I’d posit the algorithm has turned it into a monster.

Attention should be dictated more by chronological order and what others retweet, not what some black box thinks will keep you glued to the screen, and it felt like more of the former in the old days. This is a subtle, but also very significant change.

On the other hand, the track record of old social networks is not great.

And it’s reasonable to posit Twitter is deep into the enshitifiication cycle.

Still perfectly runnable in kobold.cpp. There was a whole community built up around with Pygmalion.

It is as dumb as dirt though. IMO that is going back too far.

People still run or even continue pretrain llama2 for that reason, as its data is pre-slop.

The facebook/mastadon format is much better for individuals, no? And Reddit/Lemmy for niches, as long as they’re supplemented by a wiki or something.

And Tumblr. The way content gets spread organically, rather than with an algorithm, is actually super nice.

IMO Twitter’s original premise, of letting novel, original, but very short thoughts fly into the ether has been so thoroughly corrupted that it can’t really come back. It’s entertaining and engaging, but an awful format for actually exchanging important information, like discord.

This is called prompt engineering, and it’s been studied objectively and extensively. There are papers where many different personas are benchmarked, or even dynamically created like a genetic algorithm.

You’re still limited by the underlying LLM though, especially something so dry and hyper sanitized like OpenAI’s API models.

To add to this:

All LLMs absolutely have a sycophancy bias. It’s what the model is built to do. Even wildly unhinged local ones tend to ‘agree’ or hedge, generally speaking, if they have any instruction tuning.

Base models can be better in this respect, as their only goal is ostensibly “complete this paragraph” like a naive improv actor, but even thats kinda diminished now because so much ChatGPT is leaking into training data. And users aren’t exposed to base models unless they are local LLM nerds.

I don’t know when the goal post got moved

Ken Paxton, at least?

BTW, as I wrote that post, Qwen 32B coder came out.

Now a single 3090 can beat GPT-4o, and do it way faster! In coding, specifically.

Yep.

32B fits on a “consumer” 3090, and I use it every day.

72B will fit neatly on 2025 APUs, though we may have an even better update by then.

I’ve been using local llms for a while, but Qwen 2.5, specifically 32B and up, really feels like an inflection point to me.

Yeah, well Alibaba nearly (and sometimes) beat GPT-4 with a comparatively microscopic model you can run on a desktop. And released a whole series of them. For free! With a tiny fraction of the GPUs any of the American trainers have.

Bigger is not better, but OpenAI has also just lost their creative edge, and all Altman’s talk about scaling up training with trillions of dollars is a massive con.

o1 is kind of a joke, CoT and reflection strategies have been known for awhile. You can do it for free youself, to an extent, and some models have tried to finetune this in: https://github.com/codelion/optillm

But one sad thing OpenAI has seemingly accomplished is to “salt” the open LLM space. Theres way less hacky experimentation going on than there used to be, which makes me sad, as many of its “old” innovations still run circles around OpenAI.

As a fervent AI enthusiast, I disagree.

…I’d say it’s 97% hype and marketing.

It’s crazy how much fud is flying around, and legitimately buries good open research. It’s also crazy what these giant corporations are explicitly saying what they’re going to do, and that anyone buys it. TSMC’s allegedly calling Sam Altman a ‘podcast bro’ is spot on, and I’d add “manipulative vampire” to that.

Talk to any long-time resident of localllama and similar “local” AI communities who actually dig into this stuff, and you’ll find immense skepticism, not the crypto-like AI bros like you find on linkedin, twitter and such and blot everything out.