Pebble Watch is Back, Baby! - DTNS 4944
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This is the Daily Tech News for Tuesday, January 28th, 2025. We tell you what you need to know, follow up on the context of those stories, and help each other understand. Today, Andrew Mayne tells us whether DeepSeek is worth all the hype, and an email about why humanoid robots are worth the effort. I'm Jason Howell. I'm Tom Merritt. Let's start with what you need to know with The Big Story. The Big Story

All right. If you thought the Pebble watch was a throwback to a simpler time in the world of wearables, well, you would be right. But people are very nostalgic. I'm sure you know this, Tom, for the trendsetting smartwatch brand that launched back way back at 2012.

Those people might actually be very happy to hear that the Pebble watch is coming back, baby. Eric Migakovsky is Pebble's founder, and he announced on the repebble launch page that the smartwatch is returning thanks to the efforts of a small team who's working closely with Eric to rekindle the glory days of the original hardware.

Some of the features of this new smartwatch effort and what they're chasing anyways include an always-on e-paper screen, Pebble's notably long battery life, those wonderful hardware buttons that you don't need to look at the screen in order to know what you're doing.

and a robust and comprehensive user experience that includes many of the functions that we've come to expect from smartwatches since Pebble's launch. Because you've got to remember, Pebble was kind of there in the beginning, and so set the trend for a lot of other wearables that we use today. Mikakowski says the new watch is in development and will have similar specs and features as the Pebble watch, along with, quote, some fun new stuff as well.

That's not all.

Megakovsky says he was able to convince, quote, friends at Google to make the open source transition of Pebble OS a reality. And this also means that anyone who wants to create their own Pebble-like watch running the open source OS is free to do so. So that's kind of neat. Or any type of hardware, really, that's running the OS.

This punctuates another key component of the news that Migakovsky pointed out that the new OS is, quote, hackable, meaning custom watch faces can be expected. Migakovsky says that an open source Pebble compatible app called Cobble is already running on Android, coming soon to iOS 6.

existing Pebble apps and watch faces will also be compatible with the new offering. There is no timeline though, set for the new hardware currently. Tom, what do you think? Did you ever have the, did you have the Pebble watch way back in the day? I never had one. No, I never had one. My friend, Brian Brushwood is still an enthusiast. He like held onto his Pebble until the very end. So there's so many interesting things about this. It is,

Interesting to see McGonkowski go off beeper to this because it's kind of a fight with Apple in both cases, right? Beeper clearly was a fight with Apple and trying to include iMessage compatibility. I wonder if he learned a lot there to help with compatibility here. However, he told Ars Technica that compatibility would be even worse now because Apple has like clamped down even more on that sort of thing.

But he clearly is up for the fight on that end. And more importantly than that, I think, is just the leaning into a niche market where you can tell that he's not worried about this becoming the biggest smartwatch in the world. He wants to serve the people like Brian Brushwood who are really excited about Pebble and would love to have it back. Yeah, I mean, the Pebble...

What is the right word? The Pebble excitement never really died down from the people who were real true fans. I mean, over the years, even ever since it went away, we would have stories come up on all about Android and later Android Faithful where the Pebble story continued. And really, when you think about where wearables and smartwatches and all that stuff is right now,

You really do have to credit Pebble for so much, which is kind of a testament to how unique they were back in 2012. This was prior to the Apple Watch. It was doing a lot of things that people wanted out of a wrist computer before there was a true market for it. And that's what was really kind of remarkable about this up-and-coming market.

hardware platform that didn't start on Kickstarter. I mean, it was just one of those examples of like, holy cow, you captured lightning in a bottle at a moment in time where, you know, this whole platform, this whole landscape of wearables was really just forming. So we have a lot to thank Pebble for when it comes to the wearables on Arrest.

Yeah, the OLED wearables we have now are always on. They have longer battery life. Some of the things that Pebble was so far in front of because of the e-ink screen aren't necessarily as big of a problem, but still, you can't get days of life out of a current smartwatch. You're always on is got some compromises here and there, especially when your battery starts to run low. It's going to be like, yeah, we can't be always on anymore. I think he's right that, yes,

You're never again, I'll repeat it. You're never going to overtake the Apple watch or wear OS or anything like that. But you are going to find a group of people who really want this. And I like that. That's what matters here. They not, not every enterprise has to become the winner in its space. You can, you can build really good businesses serving people who want a particular thing.

