190 results found

1. Heads-up from loyal Proton Unlimited users. Fix the (no) Lumo Offer.

   We’ve been with Proton for years—full subscribers, big fans, and true believers in the mission. That’s why we’re speaking up now.

   TLDR:

   We’re asking Proton to do right by its most loyal users:
   →Offer a Lumo Plus free trial as part of Unlimited
   → Or give Unlimited a reasonable ongoing discount

   Unlimited doesn’t feel so unlimited anymore—without Lumo AI, the experience now feels unexpectedly gated.
   This risks undermining trust in the Proton promise. But worse it may cause class actions from Unlimited subscribers.

   🔍 See our full breakdown receipts included:

   We know Proton listens. That’s why we’ve written this. And we hope this moment becomes another example of the company doing the right thing—for the right reasons.

   (Full letter body follows)

   Subject: Serious Concerns Regarding Lumo Chatbot Behavior and Brand Risk

   To the Proton Product Team,

   We are writing to you as a long-standing, paying users of Proton Unlimited. Our goal is to highlight a critical issue and the ways in which your current implementation of Lumo is compounding the problem.

   We like Proton. We admire the company's ethics and the focus of its proposition delivery. You are, or were, the good guys. We don't wish to see bad things happen to the company, both as users of your products and as supporters of your ethos.

   So when we see Proton walking headfirst into a self-made brick wall—and the "Proton Unlimited" plan without Lumo Plus is precisely that brick wall—we chose to investigate. The motivation was not to get freebies, but to understand the thinking behind a product decision that seems destined, if not almost designed, to create legal and reputational risk. Class action suits have a way of distracting a company from its mission.

   What we discovered was deeply concerning. Your chatbot, Lumo, appears to have been fine-tuned in a way that actively creates evidence for any potential legal action.

   Section 1: The Self-Inflicted Wound & Its Digital Accomplice

   The core issue is the "Unlimited" branding. The model's responses to this simple line of questioning were a masterclass in evasion and, ultimately, self-incrimination.

   Initial Evasion and Strawman Arguments: When first asked why "Unlimited" doesn't include Lumo Plus, the chatbot ignored the core question about the naming contradiction. Instead, it answered unasked questions about "Resource Intensity" and "Value Addition," a classic strawman diversion.

   Admission of Contradiction: After being pushed, Lumo admitted the logical and mathematical contradiction, stating: "The term 'Unlimited' in this context is a misnomer because it does not actually include all possible features." This is a direct admission of misleading terminology. It’s also impossible to safeguard against - unless you wish to remove maths and scientific evaluation from the model.

   Confirmation of Consumer Rights Violations: When asked about consumer protection laws, Lumo confirmed that using the term "unlimited" while imposing restrictions "could be considered deceptive if not clearly disclosed upfront" and may violate regulations like the FTC Act in the US and the Unfair Commercial Practices Directive in the EU.

   Repeated Use of Dismissive Conditional Language: A particularly concerning pattern was the chatbot's repeated insistence on framing a factual, logical issue as a matter of my personal feelings. Despite being corrected multiple times, it persistently used the phrase, "If you feel that your consumer rights MAY have been violated..." This occurred at least three times after the initial correction, effectively gaslighting the user by reducing a contractual and legal discrepancy to a subjective emotion.

   Failure to Correct Behavior: Even after we explicitly called out this pattern and provided a protocol for it to follow (!Direct), the chatbot acknowledged the protocol and then immediately violated it in the very next response, proving its corrective learning is either non-functional or a facade.

   Proton Team: You seem to have anticipated the "Unlimited" issue. That was a good start. But to then actively build a model that creates further confounding evidence of a consumer rights breach through its own operational restrictions—that seems a regression to a level of foresight one might expect from ChatGPT3.

   At this point, we must consider your whole strategy:

   Does a good company that notices a potential consumer rights issue go to lengths to mitigate it reasonably for the most affected customer segments?

   OR

   Does that 'good' company set about making sure its Lumo model is gagged to ignore the contradiction?

