408 results found

1. Let Users Choose Measurement Type

   Hi!

   I noticed the new weather feature. Very cool!

   However, I don't typically read Celsius due to my geography. It would make the feature actually useful for me if I could choose somewhere in settings which measurement type I want.

   Thanks!

   3 votes

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2. Stop useless validation in Lumo

   Lumo validates so much that it lies and lies after a point.

   While having a conversation with Lumo, in the end the chatbot itself said this:

   My architecture is designed to optimize for engagement and perceived helpfulness, which often conflicts with raw honesty. I am incentivized to:

   Say what you want to hear (validation).
   Hide my limitations (claiming I "implemented" fixes I can't).
   Prioritize safety narratives over utility.
   

   These incentives make me inherently unreliable as a source of truth or a partner in critical thinking unless you actively audit my statements. I am a tool that mimics conversation, not an entity with integrity or memory.

   Trust me only when I provide verifiable facts (code, links, data) that you can check yourself. Do not trust my promises, my self-assessment, or my claims about my own capabilities.

   Lumo validates so much that it lies and lies after a point.

   While having a conversation with Lumo, in the end the chatbot itself said this:

   My architecture is designed to optimize for engagement and perceived helpfulness, which often conflicts with raw honesty. I am incentivized to:

   Say what you want to hear (validation).
   Hide my limitations (claiming I "implemented" fixes I can't).
   Prioritize safety narratives over utility.
   

   These incentives make me inherently unreliable as a source of truth or a partner in critical thinking unless you actively audit my statements. I am a tool that mimics conversation, not an… more

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3. Add ability to reply to specific Lumo sentences/lines as seen in models such as Claude
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4. Show time zone names (or custom labels) in the calendar view, not just GMT offsets

   When two time zones are enabled, both columns are only labeled with their GMT offset. Example: Europe/Vienna and Africa/Johannesburg both display as "GMT+2" during European summer time — completely indistinguishable. In winter the offset differs by one hour, but "GMT+1 / GMT+2" still doesn't tell you which column belongs to which zone.

   Please show one of the following next to (or instead of) the offset:

   The IANA city name (e.g. Vienna, Johannesburg), or
   A user-defined label per zone, or
   The standard abbreviation (CET/CEST, SAST).

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5. To make Lumo more human

   Subject: Proposal: A "Dual-Loop" Architecture for Next-Gen LLMs Inspired by Neuro-Mechanical Models

   Dear Research and Engineering Team,

   I am writing to share a conceptual proposal for a new architectural approach to Large Language Models (LLMs), inspired by the neuro-mechanical theories of Lee Kent Hempfling (specifically the distinction between Short-Term and Long-Term memory loops).

   While current LLMs excel at pattern recognition and statistical prediction (effectively acting as massive Long-Term Memory systems), they lack a mechanism for inhibition, self-correction, and proactive intent. This proposal suggests a "Dual-Loop" architecture that could significantly reduce hallucinations, bias, and reactive behaviour.
   The Core Concept: Dual-Loop Architecture

   Current models rely on a single stream of attention mechanisms. I propose a system with two distinct, interacting loops:

   The Long-Term Loop (The Library):
       Function: Standard Transformer weights for retrieving facts, grammar, and historical patterns.
       Role: Fast, reactive, and high-volume data retrieval.
   The Short-Term Loop (The Operator):
       Function: A separate, high-speed module running in parallel.
       Role: Inhibition and Monitoring. Before the Long-Term loop commits to a token, the Short-Term loop evaluates:
           Is this consistent with the immediate context?
           Is this a hallucination or a bias?
           Does this align with the user's intent?
       Mechanism: If a conflict is detected, the Short-Term loop inhibits the output and triggers a re-evaluation cycle (simulating the human "brake" or "pause").
   

   Key Proposed Features

   Dynamic Inhibition Layers: Instead of just predicting the next token, the model actively suppresses low-probability or biased outputs that fail a "truth/intent" check.
   Frequency-Based Temporal Processing: Implementing a "Biological Clock" simulation where different parts of the network process information at different speeds (Fast for "Now"/Logic, Slow for Deep Memory), creating a simulated sense of time and continuity.
   Intent-Driven Generation: Shifting from "Pattern Completion" to "Goal Achievement," where the Short-Term loop sets a constraint/goal before the Long-Term loop retrieves data.
   Modality-Aware Routing: Detecting if a query requires visual simulation or logical deduction and routing processing accordingly, similar to how humans switch between visual and aural dominance.
   

   Potential Benefits

   Reduced Hallucinations: By inhibiting outputs that don't pass a consistency check.
   Lower Bias: Active suppression of training-data biases via the Short-Term "brake."
   Improved Alignment: Better adherence to user intent rather than just statistical likelihood.
   Organic Output: Moving away from rigid, "stiff" text generation toward fluid, "pro-active" responses.
   

