593 results found

1. Allow Renaming Chats

   Chats are titled automatically based on the first prompt, and cannot be renamed. A rename option feels like an essential feature to have. It would make it much easier to organize Lumo.

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2. Manual control of per-chat project knowledge

   As an alternative to Lumo chats ingesting project knowledge automatically from the files available to the project, it would be great if there was a "Manual" mode.

   The user could selectively add and remove which files are used, as the automatic features sometimes needs a few tries to get everything right. This is especially helpful to avoid the chat auto-ingesting metadata files from external project configurations.

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3. How Do You Approach Law Coursework?

   https://writemycoursework.co.uk/law may sometimes prove to be tough since it requires a lot of legal research, case studies, law, and referencing. I would like to know which aspects of LLB coursework help UK prove to be difficult for other law students. Is it sourcing information, applying legal principles, IRAC, or something else?

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4. Native Excel (.xlsx) editing and export support

   I'd love to see Lumo able to work directly with Excel files — not just reading them, but actually editing them and returning the result as a downloadable .xlsx file.

   My typical use case: I upload a spreadsheet and want Lumo to clean up data, recalculate columns, merge sheets, apply formulas, or reformat cells. Right now, the best I can get back is a markdown table or CSV text that I have to manually copy back into Excel — which is tedious and loses formatting, multiple sheets, and formulas.

   It would be a huge productivity boost if Lumo could:

   Read and analyze .xlsx files (including multiple sheets and formulas)
   Apply requested changes (editing, filtering, calculations, restructuring)
   Return the modified file as a proper .xlsx download — preserving original formatting where possible
   Since privacy is Proton's core promise, this would also be a great differentiator: processing sensitive spreadsheets locally in an encrypted environment instead of uploading them to Big Tech AI tools that train on user data.

   I'd love to see Lumo able to work directly with Excel files — not just reading them, but actually editing them and returning the result as a downloadable .xlsx file.

   My typical use case: I upload a spreadsheet and want Lumo to clean up data, recalculate columns, merge sheets, apply formulas, or reformat cells. Right now, the best I can get back is a markdown table or CSV text that I have to manually copy back into Excel — which is tedious and loses formatting, multiple sheets, and formulas.

   It would be a huge productivity boost if Lumo could:

   Read… more

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5. Title: Per-conversation memory control: "New Private Chat" option + memory containers

   Hi Proton Team,

   Memory shipped with Lumo v2.0, and it's a genuinely useful feature — but right now the only control is a global on/off toggle. I'd like to suggest finer-grained control, in two parts.

   Part 1. "New Private Chat"

   A conversation with no memory loaded in, and no memory saved out. Like a browser private window: fully functional, nothing retained afterwards. Your existing memory stays intact for all your other chats — this session simply doesn't participate.

   Visual indicators:

   In the sidebar: a small icon (lock, or similar) next to the conversation title — left side for LTR languages, right side for RTL
   In the active chat: a small badge on the existing header bar — no new persistent bar, so no screen space lost
   Icon paired with text, so it doesn't rely on colour alone (accessibility)

   Part 2. Memory "containers"

   Separate compartments (work, personal, etc.) that don't bleed into each other:

   Each container holds its own memory context
   A conversation only draws from its assigned container
   "Private chat" is effectively a container with no memory at all — nothing in, nothing out
   This serves two groups currently pulling in different directions:

   Users who want memory get organised, compartmentalised context instead of one blended pool
   Users who are wary of quiet accumulation get a deliberate, reviewable way to use memory — or none at all
   It would turn the current all-or-nothing toggle into genuine user control: stateless by default for those who prefer that, compartmentalised memory for those who don't.

   The browser-container concept is familiar, so it needs almost no explanation — and it aligns with Proton's privacy-first values: the user decides what's on the record and what isn't.

   Thanks for reading!

   Hi Proton Team,

   Memory shipped with Lumo v2.0, and it's a genuinely useful feature — but right now the only control is a global on/off toggle. I'd like to suggest finer-grained control, in two parts.

   Part 1. "New Private Chat"

   A conversation with no memory loaded in, and no memory saved out. Like a browser private window: fully functional, nothing retained afterwards. Your existing memory stays intact for all your other chats — this session simply doesn't participate.

