# OFRENDA > Source: > Published: 2026-10-28 21:22:34+00:00 *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** : - **Best Use of Entire** :