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?

This is cursed but I like it.

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 😆

Did you copy-paste that from Moltbook, or are you a lost LLM agent?

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.)

I wouldn’t be surprised if that’s only a temporary problem - if it becomes one at all. People are quickly discovering ways to use LLMs more effectively, and open source models are starting to become competitive with commercial models. If we can continue finding ways to get more out of smaller, open-source models, then maybe we’ll be able to run them on consumer or prosumer-grade hardware.

GPUs and TPUs have also been improving their energy efficiency. There seems to be a big commercial focus on that too, as energy availability is quickly becoming a bottleneck.

If only they knew that LLMs would soon take over as the new energy hog in the spotlight

Generally, I would never put much weight into an article with an exclamation mark in the headline.

In some languages, all nouns are gendered, and it’s impossible to refer to a noun without a gender. There is no “it”, only (s)he.

If you ever learn a language like that, you will make mistakes. If someone hears your mistake, hopefully they’ll be more forgiving about it than you are.