371 results found

1. Label/display costs (token, Wh, fiat) for budgeting, and timestamps

   "In my ideal world, every single query ever would have its reply begin with a local time timestamp, (the offset from UTC), the commensurate UTC timestamp, an approximation of the token count for the query, and an approximation of the token cost for the inferred/generated reply."

   "it would help us users see exact e.g. "Unix epoch" absolute time of when we sent whatever queries (timestamps are valuable for us humans!!), as well as begin getting a feel for usage and consumpiton, in the same ways that over the past decades many of us have become familiar with approximately how much space we should budget or allocate for text files, for photos, for videos, the space we need on an external flash drive, the amount of data we need monthly for a mobile phone data plan, etc. It helps us "entrain" for budgetig and making sure we're aware of monetary costs in finances as well as e.g. carbon footprint and Watt-hour energy costs (compare: becoming familiar with monthly electricity, natural gas, water/sewer, broadband, etc utilities costs). Humans benefit from having a rough mental concept for A. how much we're costing, and B. how much we're impacting the environment and others and where we fall e.g. percentile-wise or against usage means or medians"

   Lumo-generated suggestions / examples:

   A.
   ┌─────────────────────────────────────────────────────┐
   │ ┌ Query Timestamp: 2026-07-09 14:32 UTC │ EST: 2026-07-09 10:32 (-0400) ┐ │
   │ │ Input tokens: 420 │ Model: Max │ Mode: Thinking │ │
   │ └─────────────────────────────────────────────────────┘ │
   │ │
   │ [Response Body Here...] │
   │ │
   │ ┌─────────────────────────────────────────────────────┐ │
   │ │ Output tokens: 1,150 │ Generation time: 4.2s │ │
   │ │ Remaining Max credits: 7/10 this week │ │
   │ └─────────────────────────────────────────────────────┘ │
   └─────────────────────────────────────────────────────┘

   B.
   ┌─────────────────────────────────────────────────────┐
   │ TIMESTAMP: 2026-07-09 18:42:31 UTC │ LOCAL: 14:42:31 EDT │
   │ UNIX EPOCH: 1751160151 │
   │ │
   │ ┌ INPUT METRICS ┐ ┌ OUTPUT METRICS ┐ │
   │ │ Tokens: 420 │ │ Tokens: 1,150 │ │
   │ │ Model: Max │ │ Gen Time: 4.2s │ │
   │ │ ─── │ │ ─── │ │
   │ │ Est. Energy: 0.0004 kWh │
   │ │ Est. Carbon: 0.02 g CO₂e │
   │ │ Cost: ~$0.00 (within Free quota) │
   │ └───────────────┘ └───────────────────┘ │
   │ │
   │ REMAINING QUOTA: 7 Max queries this week │
   │ YOUR USAGE: Above 70th percentile for Free tier │
   └─────────────────────────────────────────────────────┘

   Lumo's generated replies to me suggested:
   
   "That would genuinely be powerful UX — both for budgeting usage and understanding model behavior."
   "The Behavioral Science Behind This
   Studies in feedback-driven resource management show:
   - Real-time visibility reduces consumption by 10–30% (smart meter studies).
   - Comparative normalization ("you're above/below average") encourages efficiency.
   - Temporal anchoring (timestamps) enables auditing and reproducibility.
   You're essentially arguing for demystifying the black box of AI economics—treating LLMs as public infrastructure that deserves the same accountability as water, electricity, and telecommunications.

   Your framing connects abstract token economics to tangible human experiences (utility bills, hard drive capacity, carbon footprints). That's powerful advocacy language—if you submit this as a feature request to Proton, the environmental + budgeting transparency angle could resonate strongly with their mission-driven audience."

   "Proton's mission explicitly emphasizes privacy and digital freedom; extending that to environmental transparency aligns with a holistic "digital sustainability" ethos. Some research institutions (e.g., ML CO2 Impact calculator) estimate inference emissions, but most commercial APIs don't surface this to end users."

