brgsk

big news

20

fair - repetition and density got in the way in places. cleaning that up for next.

the zet description sounds interesting - test-time compute at storage time especially.

is the repo public somewhere? github.com/vessenes/zet 404s for me.

thanks for reading

separate tradeoff worth naming - do you want "memories" available within session vs after the conversation has ended? that was what i was trying to convey in this paragraph

good catch - the example is sloppy. the real issue is lost-in-the-middle on long transcripts: the extracting model attends worse to material between endpoints, so "the transcript is still there" doesn't mean the extraction sees it equally.

thats beautiful! wow!

not at all, I appreciate your comments!

yeah i agree with you on not using the terminology, although it's intuitive it's also confusing enough. it's tempting to do that, but i share your sentiment

yeah i used cc to help me write the post itself and the comment, my bad

fair — this post mapped the gaps without making the case for whether filling them changes what an agent can do. the interesting ones are procedural and prospective. both deserve their own post.

thanks for the read.

what the hell is going on at google

Install with

  ```
  uv add "memvee[postgres]"
  ```

- Links:

  - GitHub: https://github.com/vstorm-co/memv

  - Docs: https://vstorm-co.github.io/memv

  - PyPI: https://pypi.org/project/memvee/

- Quickstart:

  ```python
  from memv import Memory
  from memv.embeddings import OpenAIEmbedAdapter
  from memv.llm import PydanticAIAdapter

  memory = Memory(
      db_url="postgresql://user:pass@host/db",
      embedding_client=OpenAIEmbedAdapter(),
      llm_client=PydanticAIAdapter("openai:gpt-4o-mini"),
  )
  ```

Big W for anthropic

Sounds captivating, but not sure what it is

I haven't read anything this insightful and interesting in a long time.