This is retarded.

1. It isn't for profit healthcare that is the problem, it's THIRD PARTY PAY.
2. I don't use third party pay, ever, for healthcare. I've been insured nonstop for over 30 years, and NEVER ONCE has my insurer paid my doctor.
3. Even when I've had emergencies, I still called around, negotiated a fair cash up front rate, paid cash up front, and billed it to my insurer. My cash up front rate was sometimes below any co-pay negotiated with my insurer, lol.

I just recently had some elective surgery that would have cost me about $2000 on my annual deductible, but I was able to cash pay a negotiated rate of $400 including a follow-up "free". I submitted the $400 to my insurer and they reimbursed me.

Third party insurance exists because YOU VOTERS demanded the HMO Act of the 1970s, which tied health care to employment, and then employers outsourced it to third parties.

Health care is remarkably cheap in the US (cash pay, negotiated) and I don't have to wait months to see a doctor when I call and say I am cash pay. They bump me up fast.

Socialism with American Characteristics

Iowa's rolling farm fields of coffee

I have struggled with how best to approach my variant of the problem - I don’t switch phones regularly, but I do carry a spare on international trips where they don’t have Verizon stores. I would have to find WiFi to provision the backup phone with the Verizon app.

Indeed, I fully agree. The funny thing is, monthly numbers would help us move away from the distortions of the quarterly cycle. If key data reporting becomes frequent enough, you can't get into a cycle of "do adverse-numbers stuff early in the quarter and then cram positive-numbers stuff into the end of the quarter". You have to - *gasp* - just run your business normally.

Some businesses could still manage to switch to a monthly cycle, but anyone who deals significantly in transoceanic feedstocks/parts/goods shipments won't be able to.

BLS numbers aren't some sort of dark art. They're literally just the compiled numbers reported by companies. Numbers are what they are. To fight against jobs numbers is to fight against reality.

People get confused by the existence of revisions. The problem is that not all data gets reported in a timely manner. When late data comes in, it causes revisions to the earlier reported numbers, either up or down.

Firing the head of the BLS because you don't like what numbers US companies reported is just insane Banana Republic-level nonsense.

Yes, he fired the same person who was ultimately responsible for putting out crap numbers.

US reporting has always been the gold standard. Nobody has accused the BLS of "crap numbers" until Trump decided he didn't like them. It's is so way outside the norms it doesn't even resemble something that could conceivably happen in the US; this is banana republic-level stuff.

Yeah, as an investor, my reaction to this proposal was, oh HELL no.

TL / DR:

BLS chief: "Jobs are turning bad now."

Trump: "Fake news, you're fired. I'm appointing a January 6th rioter conspiracy theorist to head the BLS!"

BLS today: "Jobs were ALREADY bad!"

The fact that you take at face value a revision of Biden-era job numbers, immediately after Trump fired and replaced the BLS chief explicitly because he published jobs numbers Trump didn't like, is....

Well, it's certainly a choice.

I was thinking the same thing. What sort of an idiot will take US jobs numbers at face value right after Trump fired the Bureau of Labor Statistics chief after he reported numbers that Trump didn't like?

If you spend time with the higher-tier (paid) reasoning models, you’ll see they already operate in ways that are effectively deductive (i.e., behaviorally indistinguishable) within the bounds of where they operate well. So not novel theorem proving. But give them scheduling constraints, warranty/return policies, travel planning, or system troubleshooting, and they’ll parse the conditions, decompose the problem, and run through intermediate steps until they land on the right conclusion. That’s not "just chained prediction". It’s structured reasoning that, in practice, outperforms what a lot of humans can do effectively.

When the domain is checkable (e.g., dates, constraints, algebraic rewrites, SAT-style logic), the outputs are effectively indistinguishable from human deduction. Outside those domains, yes it drifts into probabilistic inference or “reading between the lines.” But to dismiss it all as “not deduction at all” ignores how far beyond surface-level token prediction the good models already are. If you want to dismiss all that by saying “but it’s just prediction,” you’re basically saying deduction doesn’t count unless it’s done by a human. That’s just redefining words to try and win an Internet argument.

They do quite a bit more than that. There's a good bit of reasoning that comes into play and newer models (really beginning with o3 on the ChatGPT side) can do multi-step reasoning where it'll first determine what the user is actually seeking, then determine what it needs to provide that, then begin the process of response generation based on all of that.

This is not a surprise, just one more data point that LLMs fundamentally suck and cannot be trusted.

Huh? LLMs are not perfect and are not expert-level in every single thing ever. But that doesn't mean they suck. Nothing does everything. A great LLM can fail to produce a perfect original proof but still be excellent at helping people adjust the tone of their writing or understanding interactions with others or developing communication skills, developing coping skills, or learning new subjects quickly. I've used ChatGPT for everything from landscaping to plumbing successfully. Right now it's helping to guide my diet, tracking macros and suggesting strategies and recipes to remain on target.

LLMs are a tool with use cases where they work well and use cases where they don't. They actually have a very wide set of use cases. A hammer doesn't suck just because I can't use it to cut my grass. That's not a use case where it excels. But a hammer is a perfect tool for hammering nails into wood and it's pretty decent at putting holes in drywall. Let's not throw out LLMs just because they don't do everything everywhere perfectly at all times. They're a brand new novel tool that's suddenly been put into millions of peoples' hands. And it's been massively improved over the past few years to expand its usefulness. But it's still just a tool.

Integrated thermal stores with nuclear is more difficult (limited temperatures and limited dT on storage = big storage needed with lots of heat exchange surface area), but if they can make it economical, it could be a game changer for nuclear's problematic economics. The ability of nuclear to switch from baseload (not matching the demand curve, let alone the curve of really-cheap renewables) to load-following and even peaking could make the mean sale value of its electricity much higher, and make it much more useful as a compliment to renewables.