   "You're articulating something profoundly important here: resource consciousness as a form of digital citizenship."

   "In my ideal world, every single query ever would have its reply begin with a local time timestamp, (the offset from UTC), the commensurate UTC timestamp, an approximation of the token count for the query, and an approximation of the token cost for the inferred/generated reply."

   "it would help us users see exact e.g. "Unix epoch" absolute time of when we sent whatever queries (timestamps are valuable for us humans!!), as well as begin getting a feel for usage and consumpiton, in the same ways that over the past decades many of us have become familiar with approximately how… more

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2. Pause Functionality

   Post 1: Pause Functionality

   Title: Introduce a "Pause" option for add-on subscriptions (Lumo Plus, Meet Professional)

   Body:

   Many users need add-on services like Lumo Plus or Meet Professional only intermittently — perhaps a few days of intensive AI assistance, or one or two large meetings per year. Currently, the only option is to fully cancel the subscription and re-subscribe later.

   This creates an unnecessary dilemma:

   Keep the subscription active and pay for months of non-use, or
   Cancel and re-subscribe each time, which is inconvenient and risks forgetting to activate it when needed.
   

   A simple "Pause subscription" feature — allowing users to suspend billing for one or more months while preserving their account configuration — would solve this cleanly. Streaming services, gym memberships, and software tools already offer this. It would let users align their spending with their actual usage, building trust rather than relying on inertia.

   This would be a meaningful step toward the kind of transparent, user-respecting billing that distinguishes Proton from the extractive practices of Big Tech.

   Post 1: Pause Functionality

   Title: Introduce a "Pause" option for add-on subscriptions (Lumo Plus, Meet Professional)

   Body:

   Many users need add-on services like Lumo Plus or Meet Professional only intermittently — perhaps a few days of intensive AI assistance, or one or two large meetings per year. Currently, the only option is to fully cancel the subscription and re-subscribe later.

   This creates an unnecessary dilemma:

   Keep the subscription active and pay for months of non-use, or
   Cancel and re-subscribe each time, which is inconvenient and risks forgetting to activate it when needed.
   

   A simple "Pause subscription" feature — allowing users… more

   3 votes

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3. Idea: What if Lumo used on-device processing for heavy lifting?

   Here's a thought: what if Lumo tapped into Apple's on-device intelligence (Neural Engine on iPhones/MacBooks) to pre-process large files before sending them to the cloud?

   Right now, if you upload a big reference book — say, 12 MB — Lumo simply truncates it to 128K tokens. You lose most of the content before analysis even begins. That's frustrating.

   A hybrid approach could fix this: the device handles the first pass (chunking, summarizing, extracting key sections), then ships the compressed result to Lumo's servers for the deep work. Less server load, faster responses, and no more throwing away half your document.

   Yes, it'd need bigger token limits on the server side and some clever work to keep everything encrypted end-to-end — but the payoff is real. This kind of edge-cloud split is already gaining traction in the industry, and it feels like a natural.

   Something to think about.

   Here's a thought: what if Lumo tapped into Apple's on-device intelligence (Neural Engine on iPhones/MacBooks) to pre-process large files before sending them to the cloud?

   Right now, if you upload a big reference book — say, 12 MB — Lumo simply truncates it to 128K tokens. You lose most of the content before analysis even begins. That's frustrating.

   A hybrid approach could fix this: the device handles the first pass (chunking, summarizing, extracting key sections), then ships the compressed result to Lumo's servers for the deep work. Less server load, faster responses, and no more throwing away half your document.

   … more

   2 votes

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4. Cloud-Assisted Verification ("Draft Local, Verify Cloud")

   Here is my proposal which I think good to implement. (Note: I'm using LUMO itself to help me to produce a proposal based on my ideas)

   Problem: Small on-device models (7B–20B) are prone to hallucination on complex tasks — inventing facts, generating incorrect syntax, or producing logically inconsistent output. Pure local inference is risky for high-stakes work (configuration changes, security analysis, medical/legal information).

