Lumo
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6 results found
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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".
11 votes -
Lumo payment separate from my other products
Currently, to sign up for Lumo+, you must add it to your current subscription, with the same time period, so if you have a 1-year subscription, you have to pay for 1 year of Lumo, if you have a 2-year subscription, you have to pay for 2 years of Lumo up front.
The other option is to lose all the discounts on your main Proton account in order to activate Lumo+ for the new term.
For example, if you have a 2-year Duo subscription (€11.99 per month) and you only want 1 month of Lumo for both users, you have to switch to the monthly price, which increases the price to €45.97. This is more expensive than Visionary!
Lumo needs to have a separate billing option. Or fix the prices, because I will never subscribe this way. And I want to support it.
Currently, to sign up for Lumo+, you must add it to your current subscription, with the same time period, so if you have a 1-year subscription, you have to pay for 1 year of Lumo, if you have a 2-year subscription, you have to pay for 2 years of Lumo up front.
The other option is to lose all the discounts on your main Proton account in order to activate Lumo+ for the new term.
For example, if you have a 2-year Duo subscription (€11.99 per month) and you only want 1 month of Lumo for both users, you…
17 votes -
Option to "Delete All Chats EXCEPT Starred / Favourites"
In addition to "Delete all chats", I propose that there should be an option to "Delete all chats EXCEPT Starred / Favourites".
As my Lumo fills up with chats, I star the ones that I presume I would still like to come back to. As for the rest, I would like to periodically clean house by wiping them all out.
Presently that would mean deleting all of the non-starred chats one-by-one, which is unnecessarily frustrating.
This new option would regard favourites as "pinned" and leave them in place while deleting the rest.
39 votes -
Mass calendar deletion
I want to delete a lot of events in my calendar easily.
I created recurrent events (6/8 per days). In vacation period, I want to remove these events and replaced them by another ones. For each event, I need to click on the event, click on the delete button and confirm to delete the single event. I would like to select all events on the week and delete them with only one button.
3 votes -
travel time before/after event
It would be great if the calendar added the ability to include travel time to an event, after the event. For example, I have a meeting in another part of the city, and I need 30 minutes to get to the location. It would be very convenient for all that time to be shown in the calendar so that others can see that you are also busy during the travel period.
Currently this has to be worked around by creating another event or extending the existing one.
2 votes -
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…1 vote
227 results found
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Lumo integration into other proton apps (mail/calendar/drive etc)
Hi all, would be great if Lumo could be integrated into current and new proton apps such as mail so it can be used to help compose emails, create calendar entries etc.
425 votes -
Memory or Knowledge Functionality for Lumo
Hi Proton Team,
First off, thank you for your continued innovation and focus on privacy. I’m really enjoying the direction you’re taking with Lumo AI!
I’d like to request a feature that would make Lumo even more valuable: the ability to remember past interactions, facts, or preferences—essentially a "memory" or "knowledge" functionality. This could allow Lumo to provide more personalized, helpful, and context-aware responses over time while respecting privacy and user control.
For example, Lumo could keep track of things I’ve told it (like my preferences or recurring topics across all the chats or within chat groups), so I don’t have to repeat myself in each session. Of course, privacy and transparency in how this information is stored and managed would be essential—perhaps along the lines of an opt-in, adjustable, and fully user-controlled memory.
I believe this feature would set Lumo apart and make it a truly indispensable assistant, all while upholding Proton’s values.
Thank you for considering this suggestion!
Best,
[A user eager for smarter and more helpful AI]Hi Proton Team,
First off, thank you for your continued innovation and focus on privacy. I’m really enjoying the direction you’re taking with Lumo AI!
I’d like to request a feature that would make Lumo even more valuable: the ability to remember past interactions, facts, or preferences—essentially a "memory" or "knowledge" functionality. This could allow Lumo to provide more personalized, helpful, and context-aware responses over time while respecting privacy and user control.
For example, Lumo could keep track of things I’ve told it (like my preferences or recurring topics across all the chats or within chat groups), so I don’t…
258 votes -
Lumo - Project folders
I would like to group chats by folders and add to every folder(project) individual instructions to keep the conversation focused around the topics in the project.