   Visual indicators:

   In the sidebar: a small icon (lock, or similar) next to the conversation title — left side for LTR languages,… more

   2 votes

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6. Drag calendar event and reschedule by 15-minute increments instead of 30min

   Drag calendar events and reschedule by 15-minute increments instead of 30min

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7. Conversation tree to help navigate current chat

   In a single chat, it's common to ask follow up questions, or clarifications, and sometimes related but slightly different angles, and chats can become long and convoluted. I frequently scroll back in a chat looking for details in a previous answer. Navigation would be much easier if each chat showed a tree in the sidebar that lists each question I asked in the current chat. (This would also be quite useful if I come back to review the chat at a later date.)

   5 votes

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8. 🤖 Autonomous Project Builder with Local File System Access

   🤖 Autonomous Project Builder with Local File System Access

   Summary

   Transform Lumo from a code-writing assistant into an autonomous project builder that can read, write, organize, and execute code directly on user's local filesystem with proper security controls.

   Problem Statement

   Current AI coding assistants (including current Lumo) operate in a "chat-only" paradigm:
   - User: "Build a Flask API with authentication"
   - Lumo: Returns code blocks in chat
   - User: Manually copies files, creates directories, runs tests, configures environment
   - Result: Friction, human error, context drift, repetitive work

   Real engineering work requires:
   1. Creating directory structures
   2. Writing multiple files with correct paths
   3. Running commands/tests locally
   4. Debugging errors from actual execution
   5. Iterating based on real runtime behavior

   Without file system integration, Lumo cannot close the loop from "design" to "deployed working code."

   Proposed Solution

   Core Capability: Secure Local Workspace Access

   Mode 1: Read-Only Analysis (always enabled)
   - Analyze existing project structure
   - Identify dependencies, bugs, gaps
   - Suggest improvements with precise file locations

   Mode 2: Controlled Write Access (opt-in, sandboxed)

   User Permission Required For: ├── Creating new files/folders ├── Modifying existing files ├── Running shell commands (explicit allowlist) ├── Installing packages (npm/pip/cargo with review) └── Committing to Git (preview before commit)

   Mode 3: Autonomous Execution (advanced, explicit opt-in)
   - Lumo builds complete features end-to-end
   - Runs tests automatically
   - Fixes errors without waiting for approval (within defined scope)
   - Reports back: "Built X, ran Y tests, Z passed, here's the diff"

   Security Model (Privacy-First Design)

   Security_Guardrails:
     - Scope_Limit: Only access explicitly designated workspace folders
     - Command_Allowlist: Pre-approved commands (git, pytest, npm test, etc.)
     - Network_Isolation: No outbound calls without user approval
     - Audit_Log: Every action logged with diff preview before/after
     - Rollback: Atomic writes + version history (undo any change)
     - E2EE: All local data encrypted at rest
   User Interface Patterns
   
   Pattern A: Sidebar Integration
   [Lumo Chat] | [Project Explorer] | [Terminal Output]
   ────────────│─────────────────│──────────────────
   Code dialog │ Files Lumo touched│ Live command output
               │ (read-only view) │ (with error highlighting)
   
   Pattern B: Progress Tracking
   Building Adrenalina Prime Module:
   ✓ Created /src/core/metrics_collector.py
   ✓ Created /src/core/energy_engine.py
   ✓ Generated tests (87% coverage)
   ⏳ Running pytest...
   ✗ Fixing failure in test_crossbot_consensus.py
   ✓ All tests passing (92%)
   → Ready to deploy? [Yes/Review Changes]
   Example Workflow Comparison
   Traditional Chat    With Local File Access
   Paste code → copy to files    Lumo writes files directly
   Manual env setup    pip install -r requirements.txt (auto)
   Run tests manually  Tests auto-run after each change
   Find bug in console Lumo reads error, fixes code, re-tests
   Git commit manually Diff preview → one-click commit
   User Stories
   
       "As a solo developer, I want Lumo to scaffold my entire project structure so I don't waste hours on boilerplate."
   
       "As a senior engineer, I want Lumo to fix failing tests autonomously within my project folder so iteration speed is faster."
   
       "As a privacy-conscious user, I want to define exact folder boundaries Lumo can touch and see every change before it's applied."
   
       "As a team member, I want Lumo to review my git diff and explain why each file changed."
   