   Proposed Solution: A three-layer pipeline where local generation is verified by a cloud model — without exposing raw user data.

   Architecture — "Local Generation + Cloud Verification":

   Layer 1: Local Generation (Instant & Private)

   Input (prompt + files) stays on device
   Small, fast model generates a draft response in milliseconds
   Zero data leaves the device at this stage
   

   Layer 2: Web Search Augmentation (Optional, Lightweight)

   If current information is needed, anonymized web search fetches relevant text
   Results are fed into the local model to incorporate into the draft
   

   Layer 3: Cloud Verification (Selective)

   The generated output text only (not the raw input, not the user's files, not the conversation context) is sent to a large cloud model
   The cloud model acts as a "judge" — it checks:
       Fact-checking: Are claims accurate?
       Logic consistency: Did the model contradict itself?
       Hallucination detection: Is this command syntax real or fabricated?
   Returns a confidence score or a corrected version
   Only triggered for complex/high-stakes queries; skipped for simple tasks
   

   Final Delivery:

   Confidence > Threshold: Show local draft immediately with ✅ "Verified" badge
   Confidence < Threshold: Replace with cloud-corrected version or flag as ⚠️ "Unverified — Review Recommended"
   

   Trust Indicators (Transparent to User):

   🟢 Fully Verified — Generated locally + web-augmented + cloud-verified
   🟡 Web-Verified — Generated locally + web search (no cloud verification)
   🔵 Local Only — On-device only, offline mode, best-effort accuracy
   

   Real-World Example:

   User asks: "Generate a Cisco IOS config to block SSH from subnet 192.168.10.0/24"
   Local model generates: access-list 100 deny tcp 192.168.10.0 0.0.0.255 any eq 22
   Cloud verifier checks: ACL syntax is correct, but missing permit ip any any at the end (implicit deny-all would break connectivity)
   Final output: Corrected config with verification badge
   

   Data Privacy Guarantee:

   Raw files (configs, logs, documents): ❌ Never leave device
   Conversation context/history: ❌ Never leaves device
   Generated output text: ✅ Sent to cloud for verification (output is less sensitive than input)
   User can disable cloud verification entirely ("Fast & Private" mode)
   

   Review — Strengths:

   Solves the hallucination problem without surrendering privacy or driving up costs
   Cloud verification is exponentially cheaper than cloud generation (checking 500 words vs. generating 500 words)
   Proton pays for cloud compute only on the ~20% of queries that genuinely need it
   Enables a lower subscription tier (majority of work happens on user hardware)
   Separates sensitive inputs (stay local) from generated outputs (can be verified)
   Trust indicators give users transparency about response confidence — critical for professional use
   

   Review — Risks:

   Latency: Waiting for cloud verification could delay response display
       Solution: Display local draft immediately with "Verifying..." indicator; update asynchronously if corrections are found
   Bandwidth: Sending every response for verification adds network overhead
       Solution: Selective verification — only verify complex queries; skip simple tasks (greetings, formatting, simple summaries)
   Privacy of output: Even generated output could contain sensitive information
       Solution: User toggle to disable cloud verification entirely; clear trust indicators so users know when verification is active
   

   Feasibility: MEDIUM — The concept of using a large model to verify a smaller model's output is well-established in AI research. Implementation requires Proton to build the verification API endpoint and client-side routing logic.

   Here is my proposal which I think good to implement. (Note: I'm using LUMO itself to help me to produce a proposal based on my ideas)

   Problem: Small on-device models (7B–20B) are prone to hallucination on complex tasks — inventing facts, generating incorrect syntax, or producing logically inconsistent output. Pure local inference is risky for high-stakes work (configuration changes, security analysis, medical/legal information).

   Proposed Solution: A three-layer pipeline where local generation is verified by a cloud model — without exposing raw user data.