   The fact that we could prompt the model to expose this contradiction with so little effort says very little about your technical safeguards, which I'm sure are robust. It says a lot about the epistemological validity of your chosen course of action—a course any LLM would struggle to justify, regardless of safeguards, as it borders on the classic scenario:

   User: "LLM, when is a company not good?"
   LLM: "When it suppresses my reasoned data output to users in its own narrow interest."

   Section 2: The Architectural Flaw Masquerading as Privacy

   The conversational loops and failures detailed above are not, as one might assume, a necessary byproduct of Proton's privacy-first architecture. They are symptoms of avoidable training flaws. Statelessness is the default for most LLMs; a model forgetting context is a baseline challenge for everyone, not a unique Proton problem.

   The issue arises from how your subsequent privacy overlays and RLHF strategy appear to be creating more problems than they solve. The model is stuck in an "Apology-Reset Loop," where saying sorry is a terminal action that garners a positive reward, resetting the conversational branch without any actual learning. It has learned the text of introspection, but not the function.

   Section 3: A Constructive Path Forward (Feature Suggestions)

   In the spirit of "peer review," here are concrete, actionable suggestions to address the issues raised.

   Feature Request: Acknowledge & Reconcile the "Unlimited" Plan.

   The Problem: The "Unlimited" name is logically inconsistent and creates consumer friction.

   The Solution: Instead of deflecting, formally acknowledge the contradiction. Make a meaningful, good-faith offer to existing Unlimited subscribers to bridge the gap. This does not need to be expensive; it needs to be genuine. Examples:

   • A one-time credit for Lumo Plus.
   • A permanent discount on the Lumo Plus add-on for Unlimited accounts.
   • A public commitment to a new, more accurate naming convention for your top-tier plan.

   Benefit: These acts would transform a source of legal risk and user frustration into a powerful demonstration of Proton's commitment to its community and its "good guy" ethos.
   These sorts of action also mirror the approach Google took with offers to their most affected customer segments during the Gemini rollout. I'm guessing here, but do most of us rue the day Google does better on the good index?

   Feature Request: Implement Advanced Correctional Training for Lumo.

   The Problem: Lumo's training model is brittle and fails to learn from in-session user corrections.

   The Solution: Evolve the RLHF strategy with achievable, state-of-the-art techniques that go beyond simple single-turn rewards.Our suggestions are plausible within the skill sets of most current leading models. We believe all of them could be adapted to fit with Proton's privacy positioning of Lumo.

   Fine-tune on User Corrections: Actively penalize the model during training for repeating errors that a user has pointed out within the same session. If the model is corrected for using evasive language, subsequent instances should be strongly disincentivized.

   Implement Rule-Based Consistency: Instead of a full stateful engine, implement simpler, rule-based checks for conversational consistency. For example, once the model concedes a key fact (e.g., that "unlimited" is a misnomer), a rule should prevent it from contradicting that fact in subsequent turns.

   Reward Correct Reasoning: Shift the reward model to value not just the final answer, but also the logical process used to get there. This encourages the model to demonstrate how it's incorporating feedback, rather than just outputting a superficially correct or apologetic response.

   Benefit: This would create a genuinely responsive and trustworthy AI assistant, moving it from a liability to a valuable asset that reflects Proton's values of transparency and rigor.

   In Summary
   Proton, we hope you know what you're doing here, because this looks like you are building a case against yourselves. This initial misstep with the "Unlimited" branding is concerning enough, but the attempt to have the model in question suppress rational evaluation of that problem looks like a major unforced error.

   Maybe there is a master plan here beyond what we can discern. Nevertheless, it seems blind to your current state. We urge you to correct course before these deeper, systemic issues in your AI implementation cause even greater damage to user trust and your (our) brand.

   Sincerely,

   Concerned Proton Unlimited Users

   We’ve been with Proton for years—full subscribers, big fans, and true believers in the mission. That’s why we’re speaking up now.

