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This, but worse, happened with my inlaws when they purchased a new Lexus (IIRC, a 2025 RX350). It has whatever kind of built in navigation & traffic feature but they wanted to use Android Auto. You can do that, but not until after you go through Lexus' vehicle/driver registration process to enable their built in system. If you don't, not only will neither AA nor CarPlay work, but if you try to use the built in nav it will fail with a superimposed modal after a couple of minutes to nag you into creating an account.

That isn't what the OP was alleging. Of course they store data -- encrypted both in transit and at rest, and you can even activate client side encryption (https://knowledge.workspace.google.com/admin/security/about-...) -- but that doesn't mean they exfiltrate, use or share your data for their own purposes.

They don't, they never have, and they won't (unless there is a blatant, illegal policy violation and lapse of controls internally). This is very similar to the controls and policies in place governing FedRAMP contracts or the control of PCI/financial or HIPAA/healthcare data.

None of those have anything to do with Google for Education contracts, and I hope you realize that.

Education contracts do not allow Google to use customer data for advertising.

When we moved to the bay area in 2015 (south bay), I was absolutely stunned at the variety of residential architecture, having come from the mid-Atlantic where everything tended to be uniform based roughly on construction date/period. Since land is exorbitantly costly, homeowners tend to build what they like, no matter whether it fits the neighborhood aesthetic or not.

I recommend Doctors of Running (website & podcast). They're a group of running PTs and each of them has different needs & preference. One in particular likes less cushioned and more structured shoes. There are ample options out there.

That's just a bad salesperson.

Conventional wisdom among running coaches and orthopedists these days is that the most important thing is to run comfortably, and that just because something looks "weird" doesn't necessarily mean it's bad or wrong. Yes, it may be inefficient, but it probably isn't hurting the runner, either.

Shoes salespeople wield huge influence over buying decisions, especially if they come off as authoritative.

My son works in a running shoe store that also does gait analysis and they default every new runner into something stable-neutral like a Brooks Ghost, Saucony Ride, or NB 880. They never default with Nike or Adidas because, although both brands make some excellent running shoe models, neither have anything that's in the same category (if Nike had a shoe that was a mix between the Structure & Pegasus, that would be id. If Adidas had something like the SL2 but with a little more structure, that would, too.). The problem with both Nike & Adidas is that they cater to a million different niche audiences, and while both have some amazing running shoe options at the top end (e.g. Vomero Plus for cushioned daily, Alphafly / Streakfly / Vaporfly for racing, etc) their shoes targeting casual runners doing 2-5mi slow runs a couple days a week are lacking. Yes, you could go with a Pegasus, and many do, but although that's a general purpose stable-neutral running shoe, there are way better options from other brands (including Hoka & On, but also Brooks, NB, Saucony, Asics, Topo, Altra, Mizuno, and more).

Nike are too narrow for me. I alternate between Inov-8 and Topo for trail shoes. I was really happy with an older pair of Adidas Terrex Pro, but haven't tried the new Agravic options in several years.

Trail running is highly dependent on the type of trails around you. I'm in the bay area and almost everything here is dry, dusty and hard. You can generally get by with structured road running shoes if you want to, and don't need much support beyond that. I would not recommend the same thing to east coast trail runners, or most northern Europeans.

I think if you looked closer at runners you'd see many wearing Adidas, Asics, Brooks & Saucony, too. NB may be a mom/dad fashion brand with models like the 99x, 540, etc, but they're a default starting point for many runners because they're one of very few options that have wide an extra-wide across almost their entire lineup of running shoes.

You mention a handful of specific shoe models but none of them are running shoes (not even the "Night Jogger").

For my daily shoes, I have been wearing cushioned running shoes for the past ~10 years. For my daily trainers, I currently wear On Cloud Eclipse. For workout shoes I alternate between NB SC Trainer and On Cloudmonster Hyper. For trail running, Topo Vista or Inov-8 G280 (now called Trailyfly). For racing, NB SC Elite.

I also have a pair of Sambas for fashion; they're too narrow for anything else. I do light hiking in approach shoes (La Sportiva TX4 -- I ADORE these, to the point I bought their waterproof mid-height version for more extreme hiking), too!

(I am a runner, my son is a high school runner and works in a running shoe store. I'm deeper into the running shoe market than most.)

This is why it's not recommended to use providers below the Silver level in Semianalysis' rating index.

https://newsletter.semianalysis.com/p/clustermax-20-the-indu...

As an engineering leader back in the day (2000-2015) I considered it terrific value to license Telerik and others. In fact, it looks like Progress has acquired a large catalog of the most common .NET dev tools over the past ten years.

They offer inference, too. I don't know anyone who uses this service, but presumably it must be reasonably popular.

This has become a common pattern in my Claude & Gemini usage. Always require citations, then check those citations to validate they actually contain the information/data the LLM's output claims. Claude, in particular, seems to make massive logic jumps and trust tertiary data sources way more than it should.

Honestly, there's just a huge amount of thrash across the cloud & SaaS industries as a whole. It's not a lot better at Google (way behind OAI & Anthropic on coding & local AI harnesses), Microsoft (no meaningful frontier model R&D to speak of, and a constantly eroding Windows business), the neoclouds (impossible to source adequate infrastructure and are existentially threatened by the hyperscalers (especially Google) if they can't keep up with demand, chipmakers (NVIDIA vs AMD vs Qualcomm vs Apple vs the long tail of specialty shops like Cerebras), data center pure plays (legislative & community pushback, natural resources, infrastructure availability), and the SaaS folks ("let's see if AI can do it").

