So what's stopping me from using one of the million other versions of this that already exist? Or, even better, pointing Fable to your site and having it just develop its own version of this that I completely own?
I'm throwing in a bit of stank in those questions because I don't see anything here that's particularly unique, and keeping this closed and costly is just... absurd. I've vibe coded the same exact thing multiple times. It works, but it's only as useful as the philosophy of the person using it allows it to be.
So why would someone choose your solution? A vector database hosted in S3 is effectively trivial to set up at this point in time. What are you offering that I can't get from prompting a powerful enough model? If the answer to that question isn't the first thing people read when they open this post, then you can just consider all of this worthless and save yourself the hassle.
It's wordplay. I wanted it to be lightweight like pollen, but carrying all the information you need to build on top of it. The 'align' part nods to the core vector search aligning with your queries.
Thanks for taking a look. Yes, I kept it closed source for now. I'll mention that on the page.
Litestream with SQLite would solve some of these use cases, and backing up to S3 or GCS works well.
Here I was focused on more dynamic workloads. For example, larger indexes and corpus that can be accessed by multiple readers. I didn't want to invest in local storage, so the data is made to stay cold until it's actually accessed into warm cache.
Also, one node can serve all of that in one collection while serving typed agent memory in another.
Perhaps I missed something, but storage, memory, and operational overhead is something I wanted to worry less about.
So what's stopping me from using one of the million other versions of this that already exist? Or, even better, pointing Fable to your site and having it just develop its own version of this that I completely own?
I'm throwing in a bit of stank in those questions because I don't see anything here that's particularly unique, and keeping this closed and costly is just... absurd. I've vibe coded the same exact thing multiple times. It works, but it's only as useful as the philosophy of the person using it allows it to be.
So why would someone choose your solution? A vector database hosted in S3 is effectively trivial to set up at this point in time. What are you offering that I can't get from prompting a powerful enough model? If the answer to that question isn't the first thing people read when they open this post, then you can just consider all of this worthless and save yourself the hassle.
There are countless agent memory stores today. Closed source is a non-starter.
This looks promising! Curious why you named it `Polign`?
It's wordplay. I wanted it to be lightweight like pollen, but carrying all the information you need to build on top of it. The 'align' part nods to the core vector search aligning with your queries.
Closed source - for those who maybe miss this. litestream with sqlite vector based design would be more apt for someone.
Thanks for taking a look. Yes, I kept it closed source for now. I'll mention that on the page.
Litestream with SQLite would solve some of these use cases, and backing up to S3 or GCS works well.
Here I was focused on more dynamic workloads. For example, larger indexes and corpus that can be accessed by multiple readers. I didn't want to invest in local storage, so the data is made to stay cold until it's actually accessed into warm cache.
Also, one node can serve all of that in one collection while serving typed agent memory in another.
Perhaps I missed something, but storage, memory, and operational overhead is something I wanted to worry less about.