Before, the emails were "me@icloud.com", the default for all apple users. There was no way to distinguish normal emails from generated private emails.
Now, they will be "blah@private.icloud.com", so it will be easy to ban the generated/private email that reduces the ability to associate logins across services.
Unclear why Apple would shoot themselves in this way; I hope it's not Ternus complying with anti-privacy.
I've been in the ecosystem long enough to have .iCloud.com, .me, .mobileme.com, iTunes.com, and probably one or two more addresses all assigned by various Apple services over the years before they started unifying the systems.
They all work, and independently of one another.
I wonder if all the domains will be migrated, and how namespace collisions will be handled.
maybe to avoid getting their legitimate email servers banned by other servers since they host (i.e. being exploited) a growing number of spam accounts.
For most online businesses, blocking Apple email servers sounds like a good way to kill off the portion of your customer base that has the most money to spend.
Many web sites and apps do not use Sign in with Apple. And they could block the domain for account creation with email without blocking the domain for account creation with Sign in with Apple. This would not make sense unless Apple changed what personal information Sign in with Apple provided probably. But they could.
I think you as the user can use the aliases without Sign in with Apple though, right?
But otherwise, you're right, any website that wants to accept Sign in with Apple will almost certainly be agreeing to Apple's TOS for Sign in with Apple I presume will stop you from blocking this service.
I see – somehow the Apple UI for this gave me the mistaken impression that privaterelay.appleid.com was the domain used by the alias, but I see now that it was always just icloud.com.
Was it expected that Qwen is working on this? What are the current alternatives?
The TAM for robots is much, much larger than for coding or services, and much more strategic when you think about manufacturing and war-making.
The Qwen "suite" is a workmanlike breakdown with demonstrated tasks that seems to me as an outsider to suggest that one could start building integrated systems this year, and have simple products next year. I'd be very interested in an assessment from engineers from the robotics companies (cars, biomedical robots, manufacturing...).
Elsewhere on HN I see hundreds of comments on SpaceX's long-telegraphed merger with Cursor but no serious evaluation of this.
I come from a regular swe background, but I've spent the last few months getting into robotics and trying to build a snow-clearing robot, so here's my noob notes:
First, very much expected. Both Google and Qwen have been building explicit spatial reasoning and spatial output capabilities in their models since last fall, gemini 3 was released with support for outputting trajectories for example. I only took a look at Robonav (more relevant for my needs) and its architecture and capabilities are inline with other similar models (eg nVidia's alpamayo).
Second, the overall architecture they describe mirrors what I've been working on: You have general purpose LLM that takes a look at the works and the task in front of it and reasons to break it down into subtasks and tool calls, and you can think of RoboNav and RoboManip as tool calls here. The harness keeps a memory and manages the context of the LLM and tools and keep looping until the objective is complete.
Consider the task of clearing snow off a driveway using this suite: An LLM (Qwen 3.7 plus) takes look at the driveway and decides which areas to clear. The harness then tells robotnav to go to an certain location, then robotnav takes over an runs in a loop until the robot is that that location. Then the harness tells robotmanip to use the plow to clear strip of snow. The harness will then call the planner LLM to plan an execute the next clearing and repeats until the driveway is clear.
So what' the issues? Well, they didn't release the weights, nor the training scripts so you can't actually use it. But also, it's all very research-y still, the models are "small" but still huge/expensive for current edge hardware. You'd still need lots of data collection, HITL, and fine-tuning and evals to make it work for your task. You'd also need a secondary safety system to make sure the models don't wreck something. But overall, I do expect robots to use an agent/model combo like this in prod in a few years.
This is bananas to me. Theres been successful entries to snow plow competitions for ages. What a world that people now expect networks to handhold through it. Irresistable to all parties I suppose.
Yeah, there's commercially available snow plow robots, you can buy a Yarbo for your house today. As far as I can tell, they all operate on a classical robotics stack - for the Yarbo you install an RTK antenna to give the robot cm-level precision, define a map and a routine, then the Yarbo can execute that routine by itself.
But can it deal with arbitrary lots without extensive premapping, manage piles, handle obstacles intelligently, correct itself (ie spot needs a second clearing ), tackle windrows, etc? It can't, and my hunch is that LLMs are the first tech we have that can plausibly handle all the various cases that a proper robot would need to handle.
My hunch is that some kind of planning stack with environmental awareness at a network level is a good solution to this. My hunch is that LLMs aren't really it. Maybe VLA but I'd bet lower.
Robotics probably will absorb a lot of Rl/diffusion-based tech, with LLM at a high level interface at best.
Yeah, afaik the approach people take today is always some form of bi or tri level hierarchical control, with a slow LLM doing planning and sub task management and diffusion or VLA doing the motor control at higher frequencies. Major differences seem like where and how you draw the boundaries. For my project I'm personally trying to use ROS2 as a low level tool call (instead of diffusion), with an agent /LLM doing the main decisions.
Having said that, this scheme seems like it might just be a reaction to current hardware limitations. When I saw Talaas demonstrate a 8B model running on a custom chip at 17k Tok/sec, first thing I thought was "wow, you can just run an LLM in a control loop"
I wonder if there is hope for clearing ice off asphalt and concrete. It's a real problem in Scando, where temps can hover around freezing longtime, for repeated thaw/freeze cycles.
The physical work in the world far outstrips the information work. Most information work is simply organizing physical work, attempting to make physical work more efficient.
