LazyFlow AI: Turning Messy Messages into Actionable Tasks with Gemma 3

说明与用途

LazyFlow AI 使用本地 Gemma 3 4B 模型通过 Ollama 自动从长消息中提取任务、截止日期和优先级，用户可编辑、保存并跟踪任务完成情况，无需外部 AI 服务。

问题

Parents and others often receive long, unstructured messages (e.g., school announcements, workplace emails) that contain multiple reminders, deadlines, and activities, making it hard to identify and organize actionable tasks.

结果

LazyFlow AI reduces manual effort in extracting tasks from lengthy messages by automating task identification, deadline and priority extraction, and task grouping, while enabling editing, completion tracking, and collection saving—all locally without external AI services.

工作流

• Paste message into the web app
• Frontend sends text to backend via HTTP
• Backend forwards prompt + message to Ollama API
• Ollama runs Gemma 3 4B locally and returns structured JSON
• Backend validates JSON and returns tasks to frontend
• Frontend displays tasks in dashboard
• User edits, deletes, or marks tasks as completed
• User can group tasks, select subsets, and save into named collections
• Collections and tasks are persisted in browser localStorage
• User tracks completion progress per collection

证据

LazyFlow AI is a simple, full‑stack AI‑powered task management application that transforms long, messy, or unstructured messages into clear, actionable tasks using Google's open‑weight Gemma 3 4B model running locally through Ollama.

来源

When a user pastes a message and clicks Generate Tasks, the React frontend sends the text to the Express backend.

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The backend validates the response before returning the extracted tasks to the frontend.

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For this MVP, I used localStorage to persist tasks and collections across browser refreshes without introducing a database or authentication system.

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