Key Takeaway for Improvement

1. Critical Bias: Reply weight (13.5x) heavily favors controversial content that generates arguments over informational content
2. Echo Chamber Risk: Strong similarity-based matching with limited diversity injection creates preference bubbles
3. Safety Gaps: Insufficient negative feedback weights allow engaging-but-harmful content to overcome safety signals
4. Missing Diversity: No explicit mechanisms to introduce cross-demographic or ideological content diversity
5. Performance Issues: Several bugs and inefficiencies identified, including a critical runtime error in MaskNet

Personal Motivation

I personally believe that Public Source != Open Source.

For example, If Google were to open source their entire google3 repository, almost no one would know or have the resources to use it.

Similarly, everyone made a big deal about Twitter's algorithm being closed source but I have not seen any effort to review, analyze or improve it since it first became public.

People often forget how hard it is to onboard and understand a new codebase.

Fortunately, AI agents made it much easier. I've personally been able to start adding small features to OSS I use or experiment with.

This investigation was done to satisfy my curiosity in seeing what an agent could find.

Three Step Approach

journal.md contains:

1. My original (simple) prompt
2. An improved prompt by Claude Opus 4
3. The final prompt I used to kick off Claude Code