323 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

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2. Geolocation for the location field

   It would be so nice for the location field in the calendar to search for and populate a location (e.g. I type in the name of a store & city name, and then it gives me a few options to select, at which point it then replaces what I've typed for search with an address)

   3 votes

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3. 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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4. File types and size / unsupported file types

   The current file types to upload f.e. images don’t work well and constantly I’ll get the message of “unsupported file type” in the pop-up window and that’s a false flag. They are all files types that Lumo support. I can open those files without any problems within Proton Drive or iCloud. Same issue occurs on Windows desktop, iPhone and iPad. Drag and drop makes no difference.

   The file size is also an issue, limited to 10MB and I have a subscription. An average photo is already more than that. Files stored in Proton Drive can be uploaded with a maximum of 200MB, but technical files are often much larger (f.e. a Cinema 4D scene (graphics / animation) can be more than 1GB). So that makes it impossible to let Lumo analyse such files, besides the false “unsupported file type” message.

   Room for improvement.

   The current file types to upload f.e. images don’t work well and constantly I’ll get the message of “unsupported file type” in the pop-up window and that’s a false flag. They are all files types that Lumo support. I can open those files without any problems within Proton Drive or iCloud. Same issue occurs on Windows desktop, iPhone and iPad. Drag and drop makes no difference.

   The file size is also an issue, limited to 10MB and I have a subscription. An average photo is already more than that. Files stored in Proton Drive can be uploaded with a maximum… more

   4 votes

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5. STT voice transcription run on-device, not sent to Google.

   STT voice transcription run on-device, not sent to Google.

   4 votes

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6. Lumo prompt-library

   Create and save prompt to reuse them with different parameters.

   3 votes

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7. Local information

   It's absolutly infuriating that lumo helps me with medical papers, documents and more and i constantly need to remind lumo to not use the wrong format. Why is there no option to set a default for this? As much as i understand everyone want to use different date format, i do not understand how this justfiy to not have the option to change basic information. Because it's extremly annoying and absurd and even more that each time i ask lumo something, lumo assumes i am in the USA. Is there something i am not aware of? Is proton actually owned by USA companies? How does privacy work when the AI assumes i am in USA despite me CONSTANTLY remind i am not. This is absolutly absord. Especially when it comes to anything to do with products, i get USA products??? Also Lumo is showing me USA prices ($) that are then converted to my currency because, even if the OUTPUT is "corrected based on location", the so called "system" still assumes, i am in USA, so because i can not correct it, i still get results, by default, from the USA. It's absolutly infuriating how this basic element is NOT adressed. I don't know what stuff other people do with lumo, but what i am asking is an option, it's not a complet rework. It's a simple "use this format".

   EDIT:

   Just now, lumo was thinking about me asking to correct the date. And in the "Thought about this" I see this line:

   "In ISO format (YYYY-MM-DD), this would be April 7, 2026."

   This is from lumo's "Thought about this". As in Lumo can not even use a correct format even when explicitly saying it in the "Thought about this". This line is COPY PASTE. This is how bad it is. Please add options for us to change because this is ridiculous.

   EDIT 2:

   I censored my location (i just replaced it with an "-" but it's in Europe

   The user is pointing out that I keep saying "April 7" but the file name says "04-07" which could be interpreted differently. In -, 04-07 would be 4th of July, not April 7th. In US format, 04-07 would be April 7th.

   But the user is in the -. So 04-07 in - format would be July 4th, not April 7th.

   I've been reading "04-07" as US format (April 7) when the user is in the - where dates are written DD-MM (day-month).

   I want to repeat, this is an "Thought about this", meaning i have 0 control over this none sense where Lumo is obsseds with USA dates and USA formats. Insted of realising that the issue is that Lumo keep using month day, insted of day month, Lumo is trying to figure out what the issue is using USA format.

   To proton, please fix this absordity by introducing the options, to let us chose the defaults for our accounts.

   I already have comunication dificulties, and the last thing i need is lumo not know the difference between the 04 of 07 and 07 of 04 because it is mixing up the formats for dates. (also the 04-07 is because i pasted a large copy paste, but it was 2026-04-07, yet lumo is still writting it like April 7, 2026 so no, it's not an "mistake", Lumo is really bugged.

   It's absolutly infuriating that lumo helps me with medical papers, documents and more and i constantly need to remind lumo to not use the wrong format. Why is there no option to set a default for this? As much as i understand everyone want to use different date format, i do not understand how this justfiy to not have the option to change basic information. Because it's extremly annoying and absurd and even more that each time i ask lumo something, lumo assumes i am in the USA. Is there something i am not aware of? Is proton actually owned… more

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8. Feature Request: Exclusive Access to Larger LLMs for Lumo Plus Subscribers

   I would like to propose a feature specifically for paying Lumo Plus subscribers: the ability to enable Lumo to use larger LLMs (such as GPT-5, GPT-5 Thinking, Gemini 2.5 Pro, Grok 4, Claude Sonnet 4.5, etc.) for processing queries. The core idea is for Lumo to act as a privacy barrier—protecting user data—while still taking advantage of the advanced capabilities of top-tier models for those who subscribe.
   This enhanced feature should be strictly limited to users who actively pay for Lumo Plus. Free users would continue using the current models and would not have access to this option.
   Lumo Plus subscribers could choose, via settings, to allow Lumo to access more powerful LLMs. By default, Lumo would operate with its existing models. Only if a user both pays for Lumo Plus and enables the feature in the settings would Lumo use the larger models as requested.
   This approach allows premium users to benefit from the superior analysis and reasoning of large models, with Lumo serving as a privacy shield. One model could focus on analytic thinking while another produces the output, but only for those who have upgraded to Lumo Plus and specifically opt in. Free users would not have this capability or choice, ensuring it remains an exclusive benefit for paid subscribers.

   I would like to propose a feature specifically for paying Lumo Plus subscribers: the ability to enable Lumo to use larger LLMs (such as GPT-5, GPT-5 Thinking, Gemini 2.5 Pro, Grok 4, Claude Sonnet 4.5, etc.) for processing queries. The core idea is for Lumo to act as a privacy barrier—protecting user data—while still taking advantage of the advanced capabilities of top-tier models for those who subscribe.
   This enhanced feature should be strictly limited to users who actively pay for Lumo Plus. Free users would continue using the current models and would not have access to this option.
   Lumo Plus… more

   5 votes

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9. Feature Suggestion: Built-In Email Productivity Tools for Faster Daily Workflow

   Many ProtonMail users handle a high volume of emails every day and often need small productivity tools to speed up their work. A built-in panel offering simple utilities like quick text formatting, a snippet creator, a link checker, and an email content helper could make daily communication much faster and easier.
   I’ve created an external toolbox at https://toolinq.com
   that provides similar utilities. If ProtonMail adds a simplified version of these features directly inside the inbox, it could make the overall user experience even more efficient.

   3 votes

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10. Multiple Personalities - more control & community templates

    Currently, under the 'Personalization' you can create ONE personality per user. Depending on my task at hand I need more control over the expected output. As currently there is not really a good way to configure the expected outcome. (e.g. how 'creative' the model can be or how strict it needs to adhere to the input).

    This feature would also really benefit from (community) templates which must be editable for better control.

    4 votes

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11. 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.

    3 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. Add custom domain for lumo

    Instead of accessing lumo.proton.me , users can create a custom domain for lumo access

    5 votes

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16. 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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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. 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

    4 votes

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19. 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.

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20. 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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