290 votes -
Let me pet the cat
Whats the point in having a cat mascot if I can't even pet them!
209 votes -
Support pasting image (screenshot) directly to chat
As a software developer, I tend to screenshot my code screen (automatic copy) and just pasting it directly to the chat, no file saving needed. This is not possible on lumo web app right now but easily doable for chatgpt
216 votes -
Lumo Developer API
I would love to be able to programmatically call Lumo to perform advanced queries against the LLM, including the ability to run tools and functions. I'm an avid Home Assistant user that uses the OpenAI API to interface with local devices. I would prefer to use a secure and private API to do this, and am willing to pay for this capability with a similar cost structure to the OpenAI API.
186 votes -
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 discountUnlimited 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 discountUnlimited 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…
226 votes -
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.
196 votes -
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
141 votes -
Lumo button to allow permanent web search
Web search is crucial for AI answers IMO. It would be nice to not have to click the "Web Search" button every time I open the app. Great product though!
144 votes -
Lumo - Export Full Chat
In an effort to keep a clean history in Lumo, it would be nice to be able to export full chats instead of having to copy and paste individual responses. This would be similar to duck.ai's export feature.
124 votes -
Expose the underlying model name
I understand that offerings such as copilot also hide the underlying models, but I would really appreciate it if the model used with any given answer would be named. Currently, we only have a list in the supporting documentation of the possible models that may be used, and then in the answer it isn't reveled anywhere which model generated it. If the user directly asks, then the model will refuse. I assume this is because the model itself doesn't know, or it has been forbidden from revealing by the system prompt. I can understand the business incentive, but it is difficult to give such a level of control to Proton as to not even know which model has generated the answer.
I understand that offerings such as copilot also hide the underlying models, but I would really appreciate it if the model used with any given answer would be named. Currently, we only have a list in the supporting documentation of the possible models that may be used, and then in the answer it isn't reveled anywhere which model generated it. If the user directly asks, then the model will refuse. I assume this is because the model itself doesn't know, or it has been forbidden from revealing by the system prompt. I can understand the business incentive, but it is…
119 votes -
Local Lumo application on desktop
It would be great if it would be possible to have a local Lumo application on desktop which would learn/index from local data such as email and other files.
105 votes -
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.
91 votes -
Add Lumo to Firefox AI chatbots
I would love to see Lumo as an option for Firefox's AI chatbots selection. This would be a really good way to integrate it with the browser (outside of maybe making an extension in the future).
98 votes -
Ability to affect the model choice
Especially for paying power users, it would be nice to have the ability to manually affect the choice between models. I understand if this doesn't fit your service framework, but I wanted to just put this out there.
80 votes -
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.
75 votes -
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.
62 votes -
Enhance Lumo to allow file creation with download links
While its nice to have Lumo create file content, its tedious to have to copy and paste in-and-out of Lumo. This is especially true when the files are of different formats. Say, I want to have Lumo create several MD files based on a basic template I provide and a couple of bash scripts to perform various tasks. Today I have to copy and paste each to its own file, which is time consuming. It would be better if Lumo could create the files, bundle them into a compressed file format of my choice (zip, bzip2, xz, 7z, etc.) and provide me a link so I can download the bundle. A stretch goal would be to also allow me to directly store that file or archive bundle directly to my Proton Drive project folder.
While its nice to have Lumo create file content, its tedious to have to copy and paste in-and-out of Lumo. This is especially true when the files are of different formats. Say, I want to have Lumo create several MD files based on a basic template I provide and a couple of bash scripts to perform various tasks. Today I have to copy and paste each to its own file, which is time consuming. It would be better if Lumo could create the files, bundle them into a compressed file format of my choice (zip, bzip2, xz, 7z, etc.) and…
55 votes -
Date and time in responses
Lumo doesn't give any indication of the time of the response. This is missing if one looks at later responses. And in general, it's good to time stamp events;-)
55 votes
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