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

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.

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

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

everything is computer

thats beautiful! wow!

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.

Hopefully I didn’t sound too critical of the post because this wasn’t my intention. The post delivered what was needed and thank you for this!

The reason I asked the question is because in the case we don’t need the rest, it would be better to not use this terminology for these systems. We already anthropomorphize LLMs too much and although I get the marketing value of that, it’s not always to the benefit of the people who interact with them.

Please do write the rest of the posts!

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

Thanks for writing this, and look forward to the one on procedural memory.

Seems like teams are encoding procedural knowledge in skills repositories, and I wonder if there’s additional utility from an auto created procedural memory layer

what the hell is going on at google

AI.

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