Thanks

Would you mind elaborating a little more? I live under a rock but this seems interesting

Oh that’s an interesting challenge.

I hear some LLMs now have some solutions for the classic “how many Rs in ‘strawberry’” problem (related to the tokenization processes), but I have no idea how they might solve the phonetic thing. I’m sure some smart people will eventually find a way though

This may sound weird, but I think anecdotal evidence might be more informative than the productivity stats for now, until the industry settles on a new equilibrium.

Some engineers are more productive with AI, and some (maybe even most, still) are less productive. People are still putting in the effort to learn how to use it more effectively/productively (there is a learning curve), and some of the less productive are getting laid off.

It sucks, but that’s just how it is now.

Also, AI tooling is still evolving very rapidly. A lot of information and stats are only valid for maybe a few months.

It is, intentionally. Some of the training data is synthetic

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Why Deepseek?

Maybe they’d prefer “Macroslop”?

I remember noticing YouTube’s software quality taking a nosedive when they shoved Shorts into the app. At the time, it seemed like it was hastily done as a reaction to TikTok’s explosion, but it’s still a buggy POS , even after all these years.

I wouldn’t be surprised to hear about some OpenClaw-like agent doing something like that. Some people are pretty reckless with it

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

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.