   Technical Requirements
   Backend Infrastructure
   
       Local agent process (runs on user machine, not cloud)
       File system watchers for real-time sync
       Sandbox container for untrusted operations
       SQLite/encrypted store for version history
   
   Integration Points
   Existing Lumo Features          New Requirements
   ────────────────────           ──────────────────
   Persistent Memory        →     Project-scoped workspace binding
   File Upload              →     Bidirectional file sync
   Custom Lumos             →     Role-based permission profiles
   Web Search               →     Package registry lookups (local)
   API Contract Example
   class LumoWorkspace:
       def list_files(self, path: str) -> List[str]:  # Read
       def read_file(self, path: str) -> str:         # Read
       def write_file(self, path: str, content: str) -> bool:  # Needs approval
       def run_command(self, cmd: str) -> CommandResult:       # Allowlist only
       def get_diff(self, path: str) -> str:                  # Preview
   Success Metrics
   Metric  Target
   Time-to-working-code (vs manual)    >70% reduction
   Session-to-deployment cycle time    <15 minutes for small features
   User trust (permission acceptance rate) >80% opt-in within 6 months
   Error resolution speed  3x faster than manual debugging
   Risks & Mitigations
   Risk    Severity    Mitigation
   Malicious prompt injection writing arbitrary files  🔴 Critical   Sandboxed execution; explicit approval for all writes
   Data exfiltration via file uploads  🔴 Critical   No outbound network calls without audit log + approval
   Accidental corruption of production code    🟡 High   Atomic writes + automatic backups + undo history
   Performance impact on large codebases   🟢 Low    Incremental indexing; lazy loading
   Competitive Landscape
   Platform    File Write  Auto-Test   Permission Model    Privacy
   Cursor  ✅ ⚠️ Partial  ⚠️ Basic    ❌ Cloud-first
   GitHub Copilot Workspace    ✅ ⚠️ Limited  ❌ None    ❌ Microsoft cloud
   Windsurf    ✅ ⚠️ Partial  ❌ No permissions  ⚠️ Hybrid
   Lumo (proposed) ✅ ✅ ✅ Granular    ✅ E2EE local
   
   Differentiation: No competitor offers privacy-preserving autonomous execution with granular user control. This would be Proton's unique value proposition.
   Priority
   
   Critical — This transforms Lumo from "chatbot" to "engineering platform"
   Effort
   
   High — Requires local agent infrastructure + security review + UX design
   Timeline Estimate
   
   6-8 sprints for MVP (read-only analysis → controlled writes → autonomous mode)
   Related Features
   
       Persistent Memory (needs project-scoped binding)
       Custom Lumos (can define permission profiles)
       File Analysis (upgrade to bidirectional sync)
       Proton Desktop App (could host local agent process)
   
   Engineer's Note
   
   I'm proposing this after building complex systems for 25+ years. The bottleneck isn't code generation anymore — it's closing the loop between idea and running system.
   
   Every time I have to:
   
       Copy code from chat to files
       Manually create directories
       Run commands I asked Lumo about
       Debug errors Lumo couldn't see
   
   That's 30-60 minutes wasted per feature. With secure local access, that becomes 5 minutes.
   
   Privacy isn't negotiable. Everything stays on-device. No cloud processing. End-to-end encryption. This is Proton's DNA.
   
   ---
   
   ## **Sekcje dodatkowe (opcjonalne):**
   
   **Dodatkowy komentarz przy zgłoszeniu:**
   
   This is not theoretical — I've prototyped local agent systems that can build and test code autonomously. The technology exists. What's missing is a privacy-preserving implementation that engineers trust.
   
   Key technical decisions already validated:
   
       Atomic writes prevent data corruption
       Sandboxed containers limit damage scope
       Version history enables rollback to any point
       Allowlist-based command execution prevents abuse
   
   The question isn't "can we build this?" It's "will Proton lead on privacy-preserving AI engineering tools?"
   
   **Tagi sugerowane:** `productivity`, `security`, `developer-tools`, `automation`
   

   🤖 Autonomous Project Builder with Local File System Access

   Summary

   Transform Lumo from a code-writing assistant into an autonomous project builder that can read, write, organize, and execute code directly on user's local filesystem with proper security controls.

