I installed it and it's none of that. It is a mere wrapper around small local LLM models. And, it's not even multi-modal! Anyone could've one-shotted this in Claude in an hour (I'm not exaggerating).
What's the target audience here? Your average person doesn't care about the privacy value proposition (at least not by severely sacrificing chat model's quality). And users who do want that control can already install LMStudio/Llama.cpp (which is dead simple to setup).
The actual release product should've been what's described in "What's next" section.
> Instead of general chat, we shape Ensu to have a more specialized interface, say like a single, never-ending note you keep writing on, while the LLM offers suggestions, critiques, reminders, context, alternatives, viewpoints, quotes. A second brain, if you will.
> A more utilitarian take, say like an Android Launcher, where the LLM is an implementation detail behind an existing interaction that people are already used to.
> Your agent, running on your phone. No setup, no management, no manual backups. An LLM that grows with you, remembers you, your choices, manages your tasks, and has long-term memory and personality.
I think they did. If you start the download and then open the sidebar and/or background the app, the download progress bar disappears and is replaced by the download button. If you press the download button again, the progress bar reappears at the correct point.
I find that Claude often makes little statefulness mistakes like that. Human developers do too, but the slower and more iterative nature of human development makes it more likely that that would get caught.
This probably could have been one-shotted with Sonnet, not even Opus. Given how over indexed they are on LLM coding, Haiku might even be able to do it.
This is actually an interesting coding model benchmark task now that I think about it.
If it's so great, why is there so little viscera documenting it's greatness? Just lots and lots of words.
There is truly nothing original here and the product doesn't have a chance in hell of earning money. Local LLMs on-device will be dominated by the device vendors, whose control of the hardware stack combined with their ability to subsidize billions of dollars of machine learning research gives them an unfair advantage. Apple knows what the next generation of silicon will deliver, and their ML engineers are already hard at work building models that will be highly optimized for that silicon a year or two ahead of time. Open source models are really great and are backed by well funded labs; however, delivering these models on-device in a way that pleases users will never be easier than it is for the vendors of the devices.
Plus, device vendors have ways of making money from local LLMs that third-party app providers do not. They can make their local LLM free and earn money on the hardware play, without skipping a beat on the billions of dollars of ongoing R&D. I don't see how third party app vendors make money here when they will be competing with the decent, totally free alternative that Apple and Google (and Samsung etc.) will load on in the next year or two.
But where are they! https://ente.com/about
Small team, rooting for them
But sure, making money with standalone "local first is our headline feature" will be incredibly hard against those, no doubt about that. In light of the limited quality of what local models can achieve, the privacy bonus just won't compel many to pay. But that only means that this "morning with Claude" you are suggesting might be just the right amount of investment for the result you'd realistically expect. But is that so bad? I'd argue the reverse: bundling up the low hanging fruit but not by some hobbyist who will lose interest two weeks on but by a company big enough the keep it going while small enough to not be a VC furnace that will inevitably turn on users once the runway runs out (*), that's an opportunity to fill a niche few others can. Valuable for users who don't want to roll their own deployment of open source models (can't, or unwilling to commit to keeping them up to date, assuming that Ente does keep that ball rolling), and also valuable for the company of the investment actually is so low that it pays by raising awareness for their other products that apparently do earn them money.
(*) I was googling around a little wondering if they actually are as close to bootstrapped as they seem on the surface, and yes, that's supposedly the core idea [0], but despite that they also took 100 kUSD in "non-diluting" (basically a gift then?) from Mozilla with the explicit goal "to promote independent AI and machine learning" [1]. So not a CEO whim but following up to a promise made earlier. If they actually did avoid spending all that money on a one-off but went smaller planning to keep it current for a longer time horizon, I'd congratulate them on an excellent choice.
[0] https://ente.com/blog/5-years-of-ente/
[1] https://ente.io/blog/mozilla-builders/
The hn discussion for [1] seems to be completely missing the point, that Mozilla program isn't about funding an image host (yeah, I'd also prefer if Mozilla focused on the Browser and perhaps Thunderbird, but the foundation is what it is): https://news.ycombinator.com/item?id=41681666
(Though I think this announcement is sufficiently unpleasant I'm starting to reconsider)
We have not seen a tidal wave of untechnical people vibe coding up their own software solutions.
When my little brother who is a drummer, and has never even looked at "code" before, had claude on-shot a python app that let him download songs on youtube, extract the stems, collect tempo/key/etc information, then feed that into his DAW, all without ever looking at code, knowing what any of it did, etc., I knew that we were about to see a LOT of single-use applications.
I'm not against it, honestly. I have always written little one-off scripts and apps that accomplished something faster than manually, now that those one-shots are possible with an LLM in seconds sometimes, it makes all my personal scripts so much easier... that said, I definitely read the scripts that are output, and attempt to step through them in a debugger before assuming it is all good.
