Sure. I was mostly just trying to answer your question and clear up some of the confusion. In all continent models, Canada and the USA are indeed on the same continent 🙂

Oh that was centuries ago. In the US, we’ve even divided states in a similar manner over the years. The Province of Carolina became North Carolina and South Carolina. Dakota Territory -> North and South Dakota. Virginia and… West Virginia (not sure why no “East Virginia”).

I suppose you can say it’s “wrong” on the Internet, but if you tried the six-continent model on a school test in an English-speaking country, it would likely be marked as incorrect.

There are also five- and I think even four-continent models in some cultures. The thought of any of them being “wrong” is really interesting, to me. Personally, I’ve never really thought about any of them as “right” or “wrong”, just cultural differences.

Most English-speaking countries teach the seven-continent model. There is no place named “America,” so when native English speakers omit “The United States of” for brevity, other native English speakers understand that they’re referring to the USA.

It seems to cause some confusion with cultures that are taught the six-continent model (in which there is a continent called “America”). I would guess that it’s because a lot of people are unaware of these cultural differences.

Edit to actually answer your question 😅: In English, Canada is in a continent called “North America”

Ah, good call. I didn’t think about that being self-promotion, but I suppose I can understand it.

Good luck! 🙂

Are brass nails expensive? If you were to drop a Monero wallet address in this thread, I wouldn’t be surprised if you eventually gain the ability to buy a few 🙂

There’s an extra “.” in your link. Here’s a corrected link for my fellow lazies: AutoEQ

Brazil has a payment system (called Pix, IIRC) that seems to work well, and has survived some… questionable leadership.

I don’t know much about it (maybe a Brazilian can say more about it), but it seems to serve the businesses very well there.

Oh I wasn’t wishing for anything, just pointing out the possibility. There are some Chinese companies gearing up to fill the gap in the memory market. GPUs would be much harder, but maybe very profitable.

Maybe some Chinese manufacturer will find a way to fill the gap in the market

Huh. Apparently a few of them exist. TIL.

Chad looks like orange Epstein

If this is the same story I’m thinking of, she and her partner wanted to be the first vegans to climb to the top, to show the world that vegans can do it. Coincidentally, another vegan actually did it like 1-2 days before their attempt.

This app has to be installed and collected people’s information? Why?

Amazon has them in customer support

The security posture of Moltbook itself is pretty hilarious. For example, the database is (or was — not sure if fixed by now) wide open to the public 😆

Yeah true. I’m assuming (and hoping) that the problems with consumer grade hardware being less accessible will be temporary.

I have wristwatches with significantly higher CPU, memory, and storage specs than my first few computers, while consuming significantly less energy. I think the current state of LLMs is pretty rough but will continue to improve.

Yep, it’s a tool for engineers. People who try to ship vibe-coded slop to production will often eventually need an engineer when things fall apart.

Can you provide evidence the “more efficient” models are actually more efficient for vibe coding? Results would be the best measure.

Did I claim that? If so, then maybe I worded something poorly, because that’s wrong.

My hope is that as models, tooling, and practices evolve, small models will be (future tense) effective enough to use productively so we won’t need expensive commercial models.

To clarify some things:

• I’m mostly not talking about vibe coding. Vibe coding might be okay for quickly exploring or (in)validating some concept/idea, but they tend to make things brittle and pile up a lot of tech debt if you let them.
• I don’t think “more efficient” (in terms of energy and pricing) models are more efficient for work. I haven’t measured it, but the smaller/“dumber” models tend to require more cycles before they reach their goals, as they have to debug their code more along the way. However, with the right workflow (using subagents, etc.), you can often still reach the goals with smaller models.

There’s a difference between efficiency and effectiveness. The hardware is becoming more efficient, while models and tooling are becoming more effective. The tooling/techniques to use LLMs more effectively also tend to burn a LOT of tokens.

TL;DR:

• Hardware is getting more efficient.
• Models, tools, and techniques are getting more effective.

Oh, sorry, I didn’t mean to imply that consumer-grade hardware has gotten more efficient. I wouldn’t really know about that, but I assume most of the focus is on data centers.

Those were two separate thoughts:

1. Models are getting better, and tooling built around them are getting better, so hopefully we can get to a point where small models (capable of running on consumer-grade hardware) become much more useful.
2. Some modern data center GPUs and TPUs compute more per watt-hour than previous generations.

They don’t need the entire project to fit in their token windows. There are ways to make them work effectively in large projects. It takes some learning and effort, but I see it regularly in multiple large, complex monorepos.

I still feel somewhat new-ish to using LLMs for code (I was kinda forced to start learning), but when I first jumped into a big codebase with AI configs/docs from people who have been using LLMs for a while, I was kinda shocked. The LLM worked far better than I had ever experienced.

It actually takes a bit of skill to set up a decent workflow/configuration for these things. If you just jump into a big repo that doesn’t have configs/docs/optimizations for LLMs, or you haven’t figured out a decent workflow, then they’ll be underwhelming and significantly less productive.

(I know I’ll get downvoted just for describing my experience and observations here, but I don’t care. I miss the pre-LLM days very much, but they’re gone, whether we like it or not.)