   Problem Statement

   Current AI coding assistants (including current Lumo) operate in a "chat-only" paradigm:
   - User: "Build a Flask API with authentication"
   - Lumo: Returns code blocks in chat
   - User: Manually copies files, creates directories, runs tests, configures environment
   - Result: Friction, human error, context drift, repetitive work

   Real engineering work requires:
   1. Creating directory structures
   2. Writing multiple files with… more

   3 votes

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9. 🧠 Advanced Persistent Project Memory & Engineering Partner Mode

   🧠 Advanced Persistent Project Memory & Engineering Partner Mode

   Summary

   Enable Lumo to maintain long-term project context across sessions and operate as a proactive engineering partner rather than reactive assistant.

   Problem Statement

   Current conversation memory is session-bound. Each new chat requires re-establishing context about:
   - Project architecture decisions and trade-offs
   - Technology stack specifics (local models, frameworks, deployment targets)
   - Previous iterations and lessons learned
   - Ongoing development challenges

   This creates friction for complex, multi-session projects and forces users to constantly re-explain context.

   Proposed Solution

   Feature 1: Project-Specific Memory Stores

   Description: Allow users to create and manage named "project memory" objects that persist across separate conversations.

   Key Capabilities:
   - Store: Architecture diagrams, tech stack details, API contracts, codebase structure
   - Query: Natural language retrieval of project-specific knowledge
   - Update: Incremental additions as project evolves
   - Export/Import: Share project contexts between team members

