372 results found

1. Answers on Top instead of Bottom

   I would like to suggest, for answers to appear on top of a thread instead on the bottom. Instead of scrolling through the history from top to bottom, new answers appear on top. Should I want to reread the hlstory I can do so top to bottom Especially for longer threads that you would like to continue it would be more helpful.

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2. Let the cat learn to speak!

   Add an option where the cat starts reading the answer aloud through the speaker when we enter the message via microphone. This has the small effect of allowing children to grow up with AI in a privacy-compliant manner and give free rein to their imagination, developing stories that the cat can then read aloud. The cat can already write and develop stories, but it still can't read.

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3. Catnip mode: an expert mode for advanced users (ie ‘go crazy’ basically)

   I know I’m missing other features and whatnot, but hopefully this idea is somewhat helpful.

   So…catnip mode. When toggled on (default off) in a new Developer Settings menu, this mode will unlock additional parameters:

   Catnip/developer setting panel:
   - Toggle proton system prompt instructions off (default on), allowing a custom instruction set (open weights by design have native guardrails as is, reduce overhead)
   - Ideally, the developer setting should allow us to see the system prompt that Proton uses (increase transparency)

   Lumo chat area:
   - Ability to explicitly select models
   - Brief description of model, recommended use when hovering over model selection
   - Ability to see open model weights (increase transparency)
   - Conversation/session context
   - Ability to see token use, limit/remaining tokens

   Model Panel:
   - Advanced parameters and profiles
   - Create up to 5 profiles per model
   - Personalization prompt
   - Temperature, P, K, penalties, A, etc.
   - Response format
   - Max output tokens

   API:
   - Add any of the above features that may not currently be available in the API being developed (most notably specific model selection and system prompt)
   - Current GitHub as of posting has model IDs: auto, lumo-fast, lumo-thinking

   The intent is to leave the current environment for general users, however, open it up to advanced users looking for serious use cases.

   I know I’m missing other features and whatnot, but hopefully this idea is somewhat helpful.

   So…catnip mode. When toggled on (default off) in a new Developer Settings menu, this mode will unlock additional parameters:

   Catnip/developer setting panel:
   - Toggle proton system prompt instructions off (default on), allowing a custom instruction set (open weights by design have native guardrails as is, reduce overhead)
   - Ideally, the developer setting should allow us to see the system prompt that Proton uses (increase transparency)

   Lumo chat area:
   - Ability to explicitly select models
   - Brief description of model, recommended use when hovering over model… more

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4. Add Voxtral as an LLM in Lumo

   I’m an enthusiastic user of Lumo and appreciate the privacy‑first approach you’ve built into the assistant. I noticed that Lumo already incorporates several open‑source large language models, including Mistral, which demonstrates your willingness to adopt cutting‑edge, community‑driven models.

   Given the impressive performance and open‑source nature of Voxtral, I’d like to kindly request that you consider adding it to Lumo’s model roster. Integrating Voxtral would further diversify the options available to users, potentially improving multilingual capabilities and offering additional flexibility for specialized workloads—all while staying aligned with Lumo’s privacy and security standards.

   Thank you for continuously improving Lumo, and I look forward to seeing Voxtral (or other emerging models) become part of the platform.

   I’m an enthusiastic user of Lumo and appreciate the privacy‑first approach you’ve built into the assistant. I noticed that Lumo already incorporates several open‑source large language models, including Mistral, which demonstrates your willingness to adopt cutting‑edge, community‑driven models.

   Given the impressive performance and open‑source nature of Voxtral, I’d like to kindly request that you consider adding it to Lumo’s model roster. Integrating Voxtral would further diversify the options available to users, potentially improving multilingual capabilities and offering additional flexibility for specialized workloads—all while staying aligned with Lumo’s privacy and security standards.

   Thank you for continuously improving Lumo, and I look… more

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5. Private Lumo App Store for sharing & monetizing custom “MyLumos” (GDPR-safe alternative to GPT Store)

   Hi Proton Team and Community,

   first of all, thank you for creating Lumo — a privacy-first AI assistant that finally allows European users and businesses to use AI without compromising confidentiality or GDPR compliance. Proton has solved the fundamental problem that all other AI platforms struggle with: trust.

