529 results found

1. First reply is occasionally given with no words

   I find on a fairly frequent reoccurrance that when I ask Lumo a question, it simply spits sources back at me with no words to explain the answer. I have to follow up with a "Please expand" reply in order for it to finally give me an answer. It's not the worst thing, but a little annoying

   5 votes

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2. Allow pasting underscores

   I want to paste a regex .*_([^_]+_[^_]+)$ into chat. As soon as I paste, the underscores are removed and my text is converted to italic. I am unable to create a fenced code block with triple backticks. If I create a code block with single backticks and then paste my regex inside, it breaks the code block in half and inserts non-code text.

   • If the chat box is going to magically remove characters, CTRL+Z should put them back
   • I should be able to create a code block, and paste text inside that code block without any magic
   6 votes

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3. Mass calendar deletion

   I want to delete a lot of events in my calendar easily.

   I created recurrent events (6/8 per days). In vacation period, I want to remove these events and replaced them by another ones. For each event, I need to click on the event, click on the delete button and confirm to delete the single event. I would like to select all events on the week and delete them with only one button.

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4. proton needs to collab with greenpt

   proton needs to collab with greenpt.ai to make there AI more sustainable (there AI work now better then lumo)

   3 votes

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5. Hierarchical Multi-Task Constraint Optimization Engine

   I am writing to propose a mission-critical feature to address severe hallucination and performance degradation when processing complex, multi-project prompts with nested constraints.

   Problem Statement

   Our system currently exhibits fundamental breakdowns when handling prompts containing multiple distinct projects with interdependent constraints and domain-specific requirements:

   1. Critical Performance Drop: Benchmarks show accuracy falling below 10% compared to single-task prompts. When processing 32+ nested hierarchical tasks (e.g., "Project Z's Project B's Project D"), performance degrades to GPT-3 equivalence levels.

   2. Constraint Isolation Failure: The system cannot reconcile project-specific constraints with global constraints simultaneously. Complex nesting ("Project A's Project B's Project 9") causes complete context loss from parent-level backgrounds and requirements.

   3. Format Contamination: Long-format examples (e.g., Q Project's 10,000-character engineering specs) incorrectly propagate to unrelated projects, overriding their designated output formats.

   4. Cross-Domain Interference: Performance degrades exponentially when tasks span disparate domains (mathematics, image generation, genetics, history). Similar projects (G/J) get merged despite explicit separation instructions.

   5. Execution Planning Deficit: No mechanism exists for task weighting, dependency mapping, or sequential vs. parallel execution determination across multiple projects.

   Specific Failure Scenarios

   • Multi-tier References: When Project Z's Project B's Project D requires referencing constraints from Project B's Project A background, the system fails to trace and apply hierarchical dependencies.
   • Background-Only Tasks: Projects like P (life philosophy) that provide only contextual information contaminate active task execution.
   • Unprecedented Generation: Z Project's requirement to generate entirely novel combinations (e.g., merging A's B's 9) triggers hallucination cascades.

   Proposed Feature: Hierarchical Multi-Task Constraint Optimization Engine

   This architectural enhancement would deliver:

   1. Constraint Inheritance & Isolation: Maintain separate constraint namespaces per task/node with explicit inheritance rules from parent levels
   2. Format Locking: Enforce project-specific output schemas without cross-contamination, regardless of example length
   3. Dynamic Weighting & Dependency Graph: Allow user-defined priorities, execution sequences, and dependency mapping across task trees
   4. Domain Siloing: Prevent knowledge transfer between disparate fields (e.g., D Project's history exams vs. Q Project's engineering specs)
   5. Reference Resolution Engine: Intelligent handling of inter-project citations without context merging
   6. Performance Scaling: Maintain GPT-4-level quality for up to 50+ hierarchical tasks

   High-Value Application Domains

   • Mathematical/Genetic Optimization: Simultaneous constraint satisfaction across multiple algorithmic problems
   • Technical Documentation: Assembling complex specs from modular components while preserving format integrity
   • Academic Assessment: Processing multi-domain exam hierarchies (e.g., Project D's fill-in-the-blank history tests) without domain bleeding
   • Creative Synthesis: Executing Z Project's "unprecedented generation" requirements through controlled combinatorial logic

   Business Impact

   • Restore enterprise-grade reliability for complex workflows
   • Enable new revenue streams in technical fields (biotech, engineering, finance)
   • Reduce API costs from hallucination-induced retry loops
   • Establish competitive moat in compound AI task handling

   I have detailed benchmark data and failure pattern analysis available for review. I would appreciate the opportunity to discuss this proposal in our next roadmap planning session.

