vibe coding is not dead: Non-eng teams inside of tech companies are just getting started with this approach, building on top of internal “harnesses for non eng teams” They are also learning limitations of “vibe coding” v quickly!
AI has always been very jagged, and we are making models which are superhuman goal-seekers at math and software engineering, but they have massive limitations on intuitions, creativity, and other types of reasoning that humans are strong at.
Superlogical will begin by shipping a terminal multiplexer. I'm pouring my years of experience building a terminal and studying the potential and limitations of other multiplexers into something new, powerful, and, of course, fast.
But, what's not been successful is learning about uh biology and biological change. And one big reason for that has been that the sample sizes have been too small.
The the chatbot is already running into limitations of, you know, making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that, you know, the models are are the models are good enough and they're they're, you know, them getting better doesn't matter to the economy.
all of the samples you get from models are silently collapsed. They're silently, this is not obvious if you look at any individual example of it. They occupy a very tiny manifold of the possible space of um sort of thoughts about content.
That's how you end up in an endless loop of ever-increasing taxes, ever-increasing regulation, which ultimately suffocates free market, free enterprise, and free speech. So, you do want to have very, very strict limitations on the extent the government can increase its powers at the expense of citizens.
A nice personal + historical deep dive into finding your dharma by embracing both your gifts and limitations in service of something greater than yourself.
And what they found was if that search space, if the sky is an ocean and you're looking for fish, how much of the ocean have we looked at, and it turns out to be a hot tub. That's how much of the ocean that we've looked up. We've dragged a hot tub's worth of ocean water up and there was no fish in it
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
Next to proper ownership of your own work, my favorite thing about POSSE is that it massively expands what you can publish, far beyond the limitations imposed by most social media (and especially microblogging) platforms that try to enforce some uniformity in post style, length, and appearance.
Both the genome sequences of pandemic viruses and step-by-step protocols to make infectious samples from synthetic DNA are now freely available online.
I suspect that in the future, it'll be easier to get models to output exactly what we need with minimal prompting, and these techniques will become less important.
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