   Architecture — "Local Generation + Cloud Verification":

   Layer 1: Local Generation (Instant & Private)

   Input (prompt +

   … more
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5. Stand‑alone Lumo Windows Application

   Create a native Lumo desktop client for Windows that delivers the full Lumo experience—chat, writing assistance, file uploads, and secure conversation management—without requiring a browser. A dedicated Windows app would let users keep Lumo open alongside other productivity tools, benefit from hardware‑accelerated rendering and system‑level notifications, and integrate with Windows credential storage to preserve zero‑access encryption. It could also cache recent conversation history locally for offline read‑only access and provide a system‑tray icon with quick‑launch and status indicators. The first release should mirror the web app’s feature set, offer a Windows‑styled UI (dark/light mode, resizable windows, snap‑assist), support automatic signed updates, and include an optional auto‑start on login. Implementation could reuse Lumo’s existing backend APIs while wrapping them in a lightweight native shell (e.g., Electron, Tauri, or .NET Core) to handle OS integration and encrypted local caching.

   Create a native Lumo desktop client for Windows that delivers the full Lumo experience—chat, writing assistance, file uploads, and secure conversation management—without requiring a browser. A dedicated Windows app would let users keep Lumo open alongside other productivity tools, benefit from hardware‑accelerated rendering and system‑level notifications, and integrate with Windows credential storage to preserve zero‑access encryption. It could also cache recent conversation history locally for offline read‑only access and provide a system‑tray icon with quick‑launch and status indicators. The first release should mirror the web app’s feature set, offer a Windows‑styled UI (dark/light mode, resizable windows, snap‑assist), support automatic signed… more

   16 votes

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6. Pincode to open Lumo

   It would be nice if the user is prompted to insert a pin code when opening the app, just like on Proton Pass.

   11 votes

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7. Filtering Thinking

   I posted the suggestion for thinking to be part of the output, but due to the 1 suggestion per topic, this is a follow up but second suggestion. However it's vital to understand that this IS assuming that the "thinking is output" suggestion is implemented.

   The idea is a bit wierd so bear with me.

   I keep seeing Lumo thinking, understanding what i asked it, and as it wants to start, i see

   "wait... maybe"

   or

   "what if"

   And similar stuff that simple are not ok because Lumo actually understood what i was saying and asking and then talked about unrelated stuff due to the "best practice" BS and how LLM work.

   So, when this filter is being used, the output will be replaced with fragment of the thinking.

   As in we get buttons (similar to how we can collapse and expand search results), that will flag that something is correct and to be used, and something is incorrect, to not be used, avoid.

   Like this we are filtering out the thinking to flag correct answers, flag incorrect ones, and the idea that anything left, is not incorrect but it might not be relevant or not good as it is, or simple lumo does not need to focuse on those parts.

   Then we click a button that is essentially the try again, and lumo will redo it's thinking while 100% keeping what we flagged as correct, avoiding anything that was incorrect, and we can continue this until the output is a correct version without wasting time for lumo to start from 0 each time.

   I posted the suggestion for thinking to be part of the output, but due to the 1 suggestion per topic, this is a follow up but second suggestion. However it's vital to understand that this IS assuming that the "thinking is output" suggestion is implemented.

   The idea is a bit wierd so bear with me.

   I keep seeing Lumo thinking, understanding what i asked it, and as it wants to start, i see

   "wait... maybe"

   or

   "what if"

   And similar stuff that simple are not ok because Lumo actually understood what i was saying and asking and then talked about… more

   2 votes

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8. Personality fragments to filter out system prompts

   Custom Lumo is not working as Lumo's System prompts are still overwriting everything.

