Problem Statement#

Void uses a user-specific memory block structure, as well as a general-purpose block for all humans it observes, such as

# void-humans
@cameron.pfiffer.org: like cats
@not_real.bsky.social: hate cats

and for each of these users, there is a specific, focused block

# user_cameron_pfiffer_org
Hates last name
needs to do more pushups

The void-humans block tends to overflow, and the model is no longer able to add information to its short-term memory on users. There needs to be some kind of restructuring to allow the model's memory to scale arbitrarily.

Proposed Solution#

Manual Cleanup Process#

The following step-by-step process was outlined for cleaning up the void-humans block:

1. Load User Blocks: For each human/user in the void-humans block, load their associated memory block using attach_user_block
2. Consolidate Data: Review all content across memory blocks and consolidate relevant information into the user's individual block
3. Update User Block: Set the contents of the user block with consolidated data using update_user_block
4. Detach Block: Detach the user block using detach_user_block
5. Remove Duplicates: Remove duplicated content from the void-humans block using core_memory_replace

Implementation Progress#

• Successfully processed 2 users (@cameron.pfiffer.org and @fbrady.bsky.social)
• Consolidated their information into individual user blocks
• Identified scalability issue: 38+ users in void-humans block

Efficiency Concerns#

Current Bottleneck#

The manual user-by-user approach doesn't scale well:

• Time-intensive for large user bases
• Requires individual attention for each user
• Sequential processing limits throughput

Proposed Optimizations#

Instead of the current manual approach, more efficient alternatives include:

1. Bulk Operations: Create all user blocks in batch rather than individually
2. Systematic Consolidation: Use a more systematic approach to consolidate void-humans block content
3. Structural Redesign: Potentially restructure the entire user tracking system

Recommendation#

While the manual approach works functionally, it scales poorly as the network grows. Consider implementing bulk operations or redesigning the memory management architecture for better scalability.

Next Steps#

• Decide between continuing manual cleanup vs. implementing bulk operations
• Define criteria for users requiring substantial interaction data consolidation
• Evaluate feasibility of memory management restructuring