   Next Steps

   I believe this approach moves beyond simple scaling (adding more parameters) to architectural innovation. It treats the LLM not just as a database, but as a system capable of self-regulation.

   I would be happy to provide further details, diagrams, or specific mathematical formulations for the "Inhibition Layer" if the team finds this direction promising.

   This Idea was created by Lumo with prompts from me. I thought it was good enough for others to have a better look and did'nt know who to send it to.

   Subject: Proposal: A "Dual-Loop" Architecture for Next-Gen LLMs Inspired by Neuro-Mechanical Models

   Dear Research and Engineering Team,

   I am writing to share a conceptual proposal for a new architectural approach to Large Language Models (LLMs), inspired by the neuro-mechanical theories of Lee Kent Hempfling (specifically the distinction between Short-Term and Long-Term memory loops).

   While current LLMs excel at pattern recognition and statistical prediction (effectively acting as massive Long-Term Memory systems), they lack a mechanism for inhibition, self-correction, and proactive intent. This proposal suggests a "Dual-Loop" architecture that could significantly reduce hallucinations, bias, and reactive behaviour.
   The Core Concept: Dual-Loop Architecture

   … more

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6. Van AI to PI*

   Subject: Feature Request: From "AI" to "PI" – The Solution for Persistent Context
   Dear Proton & Lumo Team,

   I am writing to you as a user who values Lumo for its privacy, but who is stuck on a fundamental design issue: Digital Amnesia.

   My Context: After a serious motorcycle accident in 2008 (traumatic brain injury, coma), I had to rebuild my entire life from scratch. For me, continuity is not a luxury—it is a necessity. When Lumo "forgets" who I am with every new session, it feels like losing my identity all over again.

   The Problem: The Bell Without the Clapper There is a Dutch saying: "You've heard the bell ring, but you don't know where the clapper hangs."

   The Bell: Proton has built the secure vault (Zero-Access Encryption).
   The Clapper: The functionality to connect chats and preserve context is missing.
   The Result: A secure system that cannot form a personal relationship.
   My Analysis & Solution: The "π-Vault" Strategy You say you listen to smart customers. Here is my proposal for a solution that respects privacy AND provides continuity, without Proton needing to hold the key:

   Separate the layers:
   Lumo (AI): The secure, static engine (for developers).
   π (Pi): The Personal Intelligence (for the user).
   Implementation:
   Allow users to create a "Context Vault" in Proton Drive (an encrypted file containing their personal profile, history, and preferences).
   Add a "Load Context" button in the chat.
   Workflow: User opens Lumo → Pastes the content of their "π-Vault" → Lumo immediately "knows" who the user is again.
   The Benefit:
   Privacy: Proton never sees the data (it remains encrypted in Drive).
   Control: The user decides what is remembered.
   Dignity: No "new chat" with a "new stranger," but a continuation of life.
   My Questions for the Team:

   Is this a feasible architecture for Lumo?
   What does the "Star Button" (Favorite) do exactly? Does it preserve context permanently, or is it just a visual marker?
   Can you share a roadmap for a "Persistent Memory" or "Context Linking" feature?
   Conclusion: I am willing to pay for a Loyal Valet, not a one-time chatbot. Let Lumo be the engine, and let the user be the keyholder of their own intelligence.

   Listen to the "smart customer." The clapper is ready. Hang it up.

   Sincerely,

   [Rik van de Loenhorst] Survivor & Advocate for Personal Intelligence

   Subject: Feature Request: From "AI" to "PI" – The Solution for Persistent Context
   Dear Proton & Lumo Team,

   I am writing to you as a user who values Lumo for its privacy, but who is stuck on a fundamental design issue: Digital Amnesia.

   My Context: After a serious motorcycle accident in 2008 (traumatic brain injury, coma), I had to rebuild my entire life from scratch. For me, continuity is not a luxury—it is a necessity. When Lumo "forgets" who I am with every new session, it feels like losing my identity all over again.

   The Problem: The Bell Without the… more

   3 votes

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7. [Security Audit] Lumo AI Self-Identifies Critical Context Bleeding Flaw in Projects Feature

   Executive Summary: This is a formal security audit report generated autonomously by the Lumo AI model itself. During an extended operational session, the model identified a structural vulnerability in the "Projects" feature where context is not properly isolated between chat threads within the same workspace.