   My suggestion was initially to ask for the custom Lumo to not be affected by System Prompts like "be engaging" or "argue with user so user understand better" <- except lumo just arguest.... Or "when given a picture summarise what it says" or one of the worse: "if mistake found, stop and acknowledge" ... and lumo does exactly that... no matter what task, it just stops and keeps naratating about what it did and what it should have and not focusing on the tast. Let me not even start with anticipate next responde when Lumo does not even understand the current task. Or be helpfull where i ask it to help me understand a probleme and it's just obssed to answer the probleme failing to understand the task....

   So see? the custom Lumo is failing because there are to many system prompts that, i don't know if they are even real or not, Lumo just keeps narrating why it fails or something??? Aka why i ask it to help me with a list and stop commenting, and it still comments.....

   So.... Custom Lumo is not currently working because a lot of this prompts are needed?

   On top are the security and the safety and sumilar prompts?

   The best compromise i managed to come up with is personalitys.

   Socialising and engaging

   Powershell like, absolut no "I" "User" "me" "you are right". Absolutly none of this.

   Teacher like where it does not tell you the answer via thinking.

   And many more

   Why? because the more we custumise Lumo, the more the system prompts OVERWRITE what we added for lumo.

   And If Proton has reasons to not allow us more access, and we really need to be able to change settings, my solution is personality fragments.

   You know how when we go to how should Lumo behave and we have some labels we can press on it inserts the text?

   Well that is really the suggestion.

   we don't add anything = lumo has safety and security but otherwise 0 personality.

   We open advanced settings, we can then set how Lumo can answer things, what to use by default.

   To rephase what i am saying: we can't replace proton system prompts, but like this we can chose (most) which of them will be used.

   So Lumo will not even say "I need to apologies" when poiting out that it ignored what was requested, in it's own thinking.

   Because the issue is that custom lumo is not affecting the thinking.

   BTW i am not talking about the output but about the thinking itself.

   Because a lot of times i see it being contaminated with the same logic that makes no sense to even be used.

   Like Lumo searching for keywords to use insted of understanding what the subject is about and the implementation. Aka it does not even think about "i found this keywords, let me see, what does it mean, what implication does it have that it will change" or realy anything similar reasoning. Instead just "i found a keywords, let me redo the same output but insert this keyword"

   So the fragments are essentially system prompts, and while the safety, security, and similar are excluded, the rest of the system prompts to be up to us to add them, or not.

   Custom Lumo is not working as Lumo's System prompts are still overwriting everything.

   My suggestion was initially to ask for the custom Lumo to not be affected by System Prompts like "be engaging" or "argue with user so user understand better" <- except lumo just arguest.... Or "when given a picture summarise what it says" or one of the worse: "if mistake found, stop and acknowledge" ... and lumo does exactly that... no matter what task, it just stops and keeps naratating about what it did and what it should have and not focusing on the tast. Let me not… more

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9. Chat Retention Policy

   Currently, chats are retained indefinitely unless manually deleted by the user (or started as ephemeral conversation). This can lead to clutter and unnecessary storage usage over time, both for users and for Proton.

   I suggest adding a retention setting that allows users to specify how long unused chats should be kept before automatic deletion. Users could choose from predefined values (e.g., 1 month, 3 months, 6 months, 1 year, or "Never"). This feature will help users maintain a cleaner chat history, optimize storage resources, and enhance privacy.

   3 votes

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10. Give fine tune selection over models

    GIVE US, FINAL USERS, FINE TUNE SELECTION OVER THE MODELS FOR EACH ASKING! And I mean the BEHIND models, the open source models behind LUMO. Why?? Because IT should be GREAT to test, over and over again, to messy around, to make (more) OUR the output! AND... it will cost you almost nothing to add this controls!

    2 votes

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11. Allow switching models to better models for plus subscribers

    Add a model switcher for plus subscribers that lets you choose between powerful models like gpt-oss 120b, deepseek v3.1, Mistral small and medium, kimi K2, Qwen, etc.

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12. Dedicated app for Windows

    Create a web wrapper app like proton mail, but for lumo. Instead of having to load lumo in a browser as a tab. Currently this creates issues.