The promise of intelligence might be larger still. By scaling and using superintelligent LLMs to write code for itself, it's possible that the whole field of robotics is just another problem you can point LLM agents at and expect to be solved by afternoon, just like one of those math puzzles. "Traditional" robotics R&D (or any R&D really) would be worthless due to abundance.
You're just confirming GP's point. If AI agents make those software problems trivial, the physical tasks are all that's left.
Regard it as market segments. It's not hard to envision eg. agriculture & food processing robotized to the point where no human ever touches your food. A few generations in, and people would see potatoes as "nutrient-containing object that comes from a factory" and forgot how to grow potatoes.
I'm rooting for the 'market segment' where AGI (or ASI) finds solutions to long-standing science questions, that are hard to obtain but easy to verify. Or makes new discoveries. Stuff like cancer research, protein folding, synthetic biology, new materials, battery tech, number theory, particle physics, etc etc.
I'm with you. As these models grow in ability and commoditize across the TAM of basically every business in the world, it's going to get cheaper and cheaper to solve everything.
The reason is that people keep making the same mistake, because people are not very good at assessing high-risk, high-reward projects.
Only a real deadline/delivery failure can wake people up, and only if they haven't pivoted their dream to something else, and only if you don't have other people knowing it's a scam, but willing to prop up the stock price because they are highly invested.
Scams, like cancer, are real; they survive, grow and defend themselves using the same mechanisms (laws, advisors, promotors) as ordinary investments/tissue, until they kill the patient -- so the best scams target the largest unkillable patients and enlist the broadest and deepest range of self-interested insiders as their defense.
It's beautiful, if you really think about it, as a tragic example of the worst of capitalism.
Perhaps that's where the money and strategy is. (a) stronger need; (b) if you can build systems without real expertise, you don't have to stomach their salaries or politics.
As others have mentioned, building custom homes is the last place I'd use AI.
But if you're considering a pivot, interior design would be a great direction!
Given the space and furniture I have or could buy, what are my alternatives for flow and light and usability? What if energy or allergens are an issue?
This could engage users and has natural add-on's for buying things that would help monetize with price discrimination. End-users could be happy to explore, but you might have more features for designers.
You could fine-tune based on all the home-decorating videos and materials, add MCP for physical models (layout/positioning, environment), and use video models for ingesting current and visualizing results.
I run many models (but mainly Gemma-4) using oMLX (for caching) on a 32GB M1 max using (gasp) Xcode. For tok/sec response times, I'd say it responds faster than I could read the prompt aloud in many cases (and I'm not constantly polling the Claude status page).
For months I spent time curating the AI+harness+skills+MCP servers, but now mainly just code with it. I find myself not bothering to use Claude (but keep paying "just in case").
That's feasible in part because my prompts have very specific objectives, constraints, and suggested staging, because I want the code to be exactly as I would write it, and I want to weigh in at specific moments. I would say the speed-up is 2-4X instead of the 10X of vibe-coding greenfield projects. The problem is not the coding speed, but building something complicated that's also correct and flexible (i.e., a directional accuracy). E.g., the agents help with abandoning a less-fruitful API shape instead of sticking with what works in a local maxima.
One flaw there is that I'm still writing code that feels clean to humans, which now is probably a waste. LLM's might be happier with 10+ parameters on one API instead of a plethora of configuration objects and convenience wrappers.
I definitely see the value! But I'm not confident I can tell whether there are e.g., security implications, and I couldn't find anything on point in the docs or on github (other than one discussion on authentication that mentions the information disclosed). Would love a whitepaper on that and any other issues adopters should consider.
We should definitely do a better job explaining this.
Regarding security, one thing to be aware of is that iroh connections are just standard QUIC connections secured using standard TLS with the (also standard) raw public keys in TLS extension.
We don't roll our own crypto. What little non-standard crypto we had previously was removed on the path to iroh 1.0.
So iroh connections are just as secure as the QUIC/TLS connections your browser makes to your banking app. Whenever there are some new concerns like for example post quantum security, we can benefit from industry standards.
E.g. we do already support optional post quantum key exchange to secure connections.
What differentiates the thing me and my friends want from a scaling business is the whole business. I know young people excited about making fantasy figurines or simplifying 10 minutes of daily administrivia, but that’s not going to fly.
Aristotle describes four causes, and pg has given two; not because he doesn’t know the rest, but because this post is not intended to guide people, but to tamp down the demonizing of success, lest it usher in a Cold War on Capitalism.
There’s much to criticize, but today politicians are desperately herding outrage to make up for their ineffectual performance, so instead of criticism resulting in field-leveling regulation, we’re just seeing haircuts that send the rats scurrying off the boat and fuel for the fires consuming it.
I don't think this is Amazon targeting Anthropic, but the government shaking down Anthropic using Amazon. The government is a key customer of Amazon, so Amazon will provide cover as needed. Amazon knows their equity stake in Anthropic is not particularly at risk, and they only gain negotiating power by looping in the feds.
Security is a real concern. Security experts within the government should create public+private working groups to validate all the leading models (by the same standards). Leaving it to companies to share with friends is wishful at best. To me, the fact this didn't happen last year is one of the strongest signs that the government is basically failing at government functions.
Now, they will be "blah@private.icloud.com", so it will be easy to ban the generated/private email that reduces the ability to associate logins across services.
Unclear why Apple would shoot themselves in this way; I hope it's not Ternus complying with anti-privacy.
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