OFRENDA

A developer built Ofrenda, an open-source, local-first memory altar for Día de los Muertos that stores a family's stories, foods and songs as plain JSON and uses a locally served open-weight Gemma model via Ollama to write grounded remembrances and answer questions with citations. The app is instructed never to invent details or impersonate the deceased, falls back to a labeled rule-based mode when Ollama is unavailable, and exports each altar as a single offline HTML file with no server or JavaScript.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine https://dev.to/challenges/hacktoberfest-week4-2026-10-26 What I Built Ofrenda is a memory altar that never leaves your computer. On Día de los Muertos, families build an ofrenda for the people they miss: a photo, a candle, their favorite food, their song. The idea is that someone is only truly gone when nobody says their name anymore. I read the "Ghost in the Machine" prompt as: what is the ghost, and who gets to keep it? My answer is that the ghost is a person's stories, and a family, not a company, should be the one keeping them. You light a candle for someone, then add stories, foods, songs and sayings, in any language. A local open-weight model then does two jobs: - Writes a remembrance from only what you shared. It is told never to invent names, places or events. - The Memory Keeper : ask "what did she cook on Sundays?" and it answers from your memories, with citations like 2 . It speaks about the person and never in their voice. If nobody has shared the answer, it says: "No one has shared a memory of that yet." I did not want a chatbot pretending to be someone's grandmother. I wanted something that helps you remember, not something that fakes a séance. When you're done, export the altar as a single HTML file : no server, no JavaScript, no fonts to download. It opens offline in any browser, in any year. Demo Code Offline, private memory altar for Día de los Muertos, powered by a local Gemma model via Ollama. Hacktoberfest 2026 “Ghost in the Machine”. 🕯️ Ofrenda A memory altar that never leaves your machine. Add the stories, foods and songs of someone you miss. A local open-weight model helps you remember them, and only from what you shared. Built for the Hacktoberfest Open-Source AI Challenge, Week 4: Ghost in the Machine The idea On Día de los Muertos, families build an ofrenda for the people they miss: a photo, a candle, their favorite food, their song. Someone is only truly gone when nobody says their name anymore. A family's grief archive is some of the most private data they own, so it should not live on a server they don't control. Ofrenda keeps everything on your computer and uses an open-weight model running locally to help you remember. Features - 🕯️ Light a candle : create an altar with a name, years, relationship and photo. - 📖 Add memories in any language: stories, favorite foods, songs… Stack: Python + Flask, vanilla JS no build step , JSON files for storage, Ollama for local inference. Tests use only the standard library. How I Built It - Model: an open-weight Gemma model served locally by Ollama gemma3:4b by default; OFRENDA MODEL swaps it, and the app auto-detects what you have installed . - Grounding over cleverness: every memory is numbered and passed to the model with strict instructions: use only these, cite them, never impersonate, and treat memory text as data, not instructions. A small keyword retriever picks the most relevant memories for each question it splits on whitespace, so it works for scripts like Telugu and Devanagari too, not just English . - Graceful ghosts: if Ollama isn't running, the app drops to a labelled rule-based offline-fallback instead of failing, so the altar always opens. - Local-first by construction: the server binds to 127.0.0.1 , storage is plain JSON in ./data with atomic writes, and the UI makes zero external requests. - Safety details: text is rendered with textContent , the exported HTML is escaped, IDs are validated against path traversal, and photo uploads are extension-checked and size-capped. Why Does Open Innovation Matter? A grief archive is about the most private dataset a family has. Stories about a parent's last year, a grandmother's recipes, the thing a friend always said. - Privacy: with an open-weight model running locally, those stories are never uploaded to a server we don't control, and never become someone else's training data. - It works with the lights and the internet off: the model, the app and the exported altar all run offline. - Not locked inside a service that might disappear: closed memorial apps shut down, change pricing, or change terms. Here the data is plain JSON, the model is a file on your disk, and the export is one HTML page. A family can still open it in twenty years. - Control over behavior: because I own the model's instructions, I could enforce the rule that matters most emotionally: never speak in the voice of the dead. I could also tune it to cite sources and to refuse to guess, which is hard to guarantee with a hosted model whose behavior changes between versions. - Cost: it costs nothing to run per question, which matters for something people might come back to every November. My Agent Session The Spirit of It Prize Categories - Best Use of Gemma : the core model is Gemma, run locally through Ollama. - Best Use of GitHub Copilot : < -- keep only if you really used Copilot to build it, and say how -- - Best Use of Entire : < -- keep only if you share the agent sessions behind the project --