And that's, that's pebble to a T. So I don't know, 15 years later, 13, 13, I'm bad at math. 13 years later. Still, that's a long time. That's still, um, I hope, I hope it takes off. I, and I actually texted, uh, Brian once, as soon as I saw this story, I'm like, you're going to get this right. Uh, so I'll let you know if he, if he does. Oh,

Of course he is. If he was a huge fan, I could actually already hear Brian's voice going, are you kidding? Of course I am. I ordered three yesterday and they're not even being built yet. I'm going to build my own. So we want to hear from the Pebble fans, especially if you're like, I might not do it this time.

Or if you're somebody who never was into Pebble, but you're like, you know what? Now in 2025, I want this exactly thing. Feedback at DailyTechNewsShow.com. Let us know what you think. DTNS is made possible by you, the listener. Big thanks to Tim Deputy, Brandon Brooks, Hector Bones, and Jason, we have new patrons. Brand new patrons. Pavel and Justin. Welcome. Welcome.

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They ship Chicago deep dish pizza, New York bagels, Maine lobster rolls, and even Ina Garten's famous cakes. Seriously. So if you're looking for a Valentine's Day gift for the food lover in your life, head to goldbelly.com and get 20% off your first order with promo code gift. There is more we need to know today. So why don't we get right to the briefs, Tom?

Deep Seek. Have you ever heard of it? I have no idea what you're talking about. But there's something new with Deep Seek already. A new model family was released on Hugging Face. It's called Janus Pro. It's a set of

multimodal models for imaging, ranging in size from 1 to 7 billion parameters. The largest model is called Janus Pro 7BM, which apparently can beat Stable Diffusion and DALI 3 in GenuVal and DPG benchmarks.

According to DeepSeek, anyway, I haven't seen any independent verification of that yet, but I'm sure that's coming soon. The models can analyze and create new images, though analysis is limited to resolutions of 384 by 384, at least at the current moment. That's tiny. That's tiny. OpenAI announced ChatGPT-Gov, a self-hosted version of its platform designed for government agencies to confidently handle, quote, non-public sensitive information.

The tool will be available through an agency's own Microsoft Azure commercial cloud or Azure government community cloud. That makes it even more allied off than ChatGPT Enterprise anyways. The self-hosted solution is compliant with security frameworks, including IL-5,

CJIS, ITAR, and FedRAMP high, but only for public information at this point anyways. Testing of the product is expected within the month with wider availability in the near future. Yeah, this is why you cozy up to a new president so that you can sell them a new product. But Enterprise, Chachi PT Enterprise is very secure. It gives a lot of control to the enterprise. This gives opportunity.

all the control to the enterprise. This has got to be solid. And if you're concerned about this sort of thing, right now, only public information is supposed to go in here. Now, your weakest point is always humans. But yeah, this is a very big win for OpenAI. And it could be beneficial in a lot of agencies in speeding up things and making things more productive. Although, it will be

It'll be difficult to speed up everything when you're like, oh, wait, I can't put that in there. Or I think more often, I don't know if I should put this in here, so I'm not going to because I don't want to get in trouble. Yeah, you got to have certainty around that. And OpenAI is in deep trouble because of DeepSeek. So they've got to carve out new pathways. They're almost dead, as we'll hear later. No, no, no.

Blue Sky added a videos tab to your user profile. So now you can see all the videos that are posted by your account, including not only the original stuff that you posted, but anything you pulled in from other networks. Earlier this month, Blue Sky introduced trending videos to kind of capitalize on all the vertical video influx driven by fears over the shutdown of TikTok in the U.S.,

The Blue Sky 1.97 update also adds the ability to block or delete a conversation thread after reporting a direct message. Yeah, I saw that on my profile. The video part, not the deleted conversation. I haven't posted a video on Blue Sky yet, as I found out when I looked at the tab on my profile. Like, oh yeah, maybe I should try that. Yeah.

Spotify announced that it paid out a record $10 billion to the music industry last year, a milestone that surpassed the year prior's number by nearly $1 billion. So that number keeps going up. Spotify also shared that it has paid out $60 billion since its founding in 2006. The company says more than 10,000 artists currently earn more than $100,000 yearly from Spotify alone.

I don't know if those numbers are supposed to make me feel better or not, but there you go. Well, I think what Spotify is trying to combat is the perception that nobody makes money off of music on Spotify, which isn't true. In fact, the Variety article points out very directly, yeah, when you hear about artists not making money, it's probably not because Spotify isn't making money. It's because the record company or the label isn't passing along as much of it.

