The most common pushback I'm getting to this post is that taking this kind of feedback from an LLM is going to bleach out your style just like having the model generate the text, because models can't be trusted with "style". No.
Normies don't vibe code, they just ask something like "do my bookkeeping" or "file my tax" or "organize a movie night and send invites" or "generate a flyer for movie night" or "edit my video" They don't ever see code, vibe code, or do anything with code, their AI chat app just does it for them
Well, spatial intelligence eventually must enable us to both generate what the space is, reason within it, and being able to edit and interact within it.
And I do believe Atlas is a significant step forward because now with every single frame, you have a you can generate an estimate a important piece of information, which is the the view viewpoint, the camera pose. And that is the most critical information one needs about the geometry of the of the space.
But keep those methods you use to investigate things and monitor things very separate from the methods you use to generate reward which is something that AI companies including open AAI have held up as a principle especially in the case of avoiding putting training pressure on the chain of thought. So you might have monitors that read the agents chain of thought in order to alert you if something is going wrong somewhere but you don't train the agents with the outputs of that monitor.
We found that AIs are actually quite creative and that they generate more commercially viable ideas than groups of humans, but those ideas are very similar to each other.
Today is a very historical moment for AI video generation You can now generate AI video faster than you can watch it Before it'd take let's say 2-5 minutes to generate 15 seconds of video @fal made a post-trained Minimax H3 variant called Max which is 50x faster than the original but still maintains quality It generates 15 seconds of video in 9 seconds!
now that it is very easy for anyone to use an agent to create a body of text, I think it's more important than ever for us to make sure that the ideas we're putting out in the world are actually worth people reading.
For the first time, AI has "hands" - that is, the ability to reach out and interact with the tools on your computer and in your browser, just as you would.
Like that just does not seem like the ideal artifact. We should be able to store them somewhere. We should be able to have architecture diagrams that we can review and discuss that generate that code to spec.
Coding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks off.
Like this is actually like a new form of test time compute. Like when we talk about the scaling laws and kind of we talk about the model getting more intelligent over time, historically, it's been a function of the size of the neural net, the amount of training data, and the number of flops that you put in to the training. And then recently, we also added test time compute. So this is essentially a fancy way a researcher way of saying how many tokens does it generate. And now dynamic workflows are essentially a new way to orchestrate test time compute.
And uh it's still worth it's still worth not reading the code for most of the time at the cost of every once in a while I'm going to have to spend two weeks fixing an issue by hand. And I don't believe that anymore because I think the amount of code we're able to write now is actually like 10xed or 100xed and I think the problem's just getting worse.
However, these LLM reasoners that generate informal reasoning in natural language are fundamentally limited by the lack of precise, machine-checkable semantics, making their outputs prone to hallucinations [Huang et al., 2025b] and precluding autonomous verification, a prerequisite for tackling open-ended mathematical research.
I think the way that you'd measure conjecture generating ability is going to be more subjective on like that tone shift where um it'll be mathematicians saying they're not just using it to like solve their problems, but as they step back and decide what their research field should even be that a conversation with such and such model like was genuinely helpful for that.
If this were $200 I'd complain, but not for $25. For that, it's a neat little box that maybe more people could contribute to, to make it the most handy way to jack in from a crash cart.
In the face of weakened renewable roll-out in Australia, the most likely outcome is simply that coal and gas just generate more, and for many more years past their shut down date.
So, we're now in a situation where suddenly people can generate thousands of theories for a given scientific problem. And now we have to to verify them, evaluate them and this is something which we we have to to change our structures of science to actually sort this out.
Small studios are the future of gaming. The big studios basically acquire the small studios for new IP and ideas, and the small studios grow in. The really compelling, new, innovative ideas are gonna come out of small studios.
But the quality of these systems isn't just in the code they write. It's also in the code I don't have to write. And maybe more importantly, their actual value is in the code I would never have written, or could never have written, or never would have wanted to write.
We show that equilibrium generically occurs at neither the Harberger nor Glaeser-Luttmer benchmark. Cost-minimizing suppliers drive allocations to vertices, not interiors. Corners are not an assumption but an outcome about what cost-minimizing suppliers choose. The correct benchmark is corners, not random, and corners generate qualitatively different welfare properties: losses far larger than either efficient or random distributions, and discontinuous jumps from small parameter perturbations.
I think that there will be lots of work for us cleaning up after the slop, but if you know what you're doing AI augmented development is going to get you some amazing results
When applications can generate capabilities on demand, the definition of "what this product does" becomes more fluid. Features aren't just what shipped in the last release, they're also what users will ask for in the next session.
And in fact, I do like Moana very much. It’s my favorite film out of Disney Animated Studios in the last decade, and even (barely) edges out Coco when you include Pixar in the mix
Data centers are so much more efficient with their water that they generate 50x as much tax revenue per unit of water used than golf courses in the county:
You see this for the music now. You can generate new music. It could generate new stuff that you wouldn't know. For an LLM variance is bad. originality is is is a lower score. So what's the feedback loop for original but good?
Do you have enough data to make simulations, so that you can create more synthetic data that are from the right distribution? Obviously that's the key. So you need enough real-world data in order to be able to create those kinds of data generators, and I think that we're at that step at the moment.
It's a lot easier for someone to engage with an argument if they generated the key steps themselves by answering my questions - if imposed by me, it sparks contrarianism and defensiveness
I think there's this whole idea, I call it a denial of attention. I think there's an entire attack vector that's going to be happening. We're using LLMs to generate fake bug reports, fake all these things to just actually effectively to demotivate and hurt open source maintainers.
And humans are actually very good at reading or judging between two things versus... This goes back to the core of what RLHF and preference tuning is that it's hard to generate a good answer for a lot of problems, but it's easy to see which one is better.
The selling point of generative A.I. is that these programs generate vastly more than you put into them, and that is precisely what prevents them from being effective tools for artists.
We cannot sustain the most stringent environmental and social norms, invest less than our competitors, have a more naive trade policy than them and think that we will continue to generate jobs. This no longer holds together.
I think the question behind that question is, do people who create valuable data deserve to have some way that they get compensated for use of it, and that I think the answer is yes. I don’t know yet what the answer is. People have proposed a lot of different things. We’ve tried some different models. But if I’m like an artist for example, A, I would like to be able to opt out of people generating art in my style. And B, if they do generate art in my style, I’d like to have some economic model associated with that.
The thing about the far-right parties is that they are trying to get people to equate all foreigners in a country with a threat to our future, but they are only gaining ground because of their ability to generate fear.
Text is a much more powerful mode for model outputs. A model that can generate images can only be used for image generation, whereas a model that can generate text can be used for many tasks: summarization, translation, reasoning, question answering, etc.
the research on like brainstorming where all brainstorming together the research shows that brainstorming does not produce more ideas or more innovative ideas. And I think teams would be and groups would be better having individuals generate their own concepts and then after they've done it independently and privately sharing it with the others.
For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users.
If you're looking to understand the basics of building and scaling a sales process and team, this book is a great primer. It also goes into great detail in how the author built a better, more reliable method than pure cold-calling to generate leads beyond the marketing team at Salesforce's efforts.
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