I want to be clear, you can't throw edtech in. You can't take our time back and throw it into a school. It's not going to work cuz you haven't solved any of the other problems. And so edtech is not a magic, you know, it's not a silver bullet.
I think evals, they outlive the harness a little bit, but not by that much. Like an eval might live for maybe one, two, three model generations, but nowadays the you know, we're on the exponential. The model is improving so quickly, very often we just saturate the eval, and then we have to throw it away, and we have to come up with a new eval.
I do the research I do the plan I do the implementation I throw the docs out and the next time I need research I just do it from scratch because tokens are cheap and my time is expensive
I did try Dell's laptops a few years back, didn't like what I saw, and ended up spending a few years using Framework computers instead (they're still great too).
Since I switched the monitor, I have never experienced a failure. The system works perfectly, flawlessly waking the laptop and bringing the desktop online in under a second.
So, we're going to see people get into those where they're like, "Well, I vibe code the tip of the iceberg. I throw away the rest of the iceberg and now I'm in trouble because now I don't know what to do. Now I get to these downstream problems that I didn't even know existed." So, we're going to see on the on the side of the the the vibe coding replacers, we're going to see that kind of a naivity play out.
the genie runs out of g runs itself out of options it can't make further forward progress and so I'll wipe it away start over I won't try and tweak I'll start over and say all right well if I implement things in a different order. If I implement with this markdown file or if I implement it with this commit hook, we collectively need to try absolutely everything.
I believe that a Mac mini with Codex running on it is the best bang for your buck if you’re building an always-on agentic setup based on a frontier model that can do just about anything you throw at it.
I believe that a Mac mini with Codex running on it is the best bang for your buck if you’re building an always-on agentic setup based on a frontier model that can do just about anything you throw at it.
I agree with @theo completely, it's clear to me this is where it's going, also seeing @karpathy with Claude moving to the cloud (via Slack etc), I think AI "agents" and AI coding will operate on servers / from the cloud first
But in terms of like features, like a website like you can just throw features in there nowadays that nobody really cares about and you can you can do it so quickly. Like a new feature every single day, but do people actually care about that? Is that making it better? It could be making things worse.
I think Google has pretty much gone to on-sites at this point, back to the traditional whiteboard format. And they'll let you code on a laptop if you want to, as well, but it's going to be in person. Somebody's going to be watching you code, and you're probably not going to be able to cheat your way through that.
Gamified learning is a bit like wrapping medicine in candy. Yes, it may help some students swallow some instruction they otherwise find bitter, but in practice it’s easy to pull off the candy, consume it, and throw the medicine away.
I was not impressed with the DGX Spark; it's described as an "AI supercomputer on your desk" but in reality it has lower tokens/sec than a good laptop GPU - and on top of that, you have to figure out the networking details of how to connect to it from your actual work device etc.
and so I don't think TSMC would kick out Apple. I think Apple will become a smaller and smaller and smaller percentage of TSMC's revenue and therefore be less relevant for TSMC to cater to their demands.
But with AI, it’s different: it’s often only a couple of years between when you need a world-class computing cluster to do something and when you can run it on a high-end gaming chip on your laptop.
o3's improvement over the GPT series proves that architecture is everything. You couldn't throw more compute at GPT-4 and get these results. Simply scaling up the things we were doing from 2019 to 2023 -- take the same architecture, train a bigger version on more data -- is not enough.
So you’re using the power of the sun to split water, take out the hydrogen, stick it onto CO2, and the oxygen is a waste product and you just throw it out, throw it away. So it’s the single greatest planetary pollution event in the whole history of the earth.
It's incredibly written, deeply complex, confusing as hell, and you'll probably throw it at the wall when it's done. But then you'll kinda go "okay... I'm glad I did that" and maybe start it again.
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