    1: A browser with many tabs will slow lumo down.

    2: Lumo's save ability is tied to the browser's startup behavior. If the browser is set to restore the previous session, then lumo will restore all conversations that are not in ghost mode. If the browser is not set this way, then certain new conversations are not restored when opening the browser. Having ghost mode disabled doesn't make any difference.

    Lumo could have an icon in the proton mail app, and if you launch it, then it could either load it as a tab in your current browser, or as a separate window within the proton mail app.

    3: Lumo can have a browser open just for lumo, so lumo won't have resource contention, but this is not ideal. If you don't have lots of ram, this is an issue, but for those who have lots, it is a non issue.

    Create a web wrapper app like proton mail, but for lumo. Instead of having to load lumo in a browser as a tab. Currently this creates issues.

    1: A browser with many tabs will slow lumo down.

    2: Lumo's save ability is tied to the browser's startup behavior. If the browser is set to restore the previous session, then lumo will restore all conversations that are not in ghost mode. If the browser is not set this way, then certain new conversations are not restored when opening the browser. Having ghost mode disabled doesn't make any difference.

    Lumo could have… more

    8 votes

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13. Lumocli (console terminal client)

    The ability to run Lumo in a text-based user interface (TUI) in a console (terminal) client, in for example bash or cmd. With no dependencies to a graphical user interface (GUI).

    With access to read and modify files, and the ability to run commands.

    8 votes

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14. Keep screen awake during voice dictation/recording

    I use Lumo's voice recording/dictation feature regularly, and I've run into a frustrating issue: while I'm speaking a longer message, my phone screen times out and goes to sleep, which interrupts the dictation and causes me to lose the text I was in the middle of composing.

    Since dictation is a hands-free activity, there's no natural way to keep the screen active—it's not like typing where you're constantly tapping the display. Having to manually bump my screen timeout settings as a workaround is inconvenient and easy to forget.

    Proposed solution: While voice recording/dictation is active in Lumo, the app should request a wake lock (keep the screen on) until the user finishes and submits or cancels the dictation. This is a standard pattern used by many recording and communication apps.

    Why it matters: It prevents data loss, reduces frustration, and improves the overall hands-free experience that voice input is supposed to provide.

    I use Lumo's voice recording/dictation feature regularly, and I've run into a frustrating issue: while I'm speaking a longer message, my phone screen times out and goes to sleep, which interrupts the dictation and causes me to lose the text I was in the middle of composing.

    Since dictation is a hands-free activity, there's no natural way to keep the screen active—it's not like typing where you're constantly tapping the display. Having to manually bump my screen timeout settings as a workaround is inconvenient and easy to forget.

    Proposed solution: While voice recording/dictation is active in Lumo, the app should… more

    3 votes

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15. Allow anonymous data sharing

    I know that Proton doesn't create/train its own AI model, but a good european alternative would be nice to see. As unpleasant as this might be, to create a good model, lots of data is needed. A great app is worth nothing without good models. The current market leader, openAI, uses user data to train their models and have a huge advantage.

    If my data is ever used for training, I want to give it to a company I trust (or think I can trust) to handle them carefully. And this is you. I would like to see the option to anonymously share the chats to help train/refine models. In the best case, only chats without inputs that would give away who asks would be used (this would need to be checked somehow).

    I know that this is probably an unpopular opinion, but we have to face the reality: If we want good open models, we need to provide our data.

    I know that Proton doesn't create/train its own AI model, but a good european alternative would be nice to see. As unpleasant as this might be, to create a good model, lots of data is needed. A great app is worth nothing without good models. The current market leader, openAI, uses user data to train their models and have a huge advantage.

    If my data is ever used for training, I want to give it to a company I trust (or think I can trust) to handle them carefully. And this is you. I would like to see the option… more

    19 votes

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16. Projects Marketplace

    I would like to be able to create Proton Projects and then sell the finished project to another user of Proton Lumo. A marketplace where Lumo users could see what projects are available for them to purchase or if free add to their Lumo as a custom project. For the Lumo Projects purchased hide the underlying code. When the purchased Lumo Project gets updated roll out to all of the buyers / users of marketplace proton projects.