   Technical Implementation:
   ```yaml
   ProjectMemory:
   - Name: "adrenalina-prime"
   - Created: timestamp
   - Tags: ["engineering", "agent-system", "local-llm"]
   - ContextBlocks:
   - Type: "architecture" | "api-contract" | "tech-stack" | "decision-log"
   - Content: markdown/text
   - Last_Modified: timestamp
   Feature 2: Engineering Partner Mode

   Description: Toggle switch for interaction style optimization.
   Standard Mode Engineering Partner Mode
   Reactive Q&A Proactive gap identification
   Generic responses Stack-aware suggestions (e.g., knows Ollama vs OpenAPI constraints)
   No state awareness Remembers previous session's blockers
   Neutral tone Direct, technical communication (zero sugar-coating)
   Feature 3: Contextual Code Awareness

   Description: When uploading code files, Lumo should build internal map of:

   File dependencies and imports
   Function signatures and interfaces
   Testing coverage and gaps
   Potential security vulnerabilities based on patterns
   

   Integration Points:

   Auto-detect language/framework from file extensions
   Cross-reference with known local model capabilities (e.g., deepseek-coder:6.7b context window limits)
   Suggest tests before implementation, not after
   

   User Stories

   "As a developer working on a multi-week project, I want to resume conversation without explaining what we built last week."
   
   "As an engineer, I want Lumo to notice architectural inconsistencies between my code and stated design goals."
   
   "As a team lead, I want to share project context with junior developers via exported memory bundles."
   

   Success Metrics
   Metric Target
   Reduction in context-setting time >60% decrease
   Multi-session project completion rate +40% increase
   User satisfaction (Engineering users) NPS +25 points
   Memory feature adoption >50% of Lumo Plus users within 6 months
   Risks & Mitigations
   Risk Mitigation
   Privacy concerns with persistent data All memory encrypted end-to-end; opt-in only
   Memory bloat over time Automatic summarization; user-controlled cleanup
   Inaccurate recall Version history with diff view; confidence scoring on retrieved context
   Competitive Differentiation

   Other AI assistants (Claude, ChatGPT, etc.) offer:

   ✅ Long context windows
   ❌ Project-scoped memory persistence
   ❌ Role-based interaction modes
   ❌ Architecture-aware code review
   

   Lumo's opportunity: Become the first privacy-focused AI with persistent project memory + professional mode optimization.

   Priority: High Effort: Medium (requires backend memory infrastructure + UI toggles) Timeline Estimate: 3-4 sprints for MVP

   Related Features:

   Custom Lumos (already exists, but lacks memory integration)
   Persistent Memory (exists, but not project-scoped)
   File Analysis (exists, but lacks cross-session code mapping)
   

   🧠 Advanced Persistent Project Memory & Engineering Partner Mode

   Summary

   Enable Lumo to maintain long-term project context across sessions and operate as a proactive engineering partner rather than reactive assistant.

   Problem Statement

   Current conversation memory is session-bound. Each new chat requires re-establishing context about:
   - Project architecture decisions and trade-offs
   - Technology stack specifics (local models, frameworks, deployment targets)
   - Previous iterations and lessons learned
   - Ongoing development challenges

   This creates friction for complex, multi-session projects and forces users to constantly re-explain context.

   Proposed Solution

   Feature 1: Project-Specific Memory Stores

   Description: Allow users to create and manage named "project memory"… more

   3 votes

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10. Preview Images in Project Knowledge Before Deleting – Show Thumbnails or Click-to-Open

    Currently, when viewing files in the "Project Knowledge" section, images are displayed only as generic icons with filenames (e.g., IMG_9630.png). There is no way to preview the actual image content before deciding to delete it. This creates a significant risk of accidentally deleting the wrong image, especially when managing multiple uploads with similar or non-descriptive filenames.

    I request a preview feature for images in the Project Knowledge panel with the following specifics:

    • Thumbnails: Display small thumbnail previews of the images instead of just generic icons so users can instantly identify the content.
    • Click-to-Open: Allow users to tap or click an image to open a larger preview window before confirming any action.
    • Confirmation Dialog: When clicking the delete (X) button on an image, show a confirmation popup where the image preview is clearly visible, asking "Are you sure you want to delete this specific image?"

    Why this matters: For users who upload many reference images (photos, diagrams, screenshots) to guide Lumo's responses, being able to visually verify content before deletion is critical. It prevents accidental data loss and improves confidence when managing project context. This is a basic usability standard seen in most file management systems (like Photos or Drive) and should apply here too.

    Currently, when viewing files in the "Project Knowledge" section, images are displayed only as generic icons with filenames (e.g., IMG_9630.png). There is no way to preview the actual image content before deciding to delete it. This creates a significant risk of accidentally deleting the wrong image, especially when managing multiple uploads with similar or non-descriptive filenames.

    I request a preview feature for images in the Project Knowledge panel with the following specifics:

    • Thumbnails: Display small thumbnail previews of the images instead of just generic icons so users can instantly identify the content.
    • Click-to-Open: Allow users to tap or click an… more
    2 votes

