This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.
What I Built
I built Heirloom, a voice-first family recipe and memory archive designed for someone close to me.
The idea came from a simple problem: some of the most meaningful family recipes aren't written down. They're passed from one person to another through stories, conversations, and instructions like:
"Add a little more until it looks right."
That works when you're standing in the kitchen with someone who has made the dish a hundred times.
It doesn't work when you want to preserve that recipe for the next generation.
Heirloom turns those informal recipe memories into structured, reusable recipes while preserving the story and context behind them.
Instead of forcing someone to manually type ingredients, measurements, and instructions, they can describe the recipe naturally using their voice. AI processes that conversation and extracts the useful information into a structured recipe.
The result isn't just a recipe database.
It's a way of preserving the people, stories, and traditions attached to food.
Demo
Live Demo: https://heirloom-9trv.onrender.com
Code
GitHub: https://github.com/DaKaufeeBoii/Heirloom
How I Built It
Heirloom combines voice AI, an open-weight language model, persistent storage, observability, and a web application into a single pipeline.
The core flow looks like:
Voice → transcription → Gemma → structured recipe → MongoDB Atlas → searchable family archive
Gemma
The intelligence behind the recipe extraction is Gemma, Google's open-weight model.
I use Gemma to understand unstructured descriptions and extract information such as:
- Ingredients and quantities
- Preparation instructions
- Cooking steps
- Timing
- Serving information
- Important cooking details that might otherwise be lost
- The story and context associated with the recipe
Using an open-weight model was important because the application is fundamentally about preserving personal memories. It gives me more control over how the AI is used and makes it possible to experiment with different deployment and inference setups.
ElevenLabs
ElevenLabs powers the voice side of the experience, allowing Heirloom to work naturally with spoken memories rather than requiring everything to be typed.
This makes the application feel much closer to the way family recipes are actually passed down: through conversation.
MongoDB Atlas
MongoDB Atlas is the application's data layer.
Recipes, memories, and their associated structured information are stored in Atlas so they can become a persistent personal archive rather than disappearing after a single AI interaction.
Render
The application is deployed on Render, which hosts the live application and makes Heirloom accessible through the public demo.
Sentry
I also integrated Sentry Agent Tracing to understand what the AI pipeline is actually doing.
This gives me visibility into the agent's execution, including where time is being spent and where failures occur, instead of treating the AI as a black box.
That was particularly useful while iterating on the recipe-processing pipeline.
Why Does Open Innovation Matter?
Heirloom deals with something personal: family memories.
That makes it important to have control over the AI layer.
Using an open-weight model like Gemma means the application isn't fundamentally locked into one proprietary model provider. I can experiment with different inference environments, prompts, model versions, and deployment strategies as the project evolves.
That matters because the long-term goal isn't just to generate recipes.
It is to build a system that can understand the way real people describe food — including incomplete measurements, regional terminology, family-specific shortcuts, stories, and instructions that don't look anything like a conventional recipe.
Open models make that experimentation possible.
They also make the project more adaptable: the model can potentially move closer to the user and their data rather than requiring every interaction to depend on a closed API.
For a project about preserving personal memories, that control is valuable.
My Agent Session
I built Heirloom using an agent-assisted development workflow and used Sentry tracing to inspect the AI workflow and understand how the application was behaving during development.
Prize Categories
Featured Categories — $200
Best Use of Render
Heirloom is deployed on Render and uses it to host the live application.
Best Use of Gemma
Gemma is the open-weight model at the core of Heirloom's AI recipe-understanding workflow.
Partner Categories — $100
Best Use of ElevenLabs
ElevenLabs powers the voice interaction/transcription component of Heirloom.
Best Use of MongoDB Atlas
MongoDB Atlas serves as Heirloom's persistent data layer for storing recipes and memories.
Best Use of Sentry Agent Tracing
Sentry is integrated into the AI workflow to trace agent execution and help inspect performance and failures.
Built for a Person, Not a Prompt
The easiest way to build an AI project is to start with the technology:
"What can this model do?"
I started with a different question:
"What is something someone I care about might actually lose?"
The answer was memories.
A recipe can be recreated.
A list of ingredients can be searched online.
But the way someone explains their recipe, the little rules they never write down, and the story behind why that dish matters are much harder to replace.
That's what Heirloom is trying to preserve.
Not just the recipe.
The person behind it.
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