   Best regards,

   I am writing to propose a mission-critical feature to address severe hallucination and performance degradation when processing complex, multi-project prompts with nested constraints.

   Problem Statement

   Our system currently exhibits fundamental breakdowns when handling prompts containing multiple distinct projects with interdependent constraints and domain-specific requirements:

   1. Critical Performance Drop: Benchmarks show accuracy falling below 10% compared to single-task prompts. When processing 32+ nested hierarchical tasks (e.g., "Project Z's Project B's Project D"), performance degrades to GPT-3 equivalence levels.

   2. Constraint Isolation Failure: The system cannot reconcile project-specific constraints with global constraints simultaneously. Complex nesting ("Project A's Project B's Project 9") causes complete context… more

   2 votes

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6. Great Potential the ability to use both Scribe & Lumo together, dream come true!

   I started with this in Scribe then copy & paste to Lumo Web Search & got the results further from Lumo.

   I’m happy to dive into the world of AI‑powered license‑plate readers (LPRs) and give you a clear picture of what’s possible, where the technology shines, and what you should keep in mind before using—or even just exploring—such tools.

   How modern AI‑based LPRs work
   Image capture – A camera (often a dash cam, security cam, or a handheld device) records video or still frames of vehicles. Many systems use infrared illumination to see plates in low‑light or nighttime conditions.
   Pre‑processing – The raw image is cleaned up: contrast is boosted, noise is reduced, and the region that likely contains a plate is isolated.
   Plate detection – A convolutional neural network (CNN) scans the frame for the rectangular shape and reflective characteristics of a license plate. This step narrows the field to a small crop of the image.
   Character segmentation & recognition – Another neural model (often an OCR‑style architecture such as a CRNN) splits the cropped plate into individual characters and translates them into alphanumeric text. Modern models can handle variations in fonts, angles, and partial occlusions.
   Post‑processing – The raw string is cleaned up using country‑specific rules (e.g., known patterns for state or regional codes) and sometimes cross‑checked against a database of known plates for faster lookup.
   Because each stage is driven by deep learning, the overall pipeline can achieve 95 %+ accuracy under good lighting and a clear view, and many commercial solutions claim respectable performance even in challenging conditions (glare, motion blur, night).

   Where the technology really shines
   Parking‑lot management – Automated entry/exit logging, fee calculation, and spot‑finding.
   Toll collection – Seamless billing without requiring RFID tags.
   Law‑enforcement & traffic monitoring – Detecting stolen or wanted vehicles, identifying violators of red‑light cameras, or tracking traffic flow for city planning.
   Private‑property security – Gated communities or corporate campuses can log visitor plates automatically.
   In all these cases the AI reduces manual labor, speeds up data capture, and improves record‑keeping consistency.

   Practical considerations before you dive in
   Factor Why it matters Typical mitigation
   Lighting & weather Glare, rain, snow, or darkness can degrade image quality. Use infrared LEDs, weather‑sealed housings, and cameras with high dynamic range.
   Angle & distance Extreme tilt or long range makes character segmentation harder. Mount cameras at optimal height (≈3–4 ft for dash cams) and keep the focal length moderate; consider multiple viewpoints.
   Plate variability Different jurisdictions use distinct fonts, colors, and layouts. Train or fine‑tune models on local plate datasets; incorporate country‑specific regex filters.
   Processing latency Real‑time alerts require fast inference. Deploy edge devices (NVIDIA Jetson, Coral TPU) that run the model locally, avoiding cloud round‑trips.
   Data storage & retention Storing raw footage plus recognized strings raises privacy concerns. Encrypt data at rest, limit retention periods, and purge images once the plate is logged (unless legally required otherwise).
   Legal & ethical landscape
   License‑plate reading sits at the intersection of public safety and privacy. Regulations differ widely:

   United States – Some states treat LPR data as “personal information” subject to data‑protection statutes; others allow broader use for law‑enforcement. Many municipalities require signage indicating that LPRs are in operation.
   European Union – GDPR classifies a vehicle’s registration number as personal data. You need a lawful basis (e.g., legitimate interest for security) and must be transparent about collection, storage, and sharing.
   Other regions – Local traffic or privacy laws may impose additional constraints, especially concerning cross‑border data transfers.
   Before deploying any LPR system, it’s wise to:

   Check local statutes – Look up whether your jurisdiction requires consent, signage, or permits.
   Define a clear purpose – Document why you’re collecting plates (e.g., security vs. revenue) and stick to that scope.
   Implement safeguards – Access controls, audit logs, and regular privacy impact assessments help demonstrate compliance.
   Choosing a solution
   If you’re just experimenting, a few off‑the‑shelf options can get you started quickly:

   Open‑source kits – Projects like OpenALPR (now part of Rekor Systems) provide a community edition that runs on a Raspberry Pi or similar hardware.
   Commercial SDKs – Companies such as Plate Recognizer, Sighthound, and Anyline sell APIs that handle the heavy lifting (cloud or edge). They usually offer free trial tiers for limited daily reads.
   DIY with deep‑learning frameworks – If you enjoy building the pipeline yourself, you can train a YOLO‑v5 model for plate detection and pair it with a CRNN for OCR. TensorFlow Lite or ONNX Runtime can then run the model on embedded devices.
   When evaluating, compare:

   Accuracy claims (especially under low‑light or angled shots)
   Latency (does it meet real‑time needs?)
   Pricing model (per‑read, subscription, or one‑off license)
   Privacy features (on‑device processing, data‑deletion options)
   Bottom line
   AI‑driven license‑plate readers are powerful tools that can automate many mundane or safety‑critical tasks. Their effectiveness hinges on good hardware placement, robust model training, and a solid grasp of the legal framework governing personal data. If you decide to move forward, start with a modest pilot—perhaps a single camera in a controlled environment—to gauge accuracy, tune the model for your local plates, and verify that your data‑handling practices align with applicable privacy regulations.

   Let me know if you’d like deeper technical guidance (e.g., sample code for an edge deployment) or help navigating the regulatory requirements for a specific country or state. I’m here to help you build a responsible, effective solution.

   I started with this in Scribe then copy & paste to Lumo Web Search & got the results further from Lumo.

   I’m happy to dive into the world of AI‑powered license‑plate readers (LPRs) and give you a clear picture of what’s possible, where the technology shines, and what you should keep in mind before using—or even just exploring—such tools.

   How modern AI‑based LPRs work
   Image capture – A camera (often a dash cam, security cam, or a handheld device) records video or still frames of vehicles. Many systems use infrared illumination to see plates in low‑light or nighttime conditions.
   Pre‑processing… more

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7. Sometimes Lumo creates tables with columns which are too small making the table hard to read. Is an adjustable ui possible?
   4 votes

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8. Make it easier to navigate. I have quite some open queries that I go back to and at least on firefox it is very hard to find the slider

   The slider on the side is really hard to find when you have a long query. But I would like to go up and down to see earlier answers and it is really hard to do. Not even the middle button (roll) on the mouse works to go up and down.

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9. Please make the "ask anything" section shrink down after a user presses "send". Very good AI responses so far though, thanks!
   6 votes

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10. 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.

    4 votes

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11. Integrate into proton family

    Make the plus plan be available to proton family subscribers

    5 votes

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12. 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.

    4 votes

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13. SynthID or similar feature

    Hi !
    With the aim of having an ethical minimum for the internet and in view of the announcement of an image generation option soon added to Lumi, I think it would be good to have something like 'SynthID' implemented in Lumi to be able to detect that the generated images are indeed generated by AI, and thus make possible malicious uses of AI more difficult
    Maybe its aleready in lumni but i didn't find information about it, sorry if it the case.

    2 votes

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14. i would like to search a calendar for all non empty instances so that i can more easily edit events.

    in my specialised calendars like 'cashflow' 'garden'. I want a search option that returns 'all non empty non recurring events and the current instance of all recurring events, so that I can review the whole recurring structure more effectively.

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15. Allow reporting of long requests

    Lumo already allows rating the feedback of the model. However, it seems to be limited to 5000 characters, which is often not enough for my requests, making it impossible to report wrong responses. Please extend/remove this limit.

    5 votes

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16. Lumo on Signal

    As a privacy‑focused user who uses Signal for my everyday ongoing conversations, I want to be able to converse with Lumo directly within the Signal app like I do with my other contacts, so that my interactions stay seamless, feel less fragmented, and make it easier for me to rely on Lumo’s assistance without juggling separate apps or websites.