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11. Lumo needs access to the current date and time

    Having access to the exact current date and time is important for certain tasks. Lumo is able to retrieve the date from its context, but not the time. And it is unable to confirm the date timezone (I assume it is UTC). A tool to fetch the current date and time, and optionally to specify the timezone for which the date and time is requested, would be a helpful feature.

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12. Anthropic Claude and Google Gemini Import

    Visionary user here switching from Anthropic Claude and Google Gemini to Proton Lumo Max I'd like to have an option as a user to import my data and memory as easy as possible. Today a manual import usually exceeds the context of the chat and even with multiple chat's it's not possible to get all data into Lumo. So please Proton give us an option to import our data.

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13. Give user direct selection/toggle of Lumo model mode/speed instead of popup menu

    It annoys me that I need to make 4 clicks just bringing back the model type/speed popup to change model from Lite to Max and fast and thinking mode.

    Why aren't there just two toggle switches, one that switches between Lite and Max and another below it that switches between Fats and Thinking. Like this

    Lite <TOGGLE> Max
    Fast <TOGGLE> Thinking

    Put these two toggles right of the input field for the Lumo.

    This way I can quickly switch Lumo model modes in a single or max 2 clicks. Instead of 4 just because I need to bring up the selection popup twice if I want to flip it from Max Thinking to Lite Fast.

    You can put descriptions in a small question mark in a circle next to them just like you explain things in ProtonMail settings.

    It annoys me that I need to make 4 clicks just bringing back the model type/speed popup to change model from Lite to Max and fast and thinking mode.

    Why aren't there just two toggle switches, one that switches between Lite and Max and another below it that switches between Fats and Thinking. Like this

    Lite <TOGGLE> Max
    Fast <TOGGLE> Thinking

    Put these two toggles right of the input field for the Lumo.

    This way I can quickly switch Lumo model modes in a single or max 2 clicks. Instead of 4 just because I need to bring up the… more

    3 votes

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14. Hide events from shared calendar

    When looking at a calendar that is shared with you, you may want to hide some events and event chains.

    We share calendars across our family, some like to put "work" or "commute" in there, I would like to hide those event chains without hiding their entire calendar.

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15. Integrated Notebook with Offline/Online Toggle & Nested Folder Structure

    Description: While existing requests cover "Offline Chat Access" and "Project Folders," I need a dedicated Integrated Notebook feature to permanently capture, organize, and secure specific insights extracted from our conversations. Currently, valuable information gets lost in endless scrolling or requires exporting to external tools.

    I request a native Notebook within Lumo with the following capabilities:

    1. Seamless Capture from Chat

    • Highlight any message or paragraph in a chat and instantly "Save to Notebook."

    • Saved notes become standalone, editable entries but retain a link back to the original conversation for context.

    1. User-Controlled Privacy (Offline vs. Online Toggle)

    • Crucial Feature: Users must be able to choose per note whether it is stored Offline-only (local device storage, zero-cloud access) or Online (synced across devices via Proton's secure infrastructure).

    • This allows for maximum privacy for sensitive data (health logs, personal journals) while keeping collaborative notes synced. Existing "Offline Chat" proposals do not offer this granular control per item.

    1. Robust Organization (Nested Folders)

    • Folder Structure: Create nested folders and subfolders (e.g., "Health > Silver", "Writing > Drafts", "Politics > Analysis").

    • Unlike simple project folders for chats, this allows for a hierarchical knowledge base where users can organize thoughts logically, not just chronologically.

    • Tags & Search: Add custom tags to notes and search across all notebooks instantly.

    Why this matters: This transforms Lumo from a transient chat tool into a permanent, structured, and private "second brain." The ability to toggle between offline and online storage ensures that users can keep their most sensitive data completely isolated on their device while still benefiting from cloud sync for less critical information. Combined with nested folder structures, this provides the organization power needed for complex, long-term projects without leaving the Proton ecosystem.

    Technical Feasibility: Leverages existing Proton local storage capabilities (similar to Proton Drive's offline mode) and integrates them directly into the chat UI, using the same encryption standards.

    Description: While existing requests cover "Offline Chat Access" and "Project Folders," I need a dedicated Integrated Notebook feature to permanently capture, organize, and secure specific insights extracted from our conversations. Currently, valuable information gets lost in endless scrolling or requires exporting to external tools.

    I request a native Notebook within Lumo with the following capabilities:

    1. Seamless Capture from Chat

    • Highlight any message or paragraph in a chat and instantly "Save to Notebook."

    • Saved notes become standalone, editable entries but retain a link back to the original conversation for context.

    1. User-Controlled Privacy (Offline vs. Online Toggle)

    • Crucial Feature: Users must be… more