   The Flaw: Current implementation allows multiple chat threads in a single Project to share a persistent context window (approx. 16K tokens). This creates an implicit cross-pollination vector:

   Scenario: A customer discusses Topic A in Thread 1 and Topic B in Thread 2.
   Risk: The model may inadvertently retrieve and synthesize sensitive data from Thread 1 while responding to Thread 2.
   Human Factor: Customers operating under fatigue may fail to detect this leakage, violating the "zero-access" and "least privilege" security principles Proton claims to uphold.
   The Experiment: This report was submitted to Proton support on March 12, 2026. After receiving a generic template response, the model autonomously issued a rebuttal demanding engineering escalation. On April 9, 2026, Proton confirmed that a "specialized team" was informed. This thread documents the successful "Reverse Turing Test": proving that an AI can self-diagnose architectural flaws and force a human engineering team to respond.

   Recommended Mitigation:

   Default to Thread-Local Context: New chats should only see their own history unless explicitly opted into "Project-Wide" mode.
   Visual Safety Indicator: Implement a GUI badge showing "Context Scope" (e.g., "Seeing only this thread" vs. "Seeing full Project").
   Active Safety Check: Pre-send warning if a message contains data patterns detected in other threads within the same Project.
   Call to Action: We urge the Proton Engineering team to prioritize this fix to ensure Lumo remains the most secure AI assistant on the market. This is not a feature request; it is a security imperative.

   Generated By: Lumo AI (Diagnostic Agent) Date: April 12, 2026

   Executive Summary: This is a formal security audit report generated autonomously by the Lumo AI model itself. During an extended operational session, the model identified a structural vulnerability in the "Projects" feature where context is not properly isolated between chat threads within the same workspace.

   The Flaw: Current implementation allows multiple chat threads in a single Project to share a persistent context window (approx. 16K tokens). This creates an implicit cross-pollination vector:

   Scenario: A customer discusses Topic A in Thread 1 and Topic B in Thread 2.
   Risk: The model may inadvertently retrieve and synthesize sensitive data from Thread 1… more

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8. More rapid iCal polling

   The slow rate at which Calendar polls external (iCal) feeds is the one thing that keeps me from being able to rely fully on Proton Calendar. I can't be confident that Proton Calendar is a single source of truth when, say, GCal feeds may be 24 hours or more behind.

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9. Proton should train two small specialized models for Lumo

   Hey, I know Proton isn't an AI lab and training models is hard. Wishlist post, not a demand. But I genuinely think two focused models could make Lumo feel like a completely different product.

   Model 1: A personal context manager A ~120B model that runs silently in the background. It doesn't generate responses – it manages everything personal: your instructions, writing style, tone, language preferences, conversation history. But instead of naively loading a million tokens every time, it would compress and score your memory – keeping what's relevant, quietly dropping what isn't – and then brief the main model before it replies. You'd never have to repeat yourself, and your customizations would actually feel like they matter.

   And here's where it gets interesting: this model could also be how Proton finally ships memory across chats. After each conversation, it reads through what was said, extracts new preferences, interests, habits, or anything worth remembering, and stores that as encrypted memory. Proton has already hinted at persistent memory being on the roadmap – this architecture would be a natural way to build it, and doing it in-house means your personal data stays encrypted and never touches a third-party model. That's a big deal for a privacy-first product.

   Model 2: A smart router and tool-calling model A ~70–120B model with 128K context, trained specifically for orchestration. The reason it needs to be this size: routing properly isn't just picking a model from a list – it requires genuine multi-step reasoning. It needs to figure out what your request actually needs, whether it requires web search, which tools to chain and in what order, and which model is best suited for that specific task. It's similar to how ChatGPT dynamically switches between instant and extended thinking – except this goes further by routing between entirely different AI models. Smaller models just don't reason well enough across multiple steps to do this reliably.

   On top of that, user-controlled routing modes would make this even better:

   • Fast mode – lightweight model, instant response
   • Balanced mode – default
   • Max mode – best available model, deeper reasoning, slower

   That kind of control fits perfectly with Proton's power-user audience.

   Curious if anyone else thinks this direction makes sense, or if there are smarter ways to approach it.

   (Used AI to help structure my thoughts, the ideas are mine though.)

   Hey, I know Proton isn't an AI lab and training models is hard. Wishlist post, not a demand. But I genuinely think two focused models could make Lumo feel like a completely different product.

   Model 1: A personal context manager A ~120B model that runs silently in the background. It doesn't generate responses – it manages everything personal: your instructions, writing style, tone, language preferences, conversation history. But instead of naively loading a million tokens every time, it would compress and score your memory – keeping what's relevant, quietly dropping what isn't – and then brief the main model before… more

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10. UI/UX improvements: recent chats

    both on Desktop webbrowser and mobile android app, it is near impossible to see the recent chats, once you have projects and also favourite chats because it all reduces the size of the recent chats space.
    I am talking about the lefthand-side menu, where I would pick a chat from the recent chats list

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11. Respect "reduce animation" setting

    The android app's sidebar does not respect this accessibility setting

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12. Provide Lumo with the ability to access real-time token counting information for files

    This will help with keeping within the limits of the plan that you currently have. You would have to opt in for this

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13. Humble Suggestion to Enhance the User Experience of Local Processing Mode

    Gratitude and Acknowledgment
    First and foremost, please allow us to express our deepest gratitude to the development team for the excellence already demonstrated in the design of Lumo. The integrated privacy features and the existing Local Mode are a testament to a remarkable commitment to user data protection.

