you think about that? >> Yeah, I think this gets to like kind of like a fundamental philosophy of how agents and context should work. One exercise I like to do is like you know, as a human, like how many numbers can you like if I just start spitting out numbers now, right? Like how many can you remember before, you know, like you start losing track of them. I think it's actually very few, right? So, in some ways you could argue that your context window is actually shorter than these language models. And yet, you can actually be very effective at that, right? Your context is is very lossy. Um, I think one of the nice things that people are starting to realize with agents is like you have a lot of non-lossy systems that you can fall back to. So, you have a file system. Like uh if in your memory all your memory is that you read some file earlier, you don't need to remember the whole file. You maybe remember the important parts, but you can still have the full version of the file on your system. And that's kind of my goal when I'm thinking about how do we build a good context engineered harness is the harness should have everything in needs to find what it needs to have even if it doesn't have everything immediately available. >> In that case, uh do you think that the