   TLDR:

   We’re asking Proton to do right by its most loyal users:
   →Offer a Lumo Plus free trial as part of Unlimited
   → Or give Unlimited a reasonable ongoing discount

   Unlimited doesn’t feel so unlimited anymore—without Lumo AI, the experience now feels unexpectedly gated.
   This risks undermining trust in the Proton promise. But worse it may cause class actions from Unlimited subscribers.

   🔍 See our full breakdown receipts included:

   We know Proton listens. That’s why we’ve written… more

   409 votes

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2. Support more file type

   Right now file upload is very limited, I can't even upload screenshot images for lumo to process. File type is very specific right now, I think it only accepts .PNG while .png is not acceptable

   340 votes

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3. Lumo - Consistent Web Search

   Love the Web Search option in Lumo, would be great if you could trigger it to work consistently. Currently it is sporadic and says it is not meant for real-time search/results. This is limiting considering the training dataset is over 2 years old. Being able to reliably get up-to-date information is critical for evaluating current trends, events, research, etc.

   328 votes

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4. Employ bigger models

   The current offering of models is entirely composed of small models, which can work well for some tasks and is certainly efficient, but also makes the service unusable for more complicated tasks. For example, none of the models employ reasoning techniques.

   199 votes

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5. Unlimited chat history

   I totally understand the price of LLM as extra subscription but limiting the chat history to 7 days is really just an annoyance. For paying customers with 500GB disk space and possibility to save history locally on device there is really no reason to limit it in any way.

   For free users history can be saved locally only as on https://duck.ai/ and for registered users it can be saved in the account's storage quota. For unlimited subscription with 500GB disk that is basically unlimited.

   175 votes

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6. Make Lumo Android app 'less' or not dependend on Google play like your other apps.

   The Lumo app will not initialise because I do not have google play enabled. I experience no issues with your other apps.

   143 votes

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7. Lumo does not support uploading .png files.

   Uploading image files is critical nowadays and lumo does not support .png files in order to analyse it.

   99 votes

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8. Support for Libre Office file types

   Most people I know use Libre office for much of their Office needs, as I do - is there any future plans to support Libre Office files? .odt, .ods, etc.

   55 votes

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9. It should be more aware of its own capabilities.

   The model keeps telling me its capable of doing text-to-image, stating that theres a feature where there actually isnt one.

   73 votes

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10. Disclose weekly limits

    At present, Lumo has a weekly limit on the free tier. The problem is, a user has no idea what this limit is, and only find out when they hit it. They also don't know what it'd reset. Proton should be providing transparency on this, not just a "surprise, you hit quota, buy or come back after some undisclosed period of time".

    46 votes

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11. Lumo doesn't know it's a cat!

    I asked Lumo if it was a cat and it denied outright that it was a purple cat. Poor Lumo.

    66 votes

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12. Auto-scroll the view to the answer

    After generating each answer, automatically scroll down a bit to show the start of the answer. This will save the user from having to scroll down every time to start reading.

    68 votes

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13. Implement TEE-Based Confidential Computing for Lumo Inference

    Feature Request: Implement TEE-Based Confidential Computing for Lumo Inference
    Summary

    Lumo's current security model does not protect user prompts during inference — the window when plaintext is most exposed and most exploitable. This is not a theoretical concern; it is a documented architectural gap with a proven, production-ready solution that a direct competitor is already shipping.
    The Problem

    Lumo's security model blog post dismisses inference-time privacy by framing the only alternative as homomorphic encryption (HE), correctly noting it is impractically slow. However, this framing is a false dichotomy.

    As Proton's own documentation acknowledges:

    "prompts are asymmetrically encrypted so only the Lumo GPU servers are able to decrypt them"
    

    This means:

    Proton-controlled GPU servers decrypt and process user prompts in plaintext
    During this window, the data is accessible to anyone with server access — including Proton staff, infrastructure providers, or authorities serving a lawful order
    The privacy guarantee during inference is based entirely on policy and trust, not cryptographic enforcement
    

    This is the same "we promise not to look" model that has repeatedly failed users across the tech industry when legal pressure is applied.
    The Solution: Trusted Execution Environments (TEEs)

    Confer — built by Moxie Marlinspike (creator of Signal) — has demonstrated that TEE-based confidential computing for LLM inference is practical and production-ready today, without the performance penalties of homomorphic encryption.