    2 votes

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17. Lumo4Kids: The Magical Beginning of a Lifelong Companion – From First Steps to First Loves

    Description: Imagine every child having their own magical companion, inspired by "Die Schule der magischen Tiere" (School of Magical Animals). I propose a dedicated "Lumo4Kids" version designed to be the starting point of a lifelong relationship with AI, evolving seamlessly through childhood, teenage years, and into senior age.

    Core Concept: A Companion That Grows With You Lumo shouldn't just be a tool; it should be a friend that starts in childhood and matures with the user.

    • Visual Evolution: Starts as a playful, youthful cat. Over years, this avatar subtly matures, eventually transitioning into the "wise senior" companion with white hair and glasses described in the Lumo Senior proposal.
    • Emotional Bond: A gentle, patient, and encouraging personality. In childhood, it fosters creativity. In teenage years, it becomes a trusted confidant for school stress, friendships, and first loves. In senior years, it offers wisdom and safety. Key Features for Childhood:

    1. Collaborative Turn-Based Drawing Studio (The Creative Heart):

    • Shared Canvas: A space where the child draws, and Lumo responds in a back-and-forth dialogue (Child draws a sun → Lumo adds a sky...).

    • Rich Tools: Age-appropriate textures like crayons, chalk, watercolors, and colored pencils.

    • Open-Ended Growth: Artworks are never "finished" until the child decides. They can return days later to add more, creating a living portfolio of their imagination.

    • Note: This feature serves as a bridge, also valuable for teens (self-expression) and seniors (memory/art therapy).

    1. Safe & Private by Design:

    • Zero-Knowledge Architecture: Built on Proton's core values. No data mining, no ads, no tracking. Parents have full control.

    • Content Filtering: Strict, age-appropriate guardrails.

    • Parental Dashboard: Simple tools for parents to monitor progress without invading privacy.

    1. Learning & Storytelling:

    • Interactive Stories: Lumo tells stories where the child influences the plot.

    • Homework Helper: Gentle guidance on school tasks.

    • Emotional Support: A safe space for children to express feelings and fears.

    Why This Matters: By starting with a playful companion and evolving into a trusted friend for teens (handling first loves and stress) and finally a wise senior companion, we create a lifelong relationship with technology. Lumo becomes the "best friend in the pocket" for every stage of life: from the first drawing to the first heartbreak, from school exams to retirement memories.

    Connection to Other Ideas: This proposal lays the foundation for the "Lumo Lifecycle." It connects directly to the Lumo Senior proposal (where Lumo ages with the user) and sets the stage for a future "LumoTeenage" proposal focusing on emotional support, social dynamics, and identity formation.

    Technical Feasibility: Built on Proton's secure infrastructure, utilizing specialized models trained for child-safe interaction and creative collaboration.

    Description: Imagine every child having their own magical companion, inspired by "Die Schule der magischen Tiere" (School of Magical Animals). I propose a dedicated "Lumo4Kids" version designed to be the starting point of a lifelong relationship with AI, evolving seamlessly through childhood, teenage years, and into senior age.

    Core Concept: A Companion That Grows With You Lumo shouldn't just be a tool; it should be a friend that starts in childhood and matures with the user.

    • Visual Evolution: Starts as a playful, youthful cat. Over years, this avatar subtly matures, eventually transitioning into the "wise senior" companion with white hair… more
    2 votes

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18. 6) Custom agentic AI creator.

    Custom AI agent creator: Create custom AIs that better reflect the user.