But also, you know, sometimes artists, they just don't stream enough to really turn it into $100,000. It does take a lot of streams to do that. You know this, right? Yeah. Oh, 100%. Yeah.

Never come close to that. That's for sure. Let's fix that right now. Everyone go stream yellow gold on Spotify. Stream, stream, stream. Yes, it worked. The International Rain Enhancement Forum is taking place in Abu Dhabi, where experts are discussing ways to improve cloud seeding technology.

The United Arab Emirates has already started using nanotechnology and electrical impulses to improve cloud seeding. It uses something called hygroscopic nanomaterials that are 10% titanium dioxide. Those can be dropped once into the atmosphere versus the previous method, which required burning flares to optimize the droplet size. So it's a lot more efficient, a lot faster, and could increase rainfall in the area by 10% to 25%.

The UAE is situated in a region that includes 11 of the 17 most water-stressed countries in the world. So, yeah, there's downsides to this, but it's also needed. The forum is soliciting new ideas in six categories. They want to hear about optimized seeding materials beyond the ones they're already using, new systems for cloud formation and rain enhancement beyond just seeding. What else can we do? Unmanned autonomous systems, local climate interventions, and advanced models, software, and data analysis, which I think is mostly AI stuff.

This just blows me away. It blows it like this is the stuff of science fiction where we're like, wait a minute, you can create rain. You can just go up there and put something in the cloud and make it rain. Yeah. A little bit. A little bit. It's not, you know, it's not crazy good, but 10 to 25% in a place that's getting virtually nothing. They can make a difference. Absolutely.

Google announced its plans to change the Gulf of Mexico to Gulf of America and Denali to Mount McKinley in Google Maps after an executive order issued by the U.S. president to change the name on federal maps and within federal communications.

Google wrote on X, quote, we have a longstanding practice of applying name changes when they have been updated in official government sources, end quote. Gulf of Mexico will continue to show for users in Mexico and in other countries because Google says, quote, when official names vary between countries, Maps users see their official local name. Everyone in the rest of the world sees both names.

Yeah, this is something they've had a lot of practice with, especially in areas like Eastern Europe and around Russia, where there's conflicts over what to name things and who is actually in control of things. So this is just another example of that being applied, I guess. Yeah.

The UK Competition and Markets Authority has issued a provisional finding that Amazon and Microsoft's cloud services in the UK have inadequate competition for consumer choice. I think each of them hold like 40% of the UK's cloud service market. The investigation began in October 2023. It highlights the difficulty customers face in switching between providers and

as well as the limited choice, the CMA will take comments and publish a final version of the report August 4th. So this is saying, we think we found you in violation. You've got until August 4th to explain why we're wrong. Rarely does it happen, but it has happened where they don't put out the final version with the same conclusion. Interesting. We'll have to wait and see.

And finally, I love a good rumor on a Tuesday morning. Rumors are swirling that Apple is on the cusp of releasing a long-awaited iPhone SE update. Majin Buu posted images on X that are supposedly the iPhone SE 4. They show a single rear-facing camera, a USB-C charging port, and a small front-facing camera. The video also seems to confirm that.

from the removal of the home button. So that would imply, you know, probably face ID for security and a new edge to edge display. How will we ever know? I don't know. What are we thinking? March, March announcement.

Yeah, you know, I'm not as clear or as knowledgeable on Apple's kind of release cadence. The cadence has been March. They could switch it up and go February, though. You know, it's a dynamic world. Those are the essentials for today, folks. We're going to dive a little deeper into an ongoing story, and there's only one to follow up on.

I bet you can't guess what it is. It starts with D, ends with K, and has EAPSEE in the middle. With the popularity of the super-efficient DeepSeek skyrocketing over the weekend. I think if you go to TechMeme, it's not even 100% DeepSeek. It's like 220% DeepSeek. They made it bigger.

Yes, exactly. A lot of folks thought this might spell doom for the need for NVIDIA chips. We actually found someone who can explain why that might not be so. Here's you, Tom, talking with Andrew Main. I'm talking with Andrew Main, co-founder of Interdimensional. And were you the original prompt engineer at OpenAI? Is that fair? Yeah.

We never had an official title prompt engineer at OpenAI, but I was probably the first person hired because of that. Okay. Yeah. And not like it was initially super competitive. I'm like, oh, I'll work on this. Like, okay, cool. Sure, sure. So I found out about DeepSeek recently.

