AI applications that would help normal people in a significant way are pretty lacking, so I'm not surprised. So much conversation about AI products is cycles of "this tech will change everything" without material backup outside of coding agents.
This is a great idea! I saw a similar (inverse) idea the other day for pooling compute (https://github.com/michaelneale/mesh-llm). What are you doing for compute in the backend? Are you locked into a cohort from month to month?
It is more common now to improve models in agentic systems "in the loop" with reinforcement learning. Anthropic is [very likely] doing this in the backend to systematically improve the performance of their models specifically with their tools. I've done this with Goose at Block with more classic post-training approaches because it was before RL really hit the mainstream as an approach for this.
Do you have a source for this? Most information I’ve seen around this (e.g. Acquired podcast, from the Costco side) claims strong positive relationships.
I made an obsidian extension that does semantic and hybrid (RRF with FTS) search with local models. I have done some knowledge graph and ontology experimentation around this, but nothing that I’d like to include yet.
This is specifically a “remembrance agent”, so it surfaces related atoms to what you’re writing rather than doing anything generative.
I had the good fortune of seeing Lawrence of Arabia in 70mm in a theater and then going to watch Prometheus within the same two week span. It gave me a much greater appreciation for the movie [Prometheus], and what it was trying to do.
I like the idea of self-hostability, but not having to think about the deployment of the frontend piece has been a huge accelerant for me, someone who typically thinks only of ML and backend components.
Executing on meaningful knowledge work also might require many different paths, depending on the context and the environment. To me it's more about the method of inquiry and how you begin than it is the specific content. Sure, more individual facts help to guide that inquiry, but at any given moment you're only truly going to be able to recall a subset of those.
It's weird to read this from zettelkasten.de, given that the method is precisely about cultivating such a graph of knowledge. "Knowing enough to begin" seems to me to be the express purpose of writing and maintaining a zettelkasten and other such tools.
> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work.
You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.
The pools can be different based on the US state and the type of insurance based on the location where the insurer is doing business. For a region where a major dangerous weather event is a certainty on a yearly basis, I (a layman in insurance) would expect the premium would approach just the full cost of replacing the property rather than being something you barely think about once a year. Hence the pricing out.
Fundamentally the goal of an insurance company is to pool risk and distribute it so that catastrophic events can be covered. These areas have too much risk (and certainty in the occurrence of catastrophic events) for the pooling to be viable.