Congratulations to the Mem0 team on your launch! As LLMs continue to advance, with newer models featuring larger context windows and better context retention, how does Mem0 distinguish itself from the memory capabilities that future LLMs might inherently possess?

How does Mem0 handle the potential for outdated or irrelevant memories over time? Is there a mechanism for "forgetting" or deprioritizing older information that may no longer be applicable?

Mem0 currently handles outdated or irrelevant memories by:

1. Automatically deprioritizing older memories when new, contradictory information is added. 2. Adjusting memory relevance based on changing contexts.

We're working on improving this system to give developers more control. Future plans include:

1. Time-based decay of unused memories 2. Customizable relevance scoring 3. Manual removal options for obsolete information

These improvements aim to create a more flexible "forgetting" mechanism, allowing AI applications to maintain up-to-date and relevant knowledge bases over time.

We're open to user feedback on how to best implement these features in practical applications.

I built a sprinkler system that has 14 zones. Using raspberry pi and relays. It has a web interface and can run on schedule or manually. Worked out to be better than a kickstarter I backed, lono, that turned out to be dud and the company went under.

I plan to open source it. have to clean up the code. Built with python flask, GPIO and a small custom PCB that interfaces pi with the off the shelf relay boards.

Todo - flutter app - 3D printable enclosure to package the entire set. - basic logo etc.

Me too! Was astounded by the price of a standard solution and said to myself, "I can do that cheaper [ assuming my time is worth less than minimum wage.]" Still works, 7 years after install.