Open source training data Blender beats all its competitors because it's optimal in the most important dimension: agents can train on it and use it efficiently. OSS wins.
LAST WATCHED: SISU 2: ROAD TO REVENGE (2025) is, sadly, a more conventional film than the original. Still lots of bloody fun, but too similar to the original in its beats to be as enjoyable, trying harder to be “emotional” while losing some of the little textual depth that made the first film surprising.
we see that the across the board the single PIO like pre-trained PIO7 model matches or outperforms the fine-tuned specialists that were developed with reinforcement learning post-training for those downstream tasks.
I would say that I expect like full automation of AR&D perhaps somewhere around like 2031 2030 and then getting to like the like beats all humans on the job milestone. Maybe I expect median around 2033
So this speaks of a very important fact that um AI learns from patterns. When the patterns are not abundant, then we have to be careful. We have to know how to use AI or how not to use AI.
That's a hell of a lot slower than knowing it in cognitive L1 cache. And you can have way more round trips in your brain than you can, you know, muttering through, you know, super whisper or typing it out or whatever.
And at the end of the day, they're all like different ways to pass tokens into a model and ask it to produce usually some structured output. And understanding that is a lot more powerful than trying to learn memory and trying to pick some agent framework off the shelf and some memory framework off the shelf.
We made this something useful for adults and we had this user pay model that could monetize your engagement instead of trying to show you an ad and getting you to leave the game to go somewhere else. It actually the more engaged you were, the more likely you were to, you know, spend money.
So, if we're checking 99% instead of 100%, well, heck, that's better than the 0% that JavaScript checked, right? And it gives you like language features that no other languages can provide because they can't get to 100%.
Perfect friends are not possible. Instead of wondering how to change them, perhaps try being just a tiny bit more patient, and see how they might end up surprising you.
That's great, but I don't think it can replace a human poking at virtual machines; my experience with EC2 is that images break in all sorts of wild ways, and nothing beats a human for saying "huh, I have no idea what's going on here but something seems weird".
And I think we'll see the same thing with automation where uh basically robot plus human is much better than just human or just robot. Uh and and that just like makes total sense. It also makes it much easier to get all the technology bootstrapped because when it's robot plus human, now there's a lot more potential for the robot to like actually learn on the job, acquire new skills.
They are not useful in industrial environments where, if a humanoid robot can do it, an industrial machine which is specialized in the task can do it even better.
But the idea that is this never going to be a thing. I think that's a dangerous thought. We we can't start thinking that way because at what point do you stop second guessing yourself?
"Direction is more important than magnitude"-it's usually better to have a lower-velocity project that works on the right things, than a higher-velocity one that's pointed at the wrong goal.
A book I recommended after reading it, writing: “If you're a senior engineer wondering what the next level is, a staff-level engineer or a manager of staff engineers, this book is for you."
There is an algorithm called BM25 precisely for this, which is a more sophisticated version of TF-IDF. TF-IDF is term frequency times inverse document frequency, a very old-school information retrieval system that just works actually really well even today. And BM25 is a more sophisticated version of that, that is still beating most embeddings on ranking.
if I just try to do half-an-hour and then later in the day I do half-an-hour, I won’t retain that information as long as if I do half-an-hour today and half-an-hour one week from now. So doing that extra spacing should help me retain the information better.
This is the 2001 monolith hypothesis. I would be less surprised to find a quiescent alien artifact in our solar system than I would to catch a radio signal from an intelligent civilization.
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