It's both a great time to be gold mining in tech, and also a terrible time to be a bit employee.

Maybe, but GPU is just one aspect of NVIDIA's dominance. If you are buying Vera Rubin GPUs, you're getting an NVL72 rack, which is only one of several racks that you're probably buying. You'll also need your NVIDIA racks with NVIDIA networking & storage gear, too. At the end of the day, they're "vertically integrated" for your accelerated computing data center (e.g. the "AI Factory"). This doesn't even count the software layer, where CUDA + CUDA-X (not to mention the software for all the sysadmin pieces) has a huge first mover advantage over anyone else.

How did you effectively download 500GB of photos? Did you just use Takeout?

I think it's arguable that this "intuition" you call knowledge could very easily also be called "opinion".

The interesting thing is that China began as an agrarian economy and quickly modernized via influence/direction of the CCP.

The United States used to have a dominant middle class that was geographically distributed (cities & rural areas inclusive), but the advent of the tech economy has also been having a similar effect here as it did in China: massive wealth accumulation in Tier 1-3 cities and everyone else being largely left behind.

It'll be interesting to see if there's convergence in the next decade or so, especially with the GOP reducing regulation and increasing the explicit capitalist priorities here. TBH, though, as a generally well-informed political outsider, the feeling I have is that the US government is slow, bulky, inefficient, balkanized and overall poorly run compared to the Chinese government. We'll likely either slowly improve authoritarian efficiency and become more like the CCP ... or we'll pivot left and move more toward the EU model, but we're floundering around right now paying lip service to both.

As a business user, the same thing is true for non-code documents. The biggest exertion is reducing the excessive slop down to concise, clear points.

My question is this: why is Palantir required for this kind of fairly basic data analysis? Couldn't they use much cheaper, probably open platforms for data science & analytics?

FWIW, your pessimism isn't entirely well-founded. If you're in a hot, tech-centric market, then yes, staff will be far more inclined to job hop for comp increases. But if you're hiring remote or you're located outside one of the very few tech hubs, employees value quality of life and workplace culture/relationships much more highly relative to comp. E.g. If you're hiring a SWE in Nashville or Portland or Columbus they're going to behave differently than if you're hiring in the Bay Area, Seattle area, NYC, Boston or Austin.

I disagree, or rather I don't care if I'm wrong but there are likely millions on my side. I'm 49 years old, grew up eating grilled cheese (American cheese on Pepperidge Farms white bread), often with sliced onion or tomato, and mustard. I had never even heard of a melt until I was probably 35, and even so, I still call a doctored up grilled cheese sandwich a grilled cheese sandwich. It ceases to be grilled cheese only if meat is added.

I am not impressed with that steering of the AI. In the case of Terry Tao, he clearly held a solid understanding of the problem domain and could also understand the outputs of the LLM at an expert level. He asked astute, topical questions that specifically probed areas of the problem that were either unclear or insufficiently specified, and ultimately ended up at a place where (presumably) he both understood the Jacobian Conjecture Counterexample exhaustively, and how it was derived. My impression is that through his learnings during the chat, he became sufficiently well-educated in this subdomain to apply some of these lessons to other mathematical pursuits.

In your counterexample, the human just repeatedly asked the model to "try harder". Not remotely the same.

Google already owns 14% of Anthropic.

I think this is possibly true, but the other piece of the equation is tooling. Right now, Anthropic has by far the best tooling around (for both SWEs and non-technical users), and a huge ecosystem of integrated ISVs. I'm not suggesting it will happen, but if Anthropic decided to provide options of using models beyond Claude, they'd still have a significant moat.

NVIDIA's $2b into CoreWeave was a stock purchase.

https://investors.coreweave.com/news/news-details/2026/NVIDI...

I think so, too, and I think it'll end up being a race between Apple & NVIDIA (or NVIDIA partners) to see who realizes this first. It would probably be easier for Apple to do it because it wouldn't require a form factor adjustment [over the Mac Studio they already have]. That said, NVIDIA already offers chipsets for both the lower end (DGX Spark with Vera + GB10, at roughly the $4500 price point) and higher end (DGX Station with Vera + GB300, for $85-100k). The DGX Station is equivalent to ~5-6 RTX6000 GPUs attached to a mid-range CPU server, but far more than most individual developers would want or need. I've heard through the grapevine that NVIDIA's received consistent feedback that they need something like a "GB20" that slots above the Spark/GB10 and can simultaneously run larger models for inference while hosting a dev environment on the same box. You can daisy-chain Sparks just like you can daisy-chain Mac Minis, but you're still constrained on model performance based on what a single device can accommodate.

I have 15 years experience working in high tech electronics manufacturing (for a global firm with factories around the world, including in the US and China) and can tell you the most difficult thing to manage is the supply chain. For time as much as cost, but if you have to ship a $.10 component 6000 miles to get it to a factory, it's pretty dang inefficient.

That said, you're right about cultivating new experience in younger generations. It's probably not surprising to hear that the majority of factory workers in the US are GenX or older.