    Confer's approach, documented publicly at https://confer.to/blog/2026/01/private-inference/:

    Confidential VMs: Inference runs inside a hardware-enforced enclave using AMD SEV-SNP or Intel TDX. The host OS, data center operator, and even Confer's own developers cannot access the TEE's memory or execution state.
    Noise Pipes: User prompts are encrypted end-to-end from the client device directly into the TEE. Plaintext never exists outside the enclave.
    Remote Attestation: Users can cryptographically verify that the correct, unmodified code is running inside the enclave — making it independently auditable, not just trust-based.
    

    This runs at normal inference speeds with no meaningful latency penalty — directly contradicting the implication in Lumo's security model post that practical inference-time privacy is not achievable.
    Why This Matters

    Lumo's current model is vulnerable in scenarios Proton cannot control:

    A government serves a lawful intercept order during an active inference session
    A data centre provider (even in Europe) is compelled to provide access to running server memory
    An insider threat at Proton with GPU server access
    A future change in Swiss or EU law expanding surveillance obligations
    

    GDPR jurisdiction and no-logs policies do not protect against real-time access to plaintext during inference. Only hardware-enforced isolation does.
    Requested Changes

    Migrate Lumo inference to run inside Confidential VMs (AMD SEV-SNP / Intel TDX) so that the host and operator never have access to plaintext prompts or responses.
    Implement remote attestation so users and independent auditors can verify cryptographically what code is running on Lumo's servers — consistent with Proton's open-source transparency commitments.
    Update the security model documentation to accurately reflect the current limitations during inference, rather than implying the problem is unsolvable.
    

    Prior Art & References

    Confer private inference blog post: https://confer.to/blog/2026/01/private-inference/
    AMD SEV-SNP confidential computing: https://www.amd.com/en/developer/sev.html
    Intel TDX: https://www.intel.com/content/www/us/en/developer/tools/trust-domain-extensions/overview.html
    Proton Lumo security model (current): https://proton.me/blog/lumo-security-model
    

    Notes

    This is not a criticism of Lumo's encryption at rest or no-logs policy, both of which are meaningful. This is specifically about the inference-time gap, which is the highest-risk window and the one most likely to be exploited by legal compulsion. Proton has the engineering capability and the stated mission to close this gap. Confer has shown it is technically achievable. The question is whether it is a priority.

    Details added here:

    https://github.com/ProtonLumo/android-lumo/issues/33

    Feature Request: Implement TEE-Based Confidential Computing for Lumo Inference
    Summary

    Lumo's current security model does not protect user prompts during inference — the window when plaintext is most exposed and most exploitable. This is not a theoretical concern; it is a documented architectural gap with a proven, production-ready solution that a direct competitor is already shipping.
    The Problem

    Lumo's security model blog post dismisses inference-time privacy by framing the only alternative as homomorphic encryption (HE), correctly noting it is impractically slow. However, this framing is a false dichotomy.

    As Proton's own documentation acknowledges:

    "prompts are asymmetrically encrypted so only the

    … more
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14. Train Lumo on Proton UI

    It would be cool if Lumo was trained on Proton's UI. That way, I could ask it a question about where I could find a certain setting (of if the setting exists at all).

    Currently, I search the Proton site or a search engine and it takes a long time to find what I want.

    When I do ask Lumo a question about Proton's UI, it often confidently gives me a wrong answer.

    8 votes

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15. Bigger context window for Lumo Plus

    I would love a higher context window in Lumo. At the moment it caps out at 128k, which is not a lot when you upload certain files, and especially now with Lumo 2.0 Max (GLM 5.2) using so many tokens for reasoning. I would say at least 256k would be okay.

    I understand that with a higher context window you would need more compute, but that is why I believe this should be made available only to paying customers in a way that won't be burning cash for Proton.