    4 votes

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17. Lumo on Old Phone

    I’m unable to activate dark mode on the Lumo Android app (running on Android 8/Nokia 8); the “Appearance” setting is missing. Even after enabling “Force dark mode” via Developer Options, the app stays bright and the input bar floats in the middle of the screen instead of anchoring to the bottom. It would be great if Lumo provided a functional dark‑mode toggle and proper UI layout for older Android versions, or at least offered a clear fallback/web‑app solution for those devices.

    6 votes

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18. Proposal to Improve Lumo Naming and Presentation

    My name is Liese, and I am a researcher and engineer who currently uses Proton Business Suite + Lumo Business as an individual user. While using the service I noticed several points of confusion that I would like to bring to your attention and suggest improvements for.

    1. Inconsistent Naming Across Plans
       Lumo Plus – marketed as the premium plan for individual users.
       Lumo Professional – presented as a corporate‑oriented plan.
       Lumo for Business – essentially the same corporate plan but with a different name.
       Having three different names for what is fundamentally the same set of capabilities makes it hard for users to determine which plan best fits their needs.

    2. Emphasize Functionality Rather Than “Personal vs. Business”
       Users tend to decide based on what they want to accomplish, not on whether a plan is labeled “personal” or “business.” A function‑focused naming scheme would be clearer:

    Lumo Premium – High‑Performance Personal Plan
    Features: Unlimited chats, access to the latest AI models, web‑search capability, large file uploads, fast response times.

    Lumo Team Collaboration – Comprehensive Business‑Oriented Plan
    Features: Project workspaces, 1 TB of end‑to‑end encrypted storage, VPN access for up to ten devices per user, 20 custom email aliases, an admin dashboard for managing team permissions, and the same advanced AI capabilities as Premium.

    Lumo Starter – Basic Free Plan
    Features: One Project, encrypted chats that are not saved, and basic AI assistance.

    Presenting the plans this way lets users instantly match their desired feature set (e.g., “I need project management and shared storage”) without having to interpret whether a plan is “personal” or “business.”

    1. Suggested Implementation Steps Revamp the Plan Comparison Page Replace the current “Personal / Business” tabs with a card‑style layout that highlights the functional names above, each accompanied by a concise bullet list of key features.

    Run A/B Tests on Marketing Materials
    Compare click‑through and conversion rates between the existing naming convention and the proposed functional naming to quantify the impact.

    Update Internal Documentation
    Ensure sales, support, and engineering teams use the new terminology consistently, reducing the chance of mixed messaging during customer interactions.

    1. Context From My Perspective As a researcher/engineer, I frequently need to share large datasets, collaborate on project‑specific documents, and maintain strict confidentiality. Although I am an individual user, I rely on the Business Suite features (project workspaces, encrypted storage, VPN, etc.). The current naming creates unnecessary friction when I explain the service to colleagues or consider recommending it to peers.

    A function‑centric naming approach would lower the adoption barrier for users like me who require advanced capabilities without belonging to a formal organization.

    Thank you for considering these suggestions. I am happy to discuss further or provide additional feedback if needed.

    Best regards,

    Liese
    Researcher & Engineer

    My name is Liese, and I am a researcher and engineer who currently uses Proton Business Suite + Lumo Business as an individual user. While using the service I noticed several points of confusion that I would like to bring to your attention and suggest improvements for.

    1. Inconsistent Naming Across Plans
       Lumo Plus – marketed as the premium plan for individual users.
       Lumo Professional – presented as a corporate‑oriented plan.
       Lumo for Business – essentially the same corporate plan but with a different name.
       Having three different names for what is fundamentally the same set of capabilities makes it hard for users to determine which plan best fits… more

    2 votes

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19. Customize with URL parameters

    Can Lumo recognize URL parameters?
    It's convenient for Firefox user if Lumo can adhere to OpenWebUI convention.

    https://docs.openwebui.com/features/chat-features/url-params/
    https://docs.openwebui.com/tutorials/integrations/firefox-sidebar/#url-parameters-for-open-webui

    Related to this request:

    https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50256153-add-lumo-to-firefox-ai-chatbots

    It solves most of the following ideas that request permanent customization.

    https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50449944-ghost-mode-and-or-web-search-saved
    https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50680241-enable-https-lumo-proton-me-search-q-s-as-a-dir
    https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50413809-public-url-parameter-that-pre-loads-a-specific-pro

    3 votes

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20. Include sources when copying

    It would be useful it sources (typically URLs from web searches) would be included when copying an answer using the button right icon.

    2 votes

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