that still interesting to people, right? Like why do we not brand every single track here? Voice agents uh you know like uh like workflow agents, computer use agents like why is every single track in this conference not an agent? Well, I think basically we want to deliver value instead of arguable terminology. So the assertion that I have is that it's really about human input versus valuable um AI output and you can sort of make a mental model of this and track the ratio of this and that's more interesting than arguing about definitions of workflow versus agents. So for example in the copilot era you had sort of like a debounce input of like every few characters that you type then maybe it will do an autocomplete u in chatbt every few queries that you type it would maybe output a responding query. Um it starts to get more interesting with the reasoning models with like a 1 to10 ratio and then obviously with like the new agents now it's like more sort of deep research notebook. Uh by the way Ryzen Martin also speaking on the product uh product management track. Um she's she's incredible on uh talking about the story of notebook LM. Um the other really interesting angle if you want to take this mental model to the stretch to stretch it is the zero to one the ambient agents with no human input. What kind of interesting uh AI output can you get? So to me that's that's more a useful discussion about input versus output than what is a workflow wise and an agent how agentic is your thing versus versus not. Um talking about AI news uh so you know it is it is like a bunch of scripts in a in a in a trench code. Um and I realized I've written it three times. I've written it for the Discord scrape. I've written it for the Reddit scrape. I've written it for the Twitter scrape. And basically it's just it's always the same process. You scrape it. You plan. You recursively summarize. You format and you evaluate. Um and and yeah, that's the three kids in the trench coat. Um and that's really how what it is. I run it every day and like we improve it a little bit, but then I'm also running this conference. Um so if you generalize it, that actually starts to become an interesting model for building AI intensive applications where you start to make thousands of AI calls to serve serve a particular purpose. Um so you sync you plan and and you sort of parallel process you analyze and sort of reduce that down to uh from from many to one and then you uh deliver uh deliver the contents um to the to the user and then you evaluate and to me like that conveniently forms an acronym SP AD um which is which is really nice. There's also sort of interesting AI engineering elements that are that are fit in there. So you can process all these into a knowledge graph. you can um turn these into like structured outputs and you can generate code as well. So for example um you know chat GBT with canvas or cloud with um artifacts is a way of just delivering the output as a code artifact instead of just uh text output and I think it's like a really interesting way to think about this. So this is my