595 results found

1. Sensitive‑Data Confirmation for Feedback

   Problem

   When users give a thumbs‑up/down on a response, the associated snippet (including any code or text shown) is sent for anonymous analysis. If the snippet unintentionally contains sensitive information (IP addresses, ports, encryption keys, hashes, credentials, etc.), that data could be transmitted without the user’s explicit consent.

   Proposed Solution

   Add a post‑feedback check that scans the rated snippet for patterns that look like sensitive data. If a match is found, display a prompt:

   “The content you just rated appears to contain potentially sensitive information (e.g., IP address, key, hash). Do you want to include this data in the feedback you’re sending?”
   

   Provide three options:

   Send anyway – proceed with the normal feedback submission.
   Redact & send – automatically replace detected values with placeholders (e.g., <IP>, <KEY>).
   Cancel – abort the feedback submission so the user can edit or remove the sensitive data.
   

   Benefits

   Reduces risk of accidental leakage of confidential information.
   Gives users explicit control over what data leaves the session.
   Improves overall trust in Lumo’s privacy guarantees, especially for developers and security‑focused users who often share code snippets.
   Aligns with Lumo’s “privacy‑first” positioning without hindering valuable feedback.
   

   Implementation Notes

   Use lightweight regex patterns for common sensitive data types (IPv4/IPv6, ports, hex strings of typical key lengths, SHA hashes, JWTs, etc.).
   Allow the detection sensitivity to be toggled in settings for power users.
   Log only the user’s decision (send/redact/cancel), not the raw snippet, unless the user opts to send it.
   

   Problem

   When users give a thumbs‑up/down on a response, the associated snippet (including any code or text shown) is sent for anonymous analysis. If the snippet unintentionally contains sensitive information (IP addresses, ports, encryption keys, hashes, credentials, etc.), that data could be transmitted without the user’s explicit consent.

   Proposed Solution

   Add a post‑feedback check that scans the rated snippet for patterns that look like sensitive data. If a match is found, display a prompt:

   “The content you just rated appears to contain potentially sensitive information (e.g., IP address, key, hash). Do you want to include this data in the feedback

   … more
   4 votes

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2. Improved visibility of the Internet search icon in Lumo

   I find the current icon for the integrated Internet search in Lumo difficult to distinguish, especially with dark themes or low screen brightness. I suggest introducing a clearer, higher-contrast icon that automatically adapts to the selected theme (light/dark).

   Possible implementation:
   A minimalist magnifying glass icon with a subtle frame that is highlighted in color when search is activated.

   Optional: A short tooltip display (“Start/stop search”) when pressed and held to support new users.

   A clear icon makes it easier to find and activate the search function, which increases the overall usability of Lumo.

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3. Sometimes Lumo creates tables with columns which are too small making the table hard to read. Is an adjustable ui possible?
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4. Answers on Top instead of Bottom

   I would like to suggest, for answers to appear on top of a thread instead on the bottom. Instead of scrolling through the history from top to bottom, new answers appear on top. Should I want to reread the hlstory I can do so top to bottom Especially for longer threads that you would like to continue it would be more helpful.

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5. Repair the sound

   Repair app sound

   4 votes

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6. Personal Assistants / Preconfigured Prompts in Lumo

   I’d like Lumo to support personal assistants or preconfigured prompts, similar to what Kagi Assistant and other AI tools already offer.

   This would make it easier to switch between different roles (e.g., researcher, writer, teacher, developer) without having to retype or remember long instructions each time. It would also give users more control and flexibility to adapt Lumo to their own workflows.

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7. Lumo crashing on Android with No Google Play Store

   Hi team. great work on launching lumo. on my android phone with no google playstore and only google play services running with limited access and internet access blocked by firewall, the lumo app crashes after a delay of a few seconds.

   The error reads
   'Something went wrong.
   Check that Google Play is enabled on your device and that you're using an up-to-date version before opening the app...'

   all other proton apps work fine on my phone

   4 votes

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8. Add “Auto‑Enable Web Search” Setting for Lumo Chats

   Introduce a new option in Lumo’s Settings—“Auto‑Enable Web Search” (or a similarly named toggle). When activated, Lumo would automatically turn on the web‑search capability at the start of every new conversation, eliminating the need for users to manually enable it for each chat session.

   Why This Matters:

   • Seamless Experience: Users who rely on up‑to‑date information can get instant, real‑time answers without an extra step, making interactions feel more natural and efficient.

