I'm willing to suffer through the transition, but so help me God, if they come out with a USB-D port right after we finish transitioning to USB-C, then that's it! I'm never doing this again!

Possibly, even more practical might be an airship. Existing airships can already lift up to about 100 tons, and they can land in any large open space.

It seems to me that some kind of heavy lift helicopter solution might make more sense. My understanding is that a reliable 100m turbine blade can be made weighing about 35 tons. Although the most capable current helicopters can only accommodate an external lift weight of about 20 tons, it seems easier to build a more powerful helicopter than a massive aircraft that can land on a makeshift dirt runway.

If you're really stuck needing to buy a drink, I've never seen a cooler at a store that didn't sell water bottles right next to the soda pop. I'm not sure what the psychological reason is, but people think they're getting a better deal spending $2 on a coke instead of $2 on water because they're getting sugar and flavor. Nobody needs that extra one or two hundred calories, and if you're thirsty, water is always going to quench the thirst. You're not paying for the contents. You're paying for the bottle, the branding, and the fact that it's refrigerated.

While not quite as energy dense, LiFePO4 (aka LFP) batteries are increasingly common, and they don't pose the same thermal runaway risk that lithium ion batteries have. It's worth looking for this type of battery in devices you're buying from now on.

It's easier to just assume you're right about everything and avoid trying to understand facts and evidence.

"Alpha is a measure of an investment's performance that indicates its ability to generate returns in excess of its benchmark." - from here. So it means your ability to beat the market by picking good stocks.

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.

Companies that pay dividends tend to be bigger well established mature companies that have a large market share and aren't investing much in growth, which means they tend to hold value pretty well in a crash. They also tend to fare a little better in periods of high inflation, and we're going to see higher inflation for the foreseeable future. But you aren't going to average better than a 5% market return, and that requires a million dollars in assets to get that rather small $50,000 per year income.

What you're talking about is called price collusion, and while it does happen, it's also illegal and does get prosecuted (I can think of two examples in recent memory).

AI is just the scapegoat. The real reason is that tech companies used to be flooded with cheap capital, and now capital is significantly more expensive so they're strapped for cash. The old model was to grow at all costs and burn through investment money to gain market share. The new financial reality is that they have to be profitable today. You used to be able to hire a new grad with the understanding that they wouldn't be that useful in the first year or two, but you needed them to support future growth. Now you need people to be productive immediately.

Yes, in the very short term. But you're completely ignoring the competition factor. Someone else will buy an excavator and force the market price of digging lower.

Yes... but when deployed broadly across industries it also supports higher worker efficiency and productivity, which allows higher wages. And it's not like the unemployment rate went up to 30% and stayed there. A couple years ago we had record low unemployment. If all the jobs where gone due to automation, what were all those people doing working? I don't actually believe there's much behind the AI hype. We're poised for a big correction. But automation, in general, has improved worker productivity and driven wage increases. But I admit that the wages have not increased nearly as fast as worker productivity.