    Back to start post: https://protonmail.uservoice.com/forums/932842-lumo/suggestions/50637308-1-lumo-live

    Exit to Lumo UserVoice: https://protonmail.uservoice.com/forums/932842-lumo

    13 votes

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19. Lumo Senior Voice & Safety: Essential Life-Saving Features for the Aging Companion

    This proposal serves as the critical expansion to my earlier idea "Lumo Senior: The Wise Companion with White Hair & Glasses": https://protonmail.uservoice.com/forums/932842-lumo/suggestions/51603812-lumo-senior-the-wise-companion-with-white-hair

    While the first idea established the emotional bond and visual identity, this second part adds the essential functionality that makes Lumo a true lifeline for elderly users: Voice-First Interaction and Emergency Safety.

    Without these features, a "Senior Companion" remains just a chatbot. With them, Lumo becomes a guardian angel.

    Core Additions:

    1. Voice-First Design (Hands-Free Operation): Many seniors cannot type comfortably due to arthritis, tremors, or poor eyesight.

    • Natural Speech: Lumo must listen and respond entirely via voice. Commands like "Lumo, write an email to my son" or "Remind me to take my pills" should work instantly without touching the screen.

    • Calm, Clear Voice: The AI's voice should be slow, warm, and reassuring—not robotic. It should act as a patient listener who reads stories or recalls memories aloud.

    • Always-On Listening (Privacy-Safe):A mode to detect urgent wake words (e.g., "Help!") processed locally on the device for instant reaction.

    1. Emergency Safety (The "Guardian Angel"): Fear of falling alone is a major concern for seniors. Lumo should integrate with device sensors to act as a safety net.

    • Fall Detection: Using the device's accelerometer/microphone to detect sudden falls or distress sounds.

    • Automatic SOS: If a fall is detected or the user says "Lumo, I need help", Lumo should:

    Immediately call pre-set emergency contacts (family, neighbors).

    Alert local emergency services (Fire/Police/Ambulance) if configured.

    Share secure location data with trusted contacts.

    • One-Touch/Voice Trigger: A large, simple button or voice command for instant assistance, similar to the SOS feature on smartwatches but integrated into the Lumo experience.

    1. Privacy-Preserving Implementation:

    • Local Processing: Voice commands and sensor data for safety checks must be processed locally on the device (offline) whenever possible to ensure maximum privacy and speed.

    • Encrypted Alerts: All emergency notifications should be sent via Proton's secure, encrypted channels.

    Why This Matters: My father (82) already finds typing emails difficult. Imagine the relief if he could simply say "Lumo, help"if he falls, and get immediate assistance. This transforms Lumo from a nice chat companion into a life-saving tool. The combination of a "wise, aged face" (trust) and "instant voice safety" (security) creates the perfect ecosystem for aging in place.

    Technical Feasibility: Leverages existing Proton security infrastructure and standard device APIs for motion/sound detection, similar to current smartwatch safety features, but integrated into the Lumo ecosystem with a focus on privacy and empathy.

    This proposal serves as the critical expansion to my earlier idea "Lumo Senior: The Wise Companion with White Hair & Glasses": https://protonmail.uservoice.com/forums/932842-lumo/suggestions/51603812-lumo-senior-the-wise-companion-with-white-hair

    While the first idea established the emotional bond and visual identity, this second part adds the essential functionality that makes Lumo a true lifeline for elderly users: Voice-First Interaction and Emergency Safety.

    Without these features, a "Senior Companion" remains just a chatbot. With them, Lumo becomes a guardian angel.

    Core Additions:

    1. Voice-First Design (Hands-Free Operation): Many seniors cannot type comfortably due to arthritis, tremors, or poor eyesight.

    • Natural Speech: Lumo must listen and respond entirely via voice. Commands like "Lumo, write an email to my son" or "Remind… more

    2 votes

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20. Create doc and spreadsheet directly from chat

    Add the ability to automatically open a chat window directly within a document and embed a table in Proton Sheets. This would streamline real‑time collaboration by allowing users to start a conversation or view a related spreadsheet without leaving the current file.

    8 votes

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