Neil Patel

We convince you to store it for us for multiple years. You're getting some value out of that data yourself because you know the data and therefore you may be using it for logging or building Stripe dashboards or whatever you're doing inside of Axiom. But now the onus is on us to go and give you things you never thought about that you could do with it.

We convince you to store it for us for multiple years. You're getting some value out of that data yourself because you know the data and therefore you may be using it for logging or building Stripe dashboards or whatever you're doing inside of Axiom. But now the onus is on us to go and give you things you never thought about that you could do with it.

We convince you to store it for us for multiple years. You're getting some value out of that data yourself because you know the data and therefore you may be using it for logging or building Stripe dashboards or whatever you're doing inside of Axiom. But now the onus is on us to go and give you things you never thought about that you could do with it.

And so much of the rest of this year for us is presenting back to the user that, hey, you trusted us with this initial part of the journey. Here's now all the useful things you can do with this data, which you may have not even thought Axiom is a place to do it. But now you can do that. So whether it's

And so much of the rest of this year for us is presenting back to the user that, hey, you trusted us with this initial part of the journey. Here's now all the useful things you can do with this data, which you may have not even thought Axiom is a place to do it. But now you can do that. So whether it's

And so much of the rest of this year for us is presenting back to the user that, hey, you trusted us with this initial part of the journey. Here's now all the useful things you can do with this data, which you may have not even thought Axiom is a place to do it. But now you can do that. So whether it's

forecasting whether it's having functions that run on schedules or because of a reaction that happens inside of your data store an event that's created all these different things where actually the bread and butter of everything are these events but they've never been in a place where they're all together you can get cross like dimensional kind of context between the different streams and then be able to do new and interesting things on top of that and so that's what's the most exciting thing because we're finally on that

forecasting whether it's having functions that run on schedules or because of a reaction that happens inside of your data store an event that's created all these different things where actually the bread and butter of everything are these events but they've never been in a place where they're all together you can get cross like dimensional kind of context between the different streams and then be able to do new and interesting things on top of that and so that's what's the most exciting thing because we're finally on that

forecasting whether it's having functions that run on schedules or because of a reaction that happens inside of your data store an event that's created all these different things where actually the bread and butter of everything are these events but they've never been in a place where they're all together you can get cross like dimensional kind of context between the different streams and then be able to do new and interesting things on top of that and so that's what's the most exciting thing because we're finally on that

journey of bringing together ideas from the past ideas from the world we live in now putting them on top of axiom and then putting them out to customers as quickly as possible

journey of bringing together ideas from the past ideas from the world we live in now putting them on top of axiom and then putting them out to customers as quickly as possible

journey of bringing together ideas from the past ideas from the world we live in now putting them on top of axiom and then putting them out to customers as quickly as possible

As the team, that kind of efficiency, even as we build the org, as we use the things that we've made, dog food it, show how we're using it as a startup, as a company that's trying to balance these new technologies with this kind of full history of everything we've done, what can we learn from the past, et cetera.

As the team, that kind of efficiency, even as we build the org, as we use the things that we've made, dog food it, show how we're using it as a startup, as a company that's trying to balance these new technologies with this kind of full history of everything we've done, what can we learn from the past, et cetera.

As the team, that kind of efficiency, even as we build the org, as we use the things that we've made, dog food it, show how we're using it as a startup, as a company that's trying to balance these new technologies with this kind of full history of everything we've done, what can we learn from the past, et cetera.

I'm really excited about just giving that out to the world, but also showing the ways that we use it as well.

I'm really excited about just giving that out to the world, but also showing the ways that we use it as well.

I'm really excited about just giving that out to the world, but also showing the ways that we use it as well.

I think it's changed over the years. Probably the most consistent, weirdly enough, has been Nat Friedman, who started Xamarin. But before then, he was also part of the GNOME project on the Linux side and things like that. I think I learned a lot from Mark Shuttleworth at Canonical.

I think it's changed over the years. Probably the most consistent, weirdly enough, has been Nat Friedman, who started Xamarin. But before then, he was also part of the GNOME project on the Linux side and things like that. I think I learned a lot from Mark Shuttleworth at Canonical.