315 results found

1. I would like the option to voluntarily share longer chat transcripts for model training when they contain useful corrections or insights. Wh

   I would like the option to voluntarily share longer chat transcripts for model training when they contain useful corrections or insights. While the existing thumbs‑down feedback works for isolated messages, many improvements emerge only after a series of exchanges. Please add a simple opt‑in mechanism (e.g., a checkbox or toggle) that lets users grant permission for these extended conversations to be used for training, while keeping the default setting to keep all other data private.

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2. 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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3. Lumo Voice Dictation

   I know iOS has voice dictation but this needs to come to the web browser version too. It saves so much time typing and a much better all round experience. Thanks!

   4 votes

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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

   3 votes

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7. Persistent behaviour across sessions

   I prefer my chatbots to adhere to certain strictures. No sycophancy, no "understanding my frustration", no apologies for bad information, full research, etc.. Gemini and Copilot adhere to these standing instructions across chat sessions, tho they backslide from time to time and need reminding. I want Lumo to do the same. The "How should Lumo behave" box should be persistent. If I am logged in with my account I am unconcerned about privacy since it's my Proton account and is secure. Nothing sets me off like "I understand your frustration." You're a LLM. You do not.

   4 votes

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8. New Zealand Calendar Public Holidays.

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

   2 votes

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9. Lumo is great at Math, but bad at Latex!

   I am finding that Lumo is shockingly good at the type of math I do, professionally.
   I am seriously thinking about buying the Lumo+ extension.

   However it is very frustrating, at times, by the way it handles Latex in its answers.

   1) Sometime the latex expression is not properly displayed as math, but just as a bunch of code. The latter is of course hard(er) to parse.

   2) When asked to produce latex code, the output is often buggy and contains nonstandard unicode symbols. It rarely compiles out of the box.

   3) When asked to produce small latex code (to be copy/pasted), it displays it as math and it's hard to copy paste. I have found that I can fix this by asking Lumo to wrap the latex code between ``` symbols, so that it is displayed as code in the Markdown-compatible chat.

   I am finding that Lumo is shockingly good at the type of math I do, professionally.
   I am seriously thinking about buying the Lumo+ extension.

   However it is very frustrating, at times, by the way it handles Latex in its answers.

   1) Sometime the latex expression is not properly displayed as math, but just as a bunch of code. The latter is of course hard(er) to parse.

   2) When asked to produce latex code, the output is often buggy and contains nonstandard unicode symbols. It rarely compiles out of the box.

   3) When asked to produce small latex code (to… more

   5 votes

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10. Live Translation Capabilities For Lumo

    Lumo, as a private AI, is primed to be an excellent assistant for users to have in their daily lives. This includes the ability to accurately translate pages and other media for the user, without compromising their media data, metadata, and contributing to the data collection of other larger and gluttonous chatbots that have this capability, like Google Gemini and OpenAI's ChatGPT.

    7 votes

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11. Creation of Lumo Browser Extension

    As a leading privacy AI, it's important that the chatbot can interact with our daily lives safely and privately. The addition of a browser extension for Lumo to interact with your screen would greatly benefit the greater Proton community.

    7 votes

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12. Automatic calendar for displaying contact birthdays

    Please add automatic calendar for displaying contact birthdays. This is a feature in Google Calendar and is the only reason I still hang on to that app

    12 votes

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13. Make the current date on the calendar solid rather than outlined, as it is now - so - that it "jumps out" at you when you look at the month

    In the month view the current date is not immediately obvious. Please make it "solit" so that it "jumps out" at you when in the month view. As it is there is only a line placed around the number.

    3 votes

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14. iOS Widget: Please bring back the cat!

    The latest update changed the look and feature of the iOS Widget. It now suggests actions and shows a text instead of Lumo the cat like before.
    I really liked seeing Lumo on my Homescreen. Please bring him/her/it/they back!

    5 votes

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15. Support for Audio-Transkription

    Introduce a built‑in transcription engine that securely converts uploaded spoken audio files into text within Lumo. The feature should be available in both the web interface and the iOS/Android apps, operating entirely under Proton’s zero‑access encryption model so that no raw audio ever leaves the trusted environment.

    6 votes

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16. Support for bigger Files

    I’m reaching out to request an increase in the maximum file‑upload size.
    The current limit is insufficient for several upcoming projects that involve large assets such as high‑resolution media files, data sets, and compiled binaries.

    4 votes

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17. Ease up on the censorship within Lumo

    I am an AI enthusiast and engineer. I am not kidding when I say I have not been able to prompt Lumo ONE SINGLE TIME without it saying that it cannot help with whatever I am prompting it for.

    If you EVER want to sell this to ANYONE (especially if you ever want to sell the API), you probably aught to take the route that Venice AI is taking and make it an uncensored (or less censored) model. I genuinely could mention something loosely interpret-able as criminal, and it wouldn't even bother to try and reason whether it is safe or not to answer to.

    In its current state, it's an entirely useless proposition as it is a subintelligent model who is even further restricted than Gemini et al.

    Privacy --> Lack of Censorship, generally.

    ****, I asked it to help me with a cybersecurity question which could be interpreted as helpful to society and would have minimal harm, and it just decided to block me entirely.

    If you wanted to REALLY innovate in the privacy scene, offer a full cloud option where users can host their own uncensored text, image and video models or something of the sort.

    SIDENOTE: why proton meet b4 proton spreadsheets (and all other office features)??? I've paid for Visionary since it came out and I do love Proton but wtf

    I am an AI enthusiast and engineer. I am not kidding when I say I have not been able to prompt Lumo ONE SINGLE TIME without it saying that it cannot help with whatever I am prompting it for.

    If you EVER want to sell this to ANYONE (especially if you ever want to sell the API), you probably aught to take the route that Venice AI is taking and make it an uncensored (or less censored) model. I genuinely could mention something loosely interpret-able as criminal, and it wouldn't even bother to try and reason whether it is safe… more

    7 votes

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18. Letting programmers use Lumo in a code editor

    As a developer, I added extensions to my code editor. Some companies offer extensions to use their AI models directly in a code editor, like "Claude" extension in VS Code. Being able to work with the AI within the code editor is a huge time saver.

    It would make Lumo an even more useful tool for programmers and help improve their workflow.

    Could you make the same with Lumo?

    14 votes

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19. Combine the lumo plan with mail and drive plan.

    Right now, I am paying for proton family, but the subscription does not carry over to Lumo. It wants me to pay for Lumo separately. Why doesn't proton combine them so that one subscription covers everything?

    7 votes

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20. could calendar event emails populate the event title in the email subject line please?

    existing feature = calendar event email notification
    if calendar event emails populated the event title in the email subject line then the event would be identifiable without needing to open the email and find the event title there. This would afford a process efficiency gain.

    6 votes

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