Agents need continual learning.
At supermemory, we are doubling down and pushing the frontier for memory and in-context learning, for every agent and use case.
Introducing 𝚕𝚎𝚊𝚛𝚗𝚎𝚛-𝟷
I spent 3 days reverse-engineering Instinct's memory. It's beautifully simple, and works extremely well.
i also wrote about how you can implement the same in supermemory in 60 lines of code!
a bad memory is a pile of chat turns ranked by similarity. the fact from march sits next to the one from today and both look equally true.
so what is a good memory?
- scoped to a user, project, tenant, or any entity, with reference material kept as documents in that same scope
Releasing Muse Code in beta today. It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results. Powered by Muse Spark 1.2, a coding-focused model update.
In personal assistant and chat based agents, 𝚑𝚢𝚋𝚛𝚒𝚍 mode in supermemory can greatly improve your agents performance. Here's why:
Typically in memory systems, the main model, or another model has to figure out what's important to learn - we do this too! But, it's impossible
Fun fact: In supermemory we not only learn memories, but automatically index the conversations as well.
Below is an example of how @supermemory works in the Vercel @eve harness.
everything works together in a harmony. Memories are formed in a coherent manner (even for things