Musubi Resources: Insights & Best Practices

How to audit your fixed ML classifier

Four signs that a fixed ML Classifier might not be working for your Trust & Safety operations. What the symptoms are, how to diagnose, and what might work instead.

How We Surfaced Hidden Threats in Agentic AI’s Social Media

We analyzed 5,000 posts from an AI-agent social network and uncovered coordinated spam campaigns, prompt injection attacks, crypto exploitation, and surprisingly sophisticated philosophical discourse—all detected in minutes using behavioral clustering.

Trust & Safety, built for what’s next: Musubi x Tremau

We're excited to announce a partnership between Musubi and Tremau, which enables us to continue our shared mission of delivering smart, scalable solutions that keep online spaces safe.

Understanding and Addressing Bias in Content Moderation

Your moderation system may be 2x more likely to flag certain users unfairly. Learn how to identify bias and fix it with practical testing frameworks.

Prototyping a Content Radar for Trust and Safety

A new idea for Content Radar, which enables Trust & Safety teams to spot coordinated spam in real time by clustering comments, flagging anomalies, and revealing new abuse patterns before they scale.

What's Working for Trust & Safety Leaders Right Now

Senior T&S leaders share practical strategies for getting resources, using them wisely, and navigating constraints. Free playbook from Musubi's 2025 workshop.

How to Build Golden Datasets for Content Moderation

Learn how to build golden datasets for content moderation evaluation. Practical guidance on dataset size, composition, labeling, and measuring what matters for T&S teams.