Unison: Zero-Distraction Offline Notification Engine

说明与用途

Unison 是一个本地通知聚合器，去除噪音，使用向量嵌入去重，并将消息摘要为一句话行动项，消除用户频繁切换应用的需求。

问题

Users suffer notification fatigue and context‑switching across multiple messaging platforms, emails, and system alerts, leading to cognitive overload and missed critical information.

结果

Unison reduced a full day of hundreds of notifications to 8‑10 actionable items, saving roughly an hour of active reading and eliminating anxiety from unread badges.

工作流

• 1. Passively ingest alerts from WhatsApp, Android SMS, emails, and system feeds.
• 2. Generate text embeddings with FastEmbed (BAAI/bge-small-en-v1.5) and store them in LanceDB.
• 3. Semantically deduplicate overlapping alerts via vector similarity.
• 4. Send deduplicated context blocks to Qwen2.5‑3B‑Instruct served locally via Ollama.
• 5. Use Instructor and Pydantic v2 to enforce JSON schema output (priority and one‑sentence action item).
• 6. Render processed data into a unified single‑page dashboard with Streamlit.
• 7. User checks dashboard twice a day, receiving 8‑10 concise action items instead of hundreds of raw notifications.

证据

Unison is a 100% local, air‑gapped engine that intercepts SMS, WhatsApp, emails, and system alerts, stripping out the noise to build a single consolidated dashboard.

来源

The core intelligence runs on Qwen2.5-3B-Instruct served locally via Ollama.

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To prevent the LLM from being overwhelmed by rapid‑fire pings, the ingestion pipeline routes raw notifications through FastEmbed (using the BAAI/bge-small-en-v1.5 model) to generate text embeddings.

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The entire processed dataset is then rendered into a unified, single‑page dashboard using Streamlit, avoiding the bloat of a complex web framework while keeping the interface functional and clean.

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It successfully condensed a full day of digital chaos into 8 to 10 actual, single‑sentence action items.

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