Slothlist: a gentle task planner powered by an open-weight model

说明与用途

Slothlist 是一个低能耗任务规划器，利用开放权重模型 Qwen2.5 7B Instruct 通过 Backboard API，将混乱的任务清单转化为轻量级的第一步，并保持凭证本地化。

问题

When a to‑do list feels heavy, deciding what to do next can be overwhelming.

结果

Slothlist demonstrates how an open‑weight model can power a low‑effort task planner while keeping credentials local; it requires an internet connection and a Backboard API key.

工作流

• 1. User enters task text, energy level, and time budget into the browser UI.
• 2. Browser sends this data to a local Python server.
• 3. Server builds a payload and calls Backboard’s Threads Messages API via OpenRouter (default Qwen2.5 7B Instruct).
• 4. Backboard returns a structured JSON plan.
• 5. Browser displays the plan to the user.
• 6. Optional memory preference can be toggled to store planning preferences in Backboard threads.

证据

Slothlist turns a messy task dump into one gentle first step, a few small moves, and permission to let the rest wait.

来源

The browser sends the task text, energy level, time budget, and optional memory preference to a Python server running on your own machine.

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The server calls Backboard's Threads Messages API and asks for a structured JSON plan. The default route is OpenRouter with Qwen2.5 7B Instruct.

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The Backboard API key stays in a local, Git-ignored .env file and is read by the server. It is never placed in the browser JavaScript.

来源

The default model route is OpenRouter's Qwen2.5 7B Instruct, an open-weight model.

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