A couple of weeks ago, when you mentioned it on the attention mechanism, it's not new. It's not overnight. But tell us what it is and why you think it suddenly got all this attention.

So you have DeepSeq, which is the Chinese lab, the company that built the DeepSeq models, which you've heard about. And they've got a couple models. So over a month ago, they released V3, which was their new model, which competed with the GPD-4. And it was worth of attention because they came up with some very, very good optimization strategies. I mean, very clever. And anything I say after this, I don't want to sound like I'm downplaying what they've done.

because they did some amazing optimization things, were able to figure out how to train it on using restricted compute, et cetera. I also pointed out that if you asked the model what model you are a couple times, it would say GPT-4. If you asked for its API, it would spit out the GPT-4 API. And there is a habit among some companies –

to bootstrap their training data by basically taking a ton of outputs from other models, which arguably takes compute and should be accounted for. But it strongly looked like, hey, they did some very, very clever strategies to make this. And now they've created the V3 model. Then they just released the R1 model last week.

which is their version of O1. And it's a reasoning model. It takes time to think about things. And by all accounts, R1 is a great model. I've seen people having wonderful results, whatever. And it's a fraction of the cost to use than OpenAI's O1. Although we don't know the real cost of OpenAI's O1 because OpenAI...

determines that. I would say by comparison, though, Google has their own flash thinking model, which is a much very, very, very inexpensive thinking model that they're using offering for like, I think, free on their API right now. So we know there are some really efficient thinking models out there. And this is what's called chain of thought, right? Where they sort of iterate on the answer before they give a final answer.

You give it a question, it breaks it down. It says, okay, I've got to think about this. And it might be, if you give it a problem, like, you know, write a paragraph about memory without using the letter E, it'll write something, go, whoop, I can try it again. And R1 will fail at that in my experience, but it will try to solve it. And you can see that process. Okay. So chain of thought isn't new, but they are one of the, I guess, fewer people doing it than LLMs and

Yeah, it's a chain of thoughts. You know, the 01 paper was published a few months ago. And so we have other people who are doing, you know, chain of thought like things. And the big thing was the efficiency is that they're doing chain of thought much, much, much more cheaply than 01 is.

But I want to give context in that part of the reaction has been is this idea that they trained this model for only like $5 million. But even if you read their paper, they talk about one component of it took that. There was a lot of other work into it. And also, if you're training on top of even Hugging Phase, which is trying to recreate this right now, is going to use the V3 model to output a bunch of data to then train on top of, which is kind of going to question like, well, where does that data come from originally? Right.

So there's verifiable efficiencies that you see that you can say like, yeah, they've actually done some really good stuff that make this work with less compute on the end. We can see there's their claim that,

that they were able to train it more efficiently that we can't really verify. We just have to take their word for it that like they weren't using H one hundreds or other types of chip. Yeah. I don't, I don't know. There's been some, yeah, some people questioning that they're using secret data and stuff. And that's, I mean, it's a Chinese company possible, but I'm going to accept them that they're being sincere because on the training part, hugging face is trying to recreate this right now. And we'll find out pretty quickly. Okay. So that, that would find out real quickly. What,

What would we have to see for deep seek to live up to the hype of

that is specifically like, you won't need as many GPUs. Let's not get to the investor part of it yet, but just, yeah, in the future, nobody will need as many GPUs or as powerful GPUs to do the equivalent amount of training here. For training, I don't know that they're saying that, you know, because, you know, they use something like 800,000 high quality examples to train the reasoning model, but we, and that came from their other model, but we don't know where

Like, give you an example. OpenA has GPT-4.0 and they have GPT-4.0 Mini. GPT-4.0 Mini is what you basically call what you consider a distilled model. You take GPT-4.0 and then you have it output millions of higher quality stuff. Instead of just random internet text, it's questions and answers. How do I calculate this? What's the answer to that? So instead of it...

If you want to learn a language, there's two ways to do it. I could give you 100 books in Japanese and 100 books in English that are the same book, and you could take time to figure out how to translate. Or I give you one book that's a translation book, and you'll learn much faster. Think of a distilled model as basically having the bigger model create that translation for it so it doesn't have to learn. And so you still need that bigger model and that data originally to come from. Got it. So...

Is everyone going crazy by saying, oh, NVIDIA is not going to be as valuable now because of this? It's literally like somebody came up with a more efficient light bulb in 1899 and were like, we don't need electricity providers anymore. It is. I am.