   Now that Lumo exists, I would like to propose the next major step:

   ➡️ A Private Lumo App Store for creating, sharing, and monetizing custom “MyLumos”.

   Just like OpenAI has custom GPTs (and a GPT Store), Proton could offer a secure and encrypted alternative — fully compliant, private, and built inside the Proton ecosystem.

   ---

   1. What this feature would enable

   Users could create their own AI assistants (“MyLumos”) — for example:

   • productivity assistants
   • privacy-focused advisors
   • business workflow helpers
   • educational bots
   • interactive story or game bots
   • niche expert assistants in specific fields

   …and then share them securely with:

   • the entire Proton community,
   • selected groups,
   • teams within a business,
   • or even privately via Proton Drive.

   All without leaving the Proton environment and without any data ever going to third parties.

   This would be the first truly GDPR-compliant AI app ecosystem, something no US-based provider can offer today.

   ---

   2. Why Proton is the perfect place for this

   Other platforms store user chats on US servers, often unencrypted, often used for training, and not protected against government access. This makes serious use cases (business, legal, financial, medical) impossible.

   Lumo is already the solution:

   • End-to-end encrypted
   • No server-side logs
   • Not used for model training
   • Runs in EU-based infrastructure
   • Built by a privacy-focused, community-funded organization

   A Lumo App Store would expand this into a full secure AI ecosystem for Europe.

   ---

   3. Monetization (optional for creators)

   To motivate creators and allow quality apps to thrive, Proton could offer:

   Option A: Ad-supported free usage

   Creators can enable a small, privacy-friendly ad banner (no tracking, no profiling).
   Revenue is shared between Proton and the creator.

   Option B: Remove ads

   Users can pay a small one-time fee or subscription to unlock ad-free usage.
   Pricing is set by the creator.

   This creates a win-win-win situation:

   • Proton receives sustainable income (without surveillance)
   • Creators are rewarded for their work
   • Users can choose between free or premium

   All inside a privacy-respecting system.

   ---

   4. Why this matters

   This would give Europe its first:

   • privacy-first AI marketplace
   • GDPR-compliant platform for business AI
   • safe space for sharing creative and educational AI tools
   • secure alternative to OpenAI’s GPT Store
   • environment where users don’t have to fear data extraction or third-party access

   As a creator myself (building tools like interactive privacy games and business assistants), I would immediately move everything to Proton Lumo if such a store existed — especially for sensitive applications that cannot run on OpenAI for compliance reasons.

   ---

   5. Final statement

   A private Lumo Store would make Proton the leading AI platform for privacy-conscious users, European companies, and creators. It would fill the gap where all other AI platforms fail:
   Privacy, compliance, trust, and transparency.

   I truly believe this could become one of Proton’s most impactful innovations — and I would love to support and contribute to it.

   Thank you for considering this idea!
   Proton + Lumo + Community = 🚀

   Hi Proton Team and Community,

   first of all, thank you for creating Lumo — a privacy-first AI assistant that finally allows European users and businesses to use AI without compromising confidentiality or GDPR compliance. Proton has solved the fundamental problem that all other AI platforms struggle with: trust.

   Now that Lumo exists, I would like to propose the next major step:

   ➡️ A Private Lumo App Store for creating, sharing, and monetizing custom “MyLumos”.

   Just like OpenAI has custom GPTs (and a GPT Store), Proton could offer a secure and encrypted alternative — fully compliant, private, and built inside… more

   4 votes

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6. Transcription of long audio STT

   It would be useful to be able to use Lumo to transcribe long audio, for example from podcasts, educational audio, etc.

   4 votes

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7. 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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8. lumo.ai for quick access > lumo.proton.me

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

   5 votes

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9. 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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10. How about ditching these stupid time limited upgrade offers?

    Sick of seeing them,desperate and bloody annoying!

    2 votes

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11. 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

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12. 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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13. Apple Shortcuts

    Can we please get Apple Shortcuts for Lumo :D

    I’d love to migrate my current automation away from GPT

    6 votes

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14. 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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15. 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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16. 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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17. 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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18. 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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19. 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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20. 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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