556 results found

1. travel time before/after event

   It would be great if the calendar added the ability to include travel time to an event, after the event. For example, I have a meeting in another part of the city, and I need 30 minutes to get to the location. It would be very convenient for all that time to be shown in the calendar so that others can see that you are also busy during the travel period.

   Currently this has to be worked around by creating another event or extending the existing one.

   4 votes

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2. 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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3. Add Concise/Think Longer option for all answers

   Add an option to choose to always be concise or think longer for all answers.

   5 votes

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4. lumo.ai for quick access > lumo.proton.me

   Also: https://lumo.ai/search?q=%s site search support

   5 votes

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5. delete button (outside of settings)

   Can you please move the delete button out of the settings and into the sidebar, or somewhere? I know lots of people keep AI chats, I don't. It seems like extra steps to hide it in settings.

   2 votes

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6. Enhanced OCR (e.g. extraction of passport credential)

   Enhancement of the OCR function so that data from scans (e.g. from passports) can also be recognized.

   5 votes

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7. Lumo Integration Part Two (Lumo Dedicated Encrypted Desktop Assistant, Legacy & New Applications Work Options)

   All code generation & storage is encrypted and wrapped by a YubiKey.

   Running code requires a second‑factor OTP/TOTP from the same YubiKey.

   If the device can’t handle the workload, LDA warns the user and offers a secure cloud‑inference fallback.

   The assistant stays offline except for signed updates or an explicit, user‑approved cloud request.

   Implement the modules, follow the testing plan, and you’ll have a privacy‑preserving, hardware‑rooted Lumo Desktop Assistant that can safely generate, store, and execute programs on demand.

   4 votes

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8. Let Lumo reference past chats & have memory

   Hello Proton Team,
   I would like to suggest adding a new feature to Lumo that allows it to collect personal information and interact with users in a manner similar to ChatGPT, but with a strong focus on privacy. The idea is to enable Lumo to understand and assist users by learning from their information, preferences, and interactions — all while ensuring that this data is fully private and protected via end-to-end encryption.
   This would combine the convenience and intelligence of an AI assistant with Proton’s commitment to user privacy and security. It could open up new possibilities for personal productivity, smart assistance, and secure communication without compromising sensitive data.
   Thank you for considering this innovative enhancement for Lumo.

   Hello Proton Team,
   I would like to suggest adding a new feature to Lumo that allows it to collect personal information and interact with users in a manner similar to ChatGPT, but with a strong focus on privacy. The idea is to enable Lumo to understand and assist users by learning from their information, preferences, and interactions — all while ensuring that this data is fully private and protected via end-to-end encryption.
   This would combine the convenience and intelligence of an AI assistant with Proton’s commitment to user privacy and security. It could open up new possibilities for personal productivity,… more

   5 votes

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9. 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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10. 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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11. 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.

    5 votes

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12. 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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13. 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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14. 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

    3 votes

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15. 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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16. 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.

    5 votes

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17. 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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18. 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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19. Integrate into proton family

    Make the plus plan be available to proton family subscribers

    5 votes

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20. 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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