I advise VC firms and people like this, and I would tell you that in my opinion, the people making a lot of these investment decisions know a lot less than I do. And I have friends that work at labs who are shaking their heads going, don't people understand that this is just going to increase even more usage and value?

you know, uh, full disclosure, I am an Nvidia shareholder and even more so today. Um, not financial advice, but, uh, I, I, I don't think, I don't think wall street's never been good at predicting where these things are going to go. Um,

We'll see. The idea has been that there was an endless amount of need for compute, and NVIDIA would be the main beneficiary because they built this moat around themselves, meaning they're unassailable. And

The fear is that, well, if DeepSeek can do this on the cheap with some NVIDIA 4090s, well, then who's going to need to pay NVIDIA for that? But if I'm reading you right, what you're saying, Andrew, is like, well, sure, if you can do more on an NVIDIA 4090, then you can do even more if you have an H100.

Well, and still the inference to still because there's training, which is how you train them all to inference how you serve it. There hasn't been a big, huge inference speed up on what they're going to use. It's going to use the same amount of energy or compute than most other models are right now. That didn't change much. You know, GPD 4.0 mini is 15 cents per million tokens. You're going to see these models get better. I know researchers. I've been talking to researchers who have been talking about a thousand X or million increase in efficiency on these things.

So it's not like anybody's like, oh, we didn't think it was possible. They all thought we were headed there. It's just the timeframes are being accelerated. So yeah. And you think about, I work with companies now that are trying to do things like simulate entire biological systems or trying to simulate things in the world of physics because they want to build new materials. And these little things, reasoning systems are great speed ups, but their compute restraints are huge. And also the more we use stuff,

Like I've been using OpenAI as operator. And so as I go around and I'm running tasks, this is a system that's continuously thinking as it's trying to solve problems. So I think the demand is a thousand X what anybody thinks it is. It's going to keep increasing. Yeah. Says the guy that just bought more NVIDIA. And that's the inferencing, right? That's the sitting at home needs a query versus the training, which is them getting the model ready to answer the questions.

But also, we've moved into this new world like we talked about before. NVIDIA came out with Cosmos, right, which is a world foundation model, which is a neural net designed to simulate an entire world model to train robotics. Those, you know, you're going to build those efficiently, but you're going to keep scaling and making those more. Like we're inventing all kinds of new training paradigms all the time. And so –

So I feel like people are in a GPT-3 world and they think that the only the future of AI is just more chatbots. But it's going to be more agents, at least, and beyond. And everything's beyond that. Yeah. And yeah, Project Stargate, OpenAI's plan to build a $500 billion complex. Part of that is the idea is that like let's this to run as many experiments as you can train as many AI models as you can. And that's the advantage of great. We've got an efficiency. Great. That just brings the future closer.

Yeah, folks, if you really want to learn more about this and keep up on it, you've got to check out the attention mechanism with Andrew Main and Justin Robert Young. Obviously, there's not a particular place. Just go search for it. Right. Well, I think that's a cast a cast.

Ah, fantastic. Andrew, thanks so much for talking with us, man. Thanks for having me, Tom. What would you like to hear us talk about on the show? One way to let us know is our subreddit. Good folks in there submitting great stories and you can vote on them and submit some of your own reddit.com slash r slash Daily Tech News Show.

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We end every episode of DTNS with some shared wisdom. And today, Andrew, not Andrew Bain, is helping us understand why we want robots that look like humans. Yeah. He writes, humanoid robots may seem creepy, but as America's Test Kitchen's CES video demonstrates, it's faster to program them with videos of other humans doing those jobs.

They visited with a barista arm that was trained in a real barista, and it did a bang-up job at Latte Art, as well as the battery oven that you could talk about on the show, which we won't be talking about the battery oven. But yeah, good stuff from Frozen Cusser, a.k.a. Andrew. Thank you for pointing that out. You can train the algorithm properly.

If it's humanoid to do human things based on human motions, I guess that makes sense. That makes a lot of sense. Thank you. Cusser of the frozen variety.

Thanks to Andrew Mayne and, yes, of course, Frozen Cusser for contributing to today's show. Thank you for being along for Daily Tech News Show. The show is made possible by our patrons at patreon.com slash DTNS. DTNS has a live version called DTNS Live on YouTube and Twitch. You can find details on that and more on dailytechnewsshow.com. We will talk to you tomorrow. The DTNS family of podcasts.

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