Hi Proton Team,

I'm a long-time Proton user who relies heavily on GrapheneOS + Lumo as part of a de-Googled mobile workflow. I love that Lumo 2.0 now has private web search and persistent memory — these features make it an incredibly powerful tool for privacy-conscious professionals.

That said, there's one gap between what Lumo offers today and what would make it a true competitor to mainstream AI assistants with automation capabilities: the inability to set up proactive, scheduled monitoring of topics or changes.

Currently, if I want to track whether something new has been published about a specific topic (e.g., "Motorola GrapheneOS partnership"), I have to manually initiate a web search each time. This is inefficient and easy to forget. Other AI platforms offer background task scheduling or keyword monitoring, but often at the cost of privacy (they track your interests server-side).

Proposed Solution
Enable user-defined scheduled monitoring tasks within the Lumo client (mobile/web app). The concept would work entirely client-side:

Create a Watchlist Entry — user specifies:

Query/topic to monitor (e.g., "GrapheneOS Motorola news")
Frequency (hourly, daily, weekly, custom cron-like interval)
Comparison criteria (new results only, significant change detected, etc.)
Alert method (in-app notification, email via Proton Mail)
Client-Side Execution — the scheduling happens locally on the user's device. Lumo periodically performs web_search autonomously, compares results with baseline state stored locally, and surfaces differences.

Privacy Preservation — no server-side logging of watchlists or tracking history. Everything encrypted end-to-end where applicable, with zero-access architecture maintained. Optional opt-in for syncing watchlists across devices via encrypted storage.

Integration with Existing Features — leverage Lumo 2.0's Persistent Memory to remember previous search baselines. Leverage Private Web Search to avoid external data leaks during queries.

Use Cases
Journalists/Researchers: Track breaking developments on stories of interest without revealing query patterns.
Enterprise IT Security: Monitor threat intelligence feeds or vulnerability disclosures relevant to organization infrastructure.
Legal/Compliance Teams: Watch regulatory updates on specific topics without internal logs revealing sensitive priorities.
Consumers: Track product releases, policy changes, or service updates privately (like the Motorola-GrapheneOS-Proton scenario we've been discussing).

Implementation Considerations (Technical Notes)
Since Proton already supports zero-access encryption, client-side processing, and encrypted sync across devices, most infrastructure pieces may already exist:

Scheduling: Could run as background task using native OS scheduler (not Lumo server polling)
Baseline Storage: Local SQLite/IndexedDB with encrypted snapshot of last-known-good state
Comparison Logic: Simple diff algorithm comparing search result hashes or keywords
Notifications: Push notifications via standard platform channels (no server involvement beyond delivery trigger)
Cross-device Sync: Optional, encrypted via Proton's existing key management system
Key constraint: Do NOT store watchlist definitions on Proton servers unless explicitly encrypted by client (E2EE), and ensure no metadata leakage occurs.

Why Now?
With Lumo 2.0 launching on June 30, 2026, featuring image generation, persistent memory, and private web search, the foundation is essentially ready. Adding scheduled monitoring would naturally extend Lumo's unique value proposition as the only AI assistant that doesn't require compromising privacy for automation.

This feature would differentiate Lumo dramatically from ChatGPT, Claude, Gemini, and Copilot — all of which either track user activity server-side or lack sophisticated background task capabilities.