   • Consistency: Guarantees that all chats benefit from the latest data, especially for topics that change frequently (news, weather, stock prices, etc.).

   • Time Savings: Reduces repetitive actions for power users and those who frequently switch devices or start new threads.

   • Clear Control: The toggle remains user‑controlled, allowing privacy‑focused individuals to keep web search disabled if they prefer a purely offline Lumo experience.
     Suggested Implementation Details

   • Default State: Off by default to preserve the current behavior and respect user privacy expectations.

   • Indicator: Show a subtle icon or badge in the chat window when auto‑search is active, so users know the feature is enabled.

   • Per‑Session Override: Allow users to temporarily disable web search for a specific chat via the existing manual control, without affecting the global auto‑enable setting.
     Potential Benefits

   Improves user satisfaction for those who regularly need current information.
   Aligns Lumo’s functionality with the expectations set by other AI assistants that automatically leverage live data.
   Encourages broader adoption of Lumo Plus, where web search is a premium feature, by showcasing its convenience.

   Introduce a new option in Lumo’s Settings—“Auto‑Enable Web Search” (or a similarly named toggle). When activated, Lumo would automatically turn on the web‑search capability at the start of every new conversation, eliminating the need for users to manually enable it for each chat session.

   Why This Matters:

   • Seamless Experience: Users who rely on up‑to‑date information can get instant, real‑time answers without an extra step, making interactions feel more natural and efficient.

   • Consistency: Guarantees that all chats benefit from the latest data, especially for topics that change frequently (news, weather, stock prices, etc.).

   • Time Savings: Reduces repetitive actions for power users… more

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9. Toggle to volunteer to help improve lumos language model

   This would be opt in. If the user agreed, then warnings and such would be provided. The user would decide what information to volunteer, but certain information types would be restricted to protect the users privacy and to not compromise the values that proton is built upon. Also, proton or whoever maintains the ai language info/etc..., would have the final say before any volunteered info would be added. The exact way of doing this would need very careful considerations.

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10. An option to keep list of conversations always visible

    Currently the left sidebar is collapsed by default and past conversations aren't visible. There is a button, that expands it, however that expanded state doesn't persist between reloads, and the next time Lumo is opened, it's collapsed again. For someone who returns to previous chats frequently it's a must have.

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11. New chat shortcut

    It would be nice to have a new chat keyboard shortcut to avoid potential hallucinations without reaching for the mouse

    4 votes

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12. Fix your CSP Policy

    Content-Security-Policy: The page’s settings blocked the loading of a resource (default-src) at https://account.proton.me/lumo because it violates the following directive: “default-src 'self'”

    Content-Security-Policy: The page’s settings blocked the loading of a resource (default-src) at https://account.proton.me/lumo/signup because it violates the following directive: “default-src 'self'”

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13. 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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14. 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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15. 🤖 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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16. 🧠 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

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17. 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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18. 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.

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19. Auto-Match Controls

    The auto-match feature adds files based on keyword matching, which can unintentionally clutter conversations with unrelated files.

    Requested: Settings to control auto-match behavior:

    • Toggle auto-match on/off entirely
    • Require explicit @mention for file inclusion
    • Limit matches to exact filenames only (no substring matching)
    • Show preview before auto-added files enter context
    3 votes

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20. Pay-as-you-go option alongside subscriptions

    Since I assume you may eventually add more capable, larger, and more expensive models, such as Kimi K3, it would be great to have an alternative option for occasional users.

    Something similar to what OpenRouter and NanoGPT offer: a prepaid, usage-based credit system rather than a traditional fixed monthly subscription. Users could add funds to their account balance, and each model request would deduct credits based on the model used, token usage, and pricing.

    Alongside this, it would also be nice to have more advanced generation settings, such as a custom system prompt, the option to use the base/vanilla model behavior where supported, adjustable reasoning effort, temperature controls, and similar parameters.

    I think this would be especially useful for people who don't use Lumo frequently enough to justify a monthly subscription but would still like occasional access to more capable or expensive models.

    Since I assume you may eventually add more capable, larger, and more expensive models, such as Kimi K3, it would be great to have an alternative option for occasional users.

    Something similar to what OpenRouter and NanoGPT offer: a prepaid, usage-based credit system rather than a traditional fixed monthly subscription. Users could add funds to their account balance, and each model request would deduct credits based on the model used, token usage, and pricing.

    Alongside this, it would also be nice to have more advanced generation settings, such as a custom system prompt, the option to use the base/vanilla model… more

    3 votes

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