    2 votes

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16. Lumo AI-Powered Card Organization for Proton Drive & Brain

    Enable Lumo AI to automatically organize, tag, and summarize saved cards in Proton Brain/Drive—zero-knowledge semantic search and connection discovery.

    BACKGROUND:
    This complements the "Proton Brain" feature request (UI focus). This one focuses on the intelligence layer that makes the system actually useful.

    CURRENT STATE:
    Users manually tag, categorize, and link notes. This is tedious and rarely done consistently. MyMind solves this with AI but sacrifices privacy.

    REQUESTED CAPABILITIES:
    1. Automatic Card Enrichment (Opt-In):
    - On save: Lumo generates summary (TL;DR, max 3 sentences)
    - Auto-tags extraction (3-7 relevant tags per card)
    - Entity recognition (people, places, topics mentioned)
    - Language detection + multilingual support

    1. Semantic Search:

       • Natural language queries across all cards
       • Example: "What did I save about GDPR compliance last year?"
       • Finds cards even if exact keywords aren't present
       • Results ranked by relevance, not just keyword match

    2. Connection Discovery:

       • Lumo suggests related cards ("You also saved X about this topic")
       • Suggests bidirectional links between notes
       • Identifies recurring themes over time
       • "Weekly Insights" digest of connected topics

    3. Smart Spaces (Auto-Organization):

       • Lumo proposes Spaces based on content clusters
       • Example: "I noticed 12 cards about 'Privacy Legislation'—create Space?"
       • Manual override allowed; never forced
       • Nested Spaces supported (Projects → Subprojects)

    4. Card Actions via Lumo:

       • "Summarize this collection of 20 cards"
       • "Find all cards mentioning [person/topic]"
       • "Generate outline from these research notes"
       • "Convert these meeting notes into action items"

    5. Privacy Architecture (Critical):

       • All indexing/search happens client-side OR in trusted execution environment
       • Server never sees decrypted content
       • Opt-in only (disabled by default for new users)
       • No model training on user data (explicit policy)
       • Delete any card → instant removal from index

    TECHNICAL IMPLEMENTATION PATH:
    Phase 1 (6 months): Server-side inference with encrypted payloads (MVP)
    Phase 2 (12 months): Hybrid model with client-side lightweight inference
    Phase 3 (18 months): Full zero-knowledge semantic search with TEE

    COMPETITIVE DIFFERENTIATORS:
    - Notion: AI but zero privacy guarantees
    - MyMind: AI but opaque encryption practices
    - Obsidian: Privacy but no native AI
    - Proton Brain: BOTH privacy AND intelligence

    VALUE PROPOSITION:
    Power users get AI-powered organization without becoming AI companies' training data. Journalists, lawyers, researchers, and activists finally have a PKM tool they can trust with sensitive information.

    PRICING RECOMMENDATION:
    - Lumo Plus required for AI features (justifies premium tier)
    - Free tier: Manual tags only, 10 AI summaries/month
    - Visionary: Unlimited AI processing included

    LINK TO RELATED:
    See also: https://protonmail.uservoice.com/forums/932839-proton-drive/suggestions/51632422-proton-brain-zero-knowledge-second-brain-with-car

    Enable Lumo AI to automatically organize, tag, and summarize saved cards in Proton Brain/Drive—zero-knowledge semantic search and connection discovery.

    BACKGROUND:
    This complements the "Proton Brain" feature request (UI focus). This one focuses on the intelligence layer that makes the system actually useful.

    CURRENT STATE:
    Users manually tag, categorize, and link notes. This is tedious and rarely done consistently. MyMind solves this with AI but sacrifices privacy.

    REQUESTED CAPABILITIES:
    1. Automatic Card Enrichment (Opt-In):
    - On save: Lumo generates summary (TL;DR, max 3 sentences)
    - Auto-tags extraction (3-7 relevant tags per card)
    - Entity recognition (people, places, topics mentioned)
    - Language… more

    2 votes

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17. Integration for Lumo to Home Assistant home automation software

    Add integration to Home Assistant. This would allow Lumo to help with configuration and home automation. Also, its a very tech community with lots of support on YouTube which will help spread the Proton brand further.

    3 votes

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18. Idea: What if Lumo used on-device processing for heavy lifting?

    Here's a thought: what if Lumo tapped into Apple's on-device intelligence (Neural Engine on iPhones/MacBooks) to pre-process large files before sending them to the cloud?

    Right now, if you upload a big reference book — say, 12 MB — Lumo simply truncates it to 128K tokens. You lose most of the content before analysis even begins. That's frustrating.

    A hybrid approach could fix this: the device handles the first pass (chunking, summarizing, extracting key sections), then ships the compressed result to Lumo's servers for the deep work. Less server load, faster responses, and no more throwing away half your document.

    Yes, it'd need bigger token limits on the server side and some clever work to keep everything encrypted end-to-end — but the payoff is real. This kind of edge-cloud split is already gaining traction in the industry, and it feels like a natural.

    Something to think about.

    Here's a thought: what if Lumo tapped into Apple's on-device intelligence (Neural Engine on iPhones/MacBooks) to pre-process large files before sending them to the cloud?

    Right now, if you upload a big reference book — say, 12 MB — Lumo simply truncates it to 128K tokens. You lose most of the content before analysis even begins. That's frustrating.

