stored locally along with embeddings.

When a question is asked, MemoMate retrieves relevant memories using semantic similarity + keyword matching, then gives that context to Gemma to generate the answer.

I also added safeguards to prevent the model from inventing information that isn't present in the stored memories.
So MemoMate is deliberately instructed to:

Use only stored memories
Never invent facts
Never infer relationships
Avoid turning a task into an idea
Return an explicit “I don't know” when the available memories are insufficient

Why Does Open Innovation Matter?

MemoMate handles personal information, so I wanted its core AI workflow to run locally instead of requiring a cloud AI API.
I'm using Gemma 3:1B locally through Ollama, which means the application can work without sending every memory to a remote AI service.
For me, this is the value of open AI: a model that can run on an ordinary laptop can become the foundation of a useful, privacy-conscious application without requiring a paid API or a powerful server.

Built For a Friend

I didn't want to build another generic chatbot.

I wanted to build something that solves a small but real problem:
What if you didn't have to remember to write things down in order to remember them later?

That's the idea behind MemoMate!!

Prize Categories

Best Use of Gemma
MemoMate uses Gemma 3:1B locally through Ollama for memory extraction and question answering.

DEV Community: Aditi Rai

PacePeep - Your AI-Powered Outdoor Mission Companion

What I Built

Demo

VN20261010_230120.mp4 - Google Drive

Code

whoaditi / PacePeep

🌿 PacePeep — Less Screen. More World.

📸 Preview

Home Page

Personalized Outdoor Mission

✨ Features

How I Built It

Why Does Open Innovation Matter?

Prize Categories

MemoMate: I Built My Friend a second brain that remember his things

What I Built

Demo

Code

whoaditi / memomate

A local AI memory assistant that remembers tasks, ideas, people and personal context.

🧠 MemoMate

✨ Features

How I Built It

Why Does Open Innovation Matter?

Built For a Friend

Prize Categories