That to me is more valuable than code vibe coded by Claude in one afternoon.
I do agree that more local LLM options are always better.
Either LFM2.5-1.6B-4bit or Qwen3.5-2B-8bit or Qwen3.5-4B-4bit
Though, I don't see any references to Gemma at all in the open source code...
I would really like to know what people use these small and tiny models for. If any high-karma users are reading it, would you consider posting Ask HN?
very limited amount of use cases, perhaps. As a generalized chat assistant? I'm not sure you'd be able to get anything of value out from them, but happy to be proven otherwise. I have all of those locally already, without fine-tuning, what use case could I try right now where any of those are "very effective"?
You can use a small coding model to produce working code with a deterministic workflow (ex: state machine) if you carefully prune the context and filter down what it can do per iteration. Instead of letting it "reason" through an ever growing history, you give it distinct piecemeal steps with tailored context.
I think this can be generalized to:
Anything that can be built from small, well understood pieces and can be validated and fixed step by step. Then the challenge becomes designing these workflows and automating them.
(I'm not there yet, but one thing I have in mind might be a hybrid approach where the planning is produced by a more expensive model. The output it has to produce are data driven state machines or behavior trees (so they can be validated deterministically). Then it offloads the grunt work to a small, local model. When it's done, the work gets checked etc.)
Claude Code is a Desktop app as well.
For the user it's just important that the small grimlin that sits in the Ente app is not as smart as the grimlin that sits in the Claude app.
> Use Claude Code where you work
> Desktop Termianl IDE WEb and iOS Slack
Not that it is important any way ¯\_(ツ)_/¯
Ideally if you "participate" in the network, you would get "credits" to use it proportionally to how much GPU power you have provided to the network. Or if you can't, then buy credits (payment would be distributed as credits to other participants).
That way we could build huge LLMs that area really open and are not owned by any network.
I would LOVE to participate in building that as well.
This was posted the other day, but only briefly made the front page - seems kinda like what you’re talking about
Although the ability to use large models "for free" sounds pretty rad.
Have a comparison chart to Ollama, LMStudio, LocalAI, Exo, Jan.AI, GPT4ALL, PocketPal, etc.
When the comments here say "there's no value because anyone could've compiled llama.cpp", you can see how detached from reality these people are.
Even jumping through the hoops to get an app on Play Store and Apple Store — an app that I can tell my friends to look up and download — is worth a lot.
An app that is also is available on Mac and PC, mind you.
I'm an ex-Google/Meta/Microsoft/Roblox software engineer, and I couldn't be bothered to do any of that.
Neither could the rest of HN. But I'm not the one complaining about lack of novelty or value in this proposition.
Going to give this a try...
Does this seem sound?
Here’s where it was added to PrivacyGuides - https://github.com/privacyguides/privacyguides.org/issues/36.... The person opening the issue is the CEO of ente. So the CEO of ente gets his company mentioned in PrivacyGuides back when it was new and that makes it more legit?
this seems self-contradictory
https://en.wikipedia.org/wiki/Comparison_of_OTP_applications
They just store tokens, without other FA at "worst" you get locked of your account but nobody else has access either. You're also supposed to, as good practice, not be limited to token generation and typically have a dozen or so of recovery tokens. Also if they were somewhat not working at doing the 1 task they should do, namely generate tokens, then you won't be able to use them so it won't even be added.
So... I might be missing something, can you please explain what worries you and why I should thus worry too?
So you look down you see a tortoise. It's crawling towards you.
What I'm missing is a way to create and use Passkeys across devices. My use case does not support creating a new Passkey on every device, I need to sync them via servers I control. The system that supports that will be the system that I migrate to.
Expressly harvesting creds through a 2FA app seems a little more direct.
However, it’s a bit confusing because, for example, a larger LLM model was downloaded to my smartphone than to my computer. It would probably make the most sense if the app simply categorized devices into five different tiers and then, depending on which performance tier a device falls into, downloaded the appropriate model and simply informed the user of the performance tier. Over time, it would then be possible to periodically replace the LLM for each tier with better ones, or to redefine the device performance tiers based on hardware advancements.
They also have a TOTP auth app?
If their photos app stopped crashing and they pursued basic feature parity between their iOS and desktop apps (IMO table stakes for a photo sync service) I'd have no issue recommending them. Instead, it seems like every so often they just branch off into a new direction, leaving the existing products unfinished. It's like Mozilla-level lack of focus.
https://github.com/ente-io/ente/blob/f254af939ff6950b63edf5f... Here is the system prompt, kinda embarassing
Helping non-technical people get off of ChatGPT.com and using increasingly better local models seems worth celebrating and continued iteration.