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Promise Keeper: Never Forget What You Promised, Even in Hinglish

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend What I Built Promise Keeper is a small app that remembers what you promised people. I built it for my friend Amit, who makes a lot of promises in a lot of chats and has forgotten most of them by the end of the day. The problem isn't making promises. It's that they get buried in messages. A to-do app doesn't help,…

Promise Keeper is a small app designed to help users remember their promises made to others. Created by the developer for their friend Amit, who frequently makes promises and later forgets them, the app addresses the issue of promises getting lost in conversations. Instead of using a traditional to-do app, Promise Keeper integrates directly with the messages exchanged between users.

Users simply paste a message, which the app then analyzes to extract the promise, the person it's owed to, and the deadline. The app supports both English and Hinglish (Hindi written in English script) promises. To import a WhatsApp chat, users can export it and paste the contents. The app reads only the user's messages, ensuring that private conversations remain private.

Before saving any promise, users confirm the date and time, and the app provides a list of overdue promises, upcoming ones, and those without a deadline. Users can further categorize these promises by person or draft a nudge for overdue promises. The app, built for the Hacktoberfest Weekend Challenge, emphasizes local-first principles, meaning it operates on the user's own device without any cloud storage or API keys.

It requires Python 3.10 or later and Ollama, an open-source framework for running large language models locally. The core of the app is an open-weight model called Gemma 3, which runs through Ollama. The app was designed to be simple, fast, and secure, ensuring that user data never leaves their machine, even if the internet connection is turned off.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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