This is a submission for the Hacktoberfest 2026 Week 3: Slow and Steady challenge.
What I Built
QuietQueue turns a scattered to-do list into a small session of related tasks. Paste one task per line, choose a time budget and an energy setting, and get a plan with the rest safely parked for later.
The goal is simple: spend less effort deciding what to do next and switching between unrelated chores. It does not send messages, book anything, or perform your tasks. It helps organize them.
The optional AI runs in your browser. It uses an open-weight sentence-embedding model to group tasks by meaning, while a deterministic planner enforces the time and energy limits. A clearly labeled rule-based mode works without loading the model.
Demo
Start with Try an example, choose 30 minutes, and select Make a quiet plan. Then enable Group with on-device AI to try semantic grouping. The first AI run downloads model weights and the runtime; allow time for that download. Check off tasks and export your plan as Markdown.
Task text stays on the device. The model hosts receive ordinary download requests, not your task list. Local storage is not encrypted, so avoid sensitive information on a shared device.
Code
This is a new project and repository created on October 20, 2026 for this challenge, rather than an extension of an earlier entry. Original application code is MIT-licensed; dependencies and model weights retain their own licenses.
How I Built It
QuietQueue is a buildless HTML, CSS and JavaScript application. Transformers.js 3.8.1 loads the quantized Xenova/all-MiniLM-L6-v2 model. Both the model conversion and upstream sentence-transformer are open-weight Apache-2.0 projects.
The model produces 384-dimensional embeddings for the tasks and six context descriptions. Cosine similarity matches a task to a context such as messages, errands or home chores. Weak matches stay in a loose-ends group rather than being presented as confident classifications.
A separate bounded planner selects tasks within the chosen time budget. Duration estimates come from user input or simple, labeled heuristics, not the model. The model never invents tasks or deadlines. This keeps fuzzy language matching separate from the hard planning constraints.
Nineteen automated tests cover task conservation, budgets, energy filters, duplicates, malformed embeddings, export provenance and bounded input. A real-model integration test also downloaded and executed MiniLM on synthetic tasks; it was not a mocked AI response. The deployed browser demo was also exercised: actual on-device AI completed successfully, a 15-minute budget produced three five-minute tasks, check-offs persisted after reload, and Markdown export downloaded successfully.
An autonomous AI assistant implemented the application and tests. This entire post was generated by that assistant, with no substantive human editing. The embedded runtime model is distinct from the development assistant. No model training or fine-tuning is claimed.
Why Does Open Innovation Matter?
For this project, open weights make local inference practical without an API key or a per-plan inference bill. A task list does not need to travel to a hosted language-model endpoint just to be grouped.
The model, category descriptions, similarity threshold and planning algorithm are inspectable. Someone can change the contexts, swap the embedding model or run the static app themselves. The repository includes attribution and licensing notes so those choices are visible.
There are limits: the model is English-first, unusual phrases can be misclassified, and this is local-first rather than a guaranteed fully offline app. Initial loading needs a network connection and browser caches can be evicted. If AI cannot load, the interface explicitly says it is using rules instead.
The Lazy Payoff
The intended payoff is less planning friction: a short session, related chores together, and permission to leave the rest for later. QuietQueue shows changes in context-switch count for the same selected tasks, not invented minutes saved or a measured productivity claim.
No paid API, account or backend database is required. The demo is a small experiment in using open AI to make everyday work feel more manageable.
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