288 results found

1. Transfer/Share chat between users

   It would be nice to be able to share chats between proton users. This may be restricted to users belonging to the same duo/family/pro/... account, in which case it may be interesting to allow the transfer of chats from one user to another.

   This would enable collaborative (but private to selected users) chats.

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2. binary file type parsing

   According to Lumo, uploaded binary files are only read as a first snippled, and this is no different in the Lumo+ version. Hence, an Excel table or a pdf cannot be parsed or only the first few lines, rendering the function useless. Pls, parse full files. Output of Lumo:

   File‑type handling: I can read the first few lines of a text‑based file (CSV, plain‑text, JSON) that you upload, but I cannot open binary formats (PDF, DOCX, XLSX) or run arbitrary parsing libraries. Your annual‑report PDFs and the Excel workbook itself are binary; I can see the file name and a tiny preview of a CSV‑style export, but I cannot extract tables or numbers from them.

   According to Lumo, uploaded binary files are only read as a first snippled, and this is no different in the Lumo+ version. Hence, an Excel table or a pdf cannot be parsed or only the first few lines, rendering the function useless. Pls, parse full files. Output of Lumo:

   File‑type handling: I can read the first few lines of a text‑based file (CSV, plain‑text, JSON) that you upload, but I cannot open binary formats (PDF, DOCX, XLSX) or run arbitrary parsing libraries. Your annual‑report PDFs and the Excel workbook itself are binary; I can see the file name and a… more

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3. 2) Lumo Actions

   Lumo should be able to take cross-platform actions across the web, but the user should be in control at all times.

   Next post: https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50637320-3-lumo-research

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4. Reorder favorite chats

   Favorite chats cannot be reordered currently

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5. starting over from the beginning is annoying

   It's very annoying to have to scroll to pick up where I left off in a long chat. Please revert to the way things were before or add a setting for those who want to start the chat from the beginning.

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6. Bonjour, est-il possible de laisser la possibilité à LUMO d'inscrire des rendez-vous dans proton calendar?

   Un grand merci pour votre travail. J'écris en français parce que c'est la langue de Genève non ? (De dieu de dieu :-P)

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7. 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

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8. 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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9. New Zealand Calendar Public Holidays.

   Add New Zealand as an option in calendar to add public holidays.

   2 votes

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10. Lumo Companion

    Lumo Companion, virtual Girlfriend or Boyfriend. I like lumo and everything it stands for, but for $9.99 a month(with annual sub) I will like the option to have a virtual companion.

    2 votes

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11. Removing Lumo from my Proton experience

    Before you call me a Luddite, let me be clear: The original definition of a Luddite is one who did not want technology foisted upon them without consent. The AI craze has made me a loud-and-proud Luddite. Go elsewhere if you want to talk about how it's the "future" and "inevitable" in its current state.

    My personal views on the viability of "ethical" GenAI aside, aside, I simply don't want to see a Lumo option in my tabs of much more useful Proton apps, like Drive, Calendar, Wallet and VPN.

    Keep the cutesy purple cat out of my tabs, please, or I might think about downgrading from my existing subscription. Give me an opt-out button so I don't see it as a listed option.

    Thank you.

    Before you call me a Luddite, let me be clear: The original definition of a Luddite is one who did not want technology foisted upon them without consent. The AI craze has made me a loud-and-proud Luddite. Go elsewhere if you want to talk about how it's the "future" and "inevitable" in its current state.

    My personal views on the viability of "ethical" GenAI aside, aside, I simply don't want to see a Lumo option in my tabs of much more useful Proton apps, like Drive, Calendar, Wallet and VPN.

    Keep the cutesy purple cat out of my tabs, please,… more

    3 votes

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12. Lumo Client (or API) with secure local execution of Lumo‑generated Python graphical output

    When users request visualizations, Lumo frequently replies with Python code snippets (e.g., matplotlib graphics). Users can copy‑paste the code into their own environment to render the graphics.

    It would be interesting to have some kind of Lumo‑Python bridge that delivers the generated Python code to a local client. The client runs the code in a sandboxed, offline Python/IPython/Jupyter/... session and returns the rendered image directly to the chat.

    This would look like a local client chat application communicating with proton servers, able to execute Lumo-generated Python code on the user’s machine. No data leaves the device, preserving confidentiality and complying with privacy‑first principles, and a sandboxed Python would prevent from possible side-effect. Users would then be able to get visual answers from Lumo in both an efficient and private way.

    This may be implemented as an independent client app, or (maybe even better) as an OS-agnostic jupyter kernel for taking advantage of the notebook interface (with something that would be essentially a notebook-integrated Python API to Lumo).

    When users request visualizations, Lumo frequently replies with Python code snippets (e.g., matplotlib graphics). Users can copy‑paste the code into their own environment to render the graphics.

    It would be interesting to have some kind of Lumo‑Python bridge that delivers the generated Python code to a local client. The client runs the code in a sandboxed, offline Python/IPython/Jupyter/... session and returns the rendered image directly to the chat.

    This would look like a local client chat application communicating with proton servers, able to execute Lumo-generated Python code on the user’s machine. No data leaves the device, preserving confidentiality and complying… more

    3 votes

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13. 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

    3 votes

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14. 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

    3 votes

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15. 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

    3 votes

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16. Theme

    I understand that this isn’t a priority right now, but in the long term it would be great to let Lumo have the same themes that are offered in the other Proton products (Mail, Drive..). (Lumo with Ebony would be beautiful 😍​)

    2 votes

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17. 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.

    3 votes

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18. 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.

    3 votes

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19. New chat shortcut

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

    3 votes

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20. Security

    Changes to all existing idieas replace with a save and more secure one

    3 votes

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