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Let’s Not Be Hangry is a small household food-decision app: choose how much time you have, and it suggests something already marked as fair game, where to find it, and how to eat it. It is not a meal planner, grocery service, or diet app. Creator Tanya Mott describes the project and its implementation in her October 4, 2026 DEV Community article.
What problem does Let’s Not Be Hangry solve?
Mott says she built the app while recovering from ankle surgery. Her boyfriend was helping care for her but sometimes forgot to eat while busy, and—because it was her kitchen—felt hesitant to help himself. The app’s “fair game” list addresses that household-specific barrier by explicitly listing food he can take without asking.
As Mott puts it, she built “a tiny app with one job: make eating faster and easier than skipping.” The app’s purpose is practical permission and a quick decision, not nutrition guidance: it selects from food already in the home rather than shopping for meals or prescribing what someone should eat.
How does the app work?
- Choose a time: Select one, five, or fifteen minutes.
- Get a suggestion: Gemma picks an item from the household’s fair-game list, tells the user where it is stored, and explains how to eat it. Mott says no-cook options are prioritized.
- Log the meal: The “I ate!” button is kept readily accessible. When an item is marked finished, it is added to a restock list.
For example, Mott describes a suggestion of string cheese in the fridge door, accompanied by the encouraging line, “She’d much rather you be fed than hungry, so go for it!” The app’s model instructions request a warm, brief, lightly funny tone and prohibit references to calories, diets, or guilt.
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What reminders and household tools are included?
- Meal-window reminders: If no meal has been logged by the end of the selected lunch or dinner window, a reminder appears. Mott says its wording is generated fresh each time.
- Restock list: Marking a listed item finished adds it to the list of items to replenish.
- Accessible meal logging: The “I ate!” action is kept easy to reach, so logging a meal does not require navigating through a long flow.
Does it use hosted AI or run locally?
Mott describes two ways to route requests to Gemma. In the hosted configuration, the Node/Express server forwards requests to Google AI Studio. In her local configuration, it uses Ollama for inference on her laptop. The author describes the latter as keeping inference on her laptop; that statement should not be extended to the hosted setup.
| Option | Where inference runs | What the article establishes |
|---|---|---|
| Google AI Studio | Hosted service | Requests are routed through the app’s server to Google AI Studio. No comparative latency, cost measurement, or detailed data-retention terms are provided in Mott’s article. |
| Ollama | Locally on Mott’s laptop | Mott says “nothing leaves the house” in this configuration. This is her description of local inference, not an independent privacy assessment. |
The source does not provide benchmark results or a model-by-model comparison, so it cannot establish which route is faster or cheaper. Its privacy description also does not include a detailed data-flow inventory, retention policy, threat model, or external security review.
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What is the app built with, and where is its data stored?
Mott describes an Angular 22 frontend and a Node/Express server. The server serves the app and forwards requests to Gemma at /api/chat. The fair-game list, restock list, and meal log are stored in the browser’s localStorage, according to her article.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesShe says the server keeps the API key out of the browser, applies a simple rate limit, and can recover from Google errors by asking which Gemma models her key can use and falling back to another available Gemma model. If a model cannot be reached, the app can instead use a built-in demo-mode suggestion. These are the creator’s descriptions of the implementation, not findings from an independent code or security audit.
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The article says the public demo is hosted on Render’s free plan and may take up to a minute to load after inactivity. That note applies to the demo described in the October 4, 2026 article, not necessarily to any later deployment.
What feedback has the app received?
Mott reports informal comments from her boyfriend, including that the app helped him decide what to eat within the time available and that the reminders were helpful. He also said it took a little time to figure out but then seemed self-explanatory. This is feedback from one user reported by the creator; it is not a measured study of eating habits, health effects, or broader usability.
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Who is this app for?
Its described use case is a shared household where someone has permission to take particular foods but may otherwise hesitate or forget to eat. The time-based suggestion flow may help turn a vague “I should eat” moment into a specific quick option. It depends on someone maintaining a fair-game list that reflects what is actually available and appropriate for that household; it does not establish a general meal plan or replace dietary advice.
Quick Recap
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