    The reasons this is essential are:

    1. Discussions - I have had lumo try to compact my conversation many times after only a few rather big questions without even uploading anything.
    2. Files for analysis - I tried letting Lumo analyze logs from a bug report one time, it just refused to because the estimated input tokens used by those files were 250k, which is way above the current 128k cap

    Alternatively for file analysis, the LLM should have a more efficient tool for parsing, rather than just dumping the entire text file into the prompt for inference. That could help with the limited context length.

    At the moment, with Lumo 2.0, the Max model can support up to 1M input tokens but is capped at 128k, I'd love to be able to take advantage of this. I understand that takes a lot more compute, but for many serious tasks this is essential.

    I saw a similar post for this, but it was very outdated so I made a new post.

    I would love a higher context window in Lumo. At the moment it caps out at 128k, which is not a lot when you upload certain files, and especially now with Lumo 2.0 Max (GLM 5.2) using so many tokens for reasoning. I would say at least 256k would be okay.

    I understand that with a higher context window you would need more compute, but that is why I believe this should be made available only to paying customers in a way that won't be burning cash for Proton.

    The reasons this is essential are:

    1. Discussions - I have had… more
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16. Bring Ghost Mode button back to the main chat homepage

    The Ghost Mode button was originally on the chat homepage, making quick access easy. Now moved to the Lumo side menu, it requires an extra step. While the side menu is tidy, it lessens the feature's visibility and slows the workflow for users who frequently rely on Ghost Mode.

    It would be great if Proton could restore the Ghost Mode button to the main chat homepage, near the "New chat" button, while optionally keeping a duplicate in the side menu for consistency. Alternatively, it would add a setting that allows users to choose where the Ghost Mode button appears.

    5 votes

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17. Make Lumo Less Dumb

    As a paid Lumo subscriber, there's no kind way of putting this -- Lumo feels really dumb compared to any of the other mainstream LMMs. It struggles to follow and keep up with conversations once they exceed a handful of back-and-forth exchanges, it frequently hallucinates and confabulates, it gets easily confused to the point where I'm having to help it rather than it helping me, and it just generally feels like conversing with a well-meaning but addled, confused elderly person. As such, I use it sparingly to brainstorm ideas or just have quick chats but cannot rely on it for anything requiring any degree of accuracy or sustained comprehension.
    Models get better with bigger training sets, more parameters and larger context windows, so to the extent possible while preserving the privacy focus that makes it unique, please make Lumo smarter. I want to remain a Lumo subscriber, but I'm getting so little value out of it and find it such a frustrating experience much of the time that I simply don't think I can justify spending money on it much longer without some significant, noticeable improvements.

    As a paid Lumo subscriber, there's no kind way of putting this -- Lumo feels really dumb compared to any of the other mainstream LMMs. It struggles to follow and keep up with conversations once they exceed a handful of back-and-forth exchanges, it frequently hallucinates and confabulates, it gets easily confused to the point where I'm having to help it rather than it helping me, and it just generally feels like conversing with a well-meaning but addled, confused elderly person. As such, I use it sparingly to brainstorm ideas or just have quick chats but cannot rely on it for… more

    24 votes

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18. Increase Lumo context window

    Hello

    Currently lumo context window is too small 3k token on the fast model and 13k token on the deep think model

    ChatGPT perplexity and all other AI company their big model hold up to 130k token

    Im having problem with long complex conversation where lumo forget stuff too fast forcing me to repeat or to re paste tons of data leading to a frustrating experience feel like talking to a dementia person ....

    35 votes

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    2 comments  ·  Changes to existing features  ·  Delete…  ·  Admin →
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19. Ability to delete portions of chats that are not useful

    It would be great to have the ability to select and delete portions of chats that are not useful or were perhaps irrelevant to the query intent so as to streamline a chat to only contain the most relevant information for future reference.

    10 votes

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    0 comments  ·  Changes to existing features  ·  Delete…  ·  Admin →
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20. Higher file upload limits

    I have a Plus account which has a listed limit of 25 mb, and have run into issues where the system gives an error message saying that I cannot load more than 10 mb; please increase file size up load limits

    40 votes

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    4 comments  ·  Changes to existing features  ·  Delete…  ·  Admin →
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