I Built SlothFlow AI: A Local-First AI Assistant That Turns Brain Dumps Into Action Plans 🦥

说明与用途

SlothFlow AI 使用本地 LLM 或规则引擎将非结构化脑雾转化为可执行任务，局部存储并提供任务审查、跟踪和每日重置的工作流程。

问题

Users struggle to decide where to start when overwhelmed with tasks and brain dumps.

结果

SlothFlow AI reduces manual organization effort by converting brain dumps into structured task plans locally, enabling users to focus on execution rather than planning.

工作流

• 1. User writes a brain dump in plain language.
• 2. SlothFlow AI processes the input using a local LLM (Ollama) or a rules‑based fallback.
• 3. AI outputs a draft task list with priorities and time estimates.
• 4. User reviews and saves selected tasks to the workspace.
• 5. Tasks are tracked, completed, or deleted in the local SQLite database.
• 6. Daily Reset view summarizes unfinished work and estimated time.
• 7. User can add new tasks manually or adjust existing ones.

证据

I wanted to build something that could help with that problem, so for the Hacktoberfest DEV Challenge Week 3, I built SlothFlow AI — a local-first productivity assistant designed to turn overwhelming brain dumps into manageable action plans.

来源

When a compatible local model is available, SlothFlow uses it to generate a more contextual task plan. If Ollama isn't available, a built-in rules-based fallback can still produce a basic checklist.

来源

The project uses a lightweight stack: Python — application logic, FastAPI — REST API and backend, Ollama — optional local LLM inference, SQLite — local task persistence, HTML, CSS and JavaScript — responsive frontend, Pytest — automated testing.

来源