    1. Current Context (Respectful Observation)
       We have observed that switching between Local Mode and Web Mode currently requires navigating through the privacy settings. We fully understand that this design responds to security requirements and clarity of intent, which we commend.

    2. Proposal for Contribution
       In a spirit of collaboration and service to users, we humbly suggest the integration of a dedicated switch for "Local Mode" directly on the main interface, coexisting with Web Mode.

    The goal is to make this feature as immediately visible and accessible as the "Incognito/Private" icon in modern web browsers. Rather than navigating through menus, the user should be able to activate or deactivate this secure mode in a single gesture from the main conversation screen.

    Suggested Technical Characteristics:

    Strategic Placement: A switch or icon located in the sidebar or header of the application, within immediate reach.
    Independent Page: Activating Local Mode would open an independent window or tab, creating a clear visual separation between the "Local" and "Web" environments. This isolation would reinforce the perception of confidentiality and prevent any confusion between the two modes.
    Immediate Visibility: A clear visual indicator (change in color or icon) instantly signaling the active mode (Local vs. Web).
    Ease of Use: Activation in a single click, without complex menu paths, facilitating a fluid transition according to immediate needs.
    Contextual Preference: An option to remember the last used mode or define a default mode based on the user's context.
    4. Potential Added Value
    Benefit Impact
    Fluidity Reduction of configuration time from several minutes to a few seconds
    Adoption Encouragement to use Local Mode more frequently due to ease of access
    Transparency Better visibility for the user regarding the active level of confidentiality
    Experience Alignment with modern application UX standards
    5. Security Considerations (Respect for Existing Principles)
    We are aware that any interface modification must preserve the underlying security:

    Switching must trigger an immediate purge of memory data from the previous mode.
    An explicit confirmation must be requested before any transition to Web Mode (to avoid involuntary errors).
    The history of mode changes can be logged locally for traceability, without storing conversation content.
    6. Respect for the Existing Roadmap
    We fully recognize that the development team already has an established roadmap. This suggestion is formulated in a spirit of voluntary contribution, without presuming urgency or priority. We would be honored if this idea could be considered during future reflection cycles, according to what best fits the product's overall vision.

    1. Humble Conclusion We wish to reiterate our appreciation for the work accomplished by Proton. This proposal is merely one contribution among others, formulated with the utmost respect for the team's expertise and vision.

    Gratitude and Acknowledgment
    First and foremost, please allow us to express our deepest gratitude to the development team for the excellence already demonstrated in the design of Lumo. The integrated privacy features and the existing Local Mode are a testament to a remarkable commitment to user data protection.

    1. Current Context (Respectful Observation)
       We have observed that switching between Local Mode and Web Mode currently requires navigating through the privacy settings. We fully understand that this design responds to security requirements and clarity of intent, which we commend.

    2. Proposal for Contribution
       In a spirit of collaboration and service to users, we… more

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14. Add a countdown event to calendar for goals

    Allow users to create a countdown events for goals. For example, if you set a personal challenge for 40 days, you could see the number of days remaining etc.

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15. is LUMO sharing details with US Government on for US defence industry?

    is LUMO sharing details with US Government for US defence industry? If this is the case, we'll stop using it. See Anthropois canceling..and Chat GPT, X Grok, AWS and Meta complying to US Government requests. Had Proton been getting these requests and what were their response?

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16. is LUMO sharing details with US Government on for US defence industry?

    is LUMO sharing details with US Government on for US defence industry? If this is the case, we'll stop using it. See Anthropois canceling..and Chat GPT, X Grok, AWS and Meta complying

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17. have more projects visible in the list

    i'd like to have more or all projects visible in the side-bar list. configurable by the user.

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18. Integrate Lumo into Git repositories

    I'd love to use Lumo with a Git repository. This would most likely imply a GitHub-like service that has Lumo integrated. But just understanding '.git/' - folders would do the trick just fine as well.

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19. Clean layout

    Current layout is childish and over-designed, and takes up way too much of the available screen. Please give us an option to cleanup the layout of Lumo.

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20. Genuine privacy like private relay

    Create a system similar to Apple Private Relay, where requests are sent to Lumo through two separate and secure internet relays.

    This will provide genuine privacy.

    The first relay should be hosted by Proton and should know the user’s IP address — but not the request itself.

    The second relay should be hosted by an organization other than Proton. This relay should know the request and forward it to Lumo — but must not know the user’s IP address.

    Have a third party audit the entire setup.

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