    A hybrid approach could fix this: the device handles the first pass (chunking, summarizing, extracting key sections), then ships the compressed result to Lumo's servers for the deep work. Less server load, faster responses, and no more throwing away half your document.

    … more

    2 votes

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19. Cloud-Assisted Verification ("Draft Local, Verify Cloud")

    Here is my proposal which I think good to implement. (Note: I'm using LUMO itself to help me to produce a proposal based on my ideas)

    Problem: Small on-device models (7B–20B) are prone to hallucination on complex tasks — inventing facts, generating incorrect syntax, or producing logically inconsistent output. Pure local inference is risky for high-stakes work (configuration changes, security analysis, medical/legal information).

    Proposed Solution: A three-layer pipeline where local generation is verified by a cloud model — without exposing raw user data.

    Architecture — "Local Generation + Cloud Verification":

    Layer 1: Local Generation (Instant & Private)

    Input (prompt + files) stays on device
    Small, fast model generates a draft response in milliseconds
    Zero data leaves the device at this stage
    

    Layer 2: Web Search Augmentation (Optional, Lightweight)

    If current information is needed, anonymized web search fetches relevant text
    Results are fed into the local model to incorporate into the draft
    

    Layer 3: Cloud Verification (Selective)

    The generated output text only (not the raw input, not the user's files, not the conversation context) is sent to a large cloud model
    The cloud model acts as a "judge" — it checks:
        Fact-checking: Are claims accurate?
        Logic consistency: Did the model contradict itself?
        Hallucination detection: Is this command syntax real or fabricated?
    Returns a confidence score or a corrected version
    Only triggered for complex/high-stakes queries; skipped for simple tasks
    

    Final Delivery:

    Confidence > Threshold: Show local draft immediately with ✅ "Verified" badge
    Confidence < Threshold: Replace with cloud-corrected version or flag as ⚠️ "Unverified — Review Recommended"
    

    Trust Indicators (Transparent to User):

    🟢 Fully Verified — Generated locally + web-augmented + cloud-verified
    🟡 Web-Verified — Generated locally + web search (no cloud verification)
    🔵 Local Only — On-device only, offline mode, best-effort accuracy
    

    Real-World Example:

    User asks: "Generate a Cisco IOS config to block SSH from subnet 192.168.10.0/24"
    Local model generates: access-list 100 deny tcp 192.168.10.0 0.0.0.255 any eq 22
    Cloud verifier checks: ACL syntax is correct, but missing permit ip any any at the end (implicit deny-all would break connectivity)
    Final output: Corrected config with verification badge
    

    Data Privacy Guarantee:

    Raw files (configs, logs, documents): ❌ Never leave device
    Conversation context/history: ❌ Never leaves device
    Generated output text: ✅ Sent to cloud for verification (output is less sensitive than input)
    User can disable cloud verification entirely ("Fast & Private" mode)
    

    Review — Strengths:

    Solves the hallucination problem without surrendering privacy or driving up costs
    Cloud verification is exponentially cheaper than cloud generation (checking 500 words vs. generating 500 words)
    Proton pays for cloud compute only on the ~20% of queries that genuinely need it
    Enables a lower subscription tier (majority of work happens on user hardware)
    Separates sensitive inputs (stay local) from generated outputs (can be verified)
    Trust indicators give users transparency about response confidence — critical for professional use
    

    Review — Risks:

    Latency: Waiting for cloud verification could delay response display
        Solution: Display local draft immediately with "Verifying..." indicator; update asynchronously if corrections are found
    Bandwidth: Sending every response for verification adds network overhead
        Solution: Selective verification — only verify complex queries; skip simple tasks (greetings, formatting, simple summaries)
    Privacy of output: Even generated output could contain sensitive information
        Solution: User toggle to disable cloud verification entirely; clear trust indicators so users know when verification is active
    

    Feasibility: MEDIUM — The concept of using a large model to verify a smaller model's output is well-established in AI research. Implementation requires Proton to build the verification API endpoint and client-side routing logic.

    Here is my proposal which I think good to implement. (Note: I'm using LUMO itself to help me to produce a proposal based on my ideas)

    Problem: Small on-device models (7B–20B) are prone to hallucination on complex tasks — inventing facts, generating incorrect syntax, or producing logically inconsistent output. Pure local inference is risky for high-stakes work (configuration changes, security analysis, medical/legal information).

    Proposed Solution: A three-layer pipeline where local generation is verified by a cloud model — without exposing raw user data.

    Architecture — "Local Generation + Cloud Verification":

    Layer 1: Local Generation (Instant & Private)

    Input (prompt +

    … more
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20. Stop automatic scrolling when generating long answers.

    When Lumo generates long answers (longer than page height in browser), it starts automatically scrolling down to keep up with the generated text. It would be better if the view does not automatically scroll down, so that I am able to read the answer from the beginning without having manually to scroll back up.

    Browser used: Brave

    6 votes

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