Not Sure Where to Start With Musubi? Here's an Honest Guide
Not Sure Where to Start With Musubi? Here's an Honest Guide
Musubi is a modular system for Safety and Fraud teams shipping with AI. Here's what each building block actually does, which problem it's built for, and how to combine them.
AI Literacy for Trust & Safety and Fraud Teams: How Models Behave, Fail, and Mislead
AI Literacy for Trust & Safety and Fraud Teams: How Models Behave, Fail, and Mislead
A confident, fluent answer that happens to be wrong is the most dangerous thing an AI model will hand you. This guide covers how LLMs behave, where they fail, how they mislead, and what T&S and fraud teams can do to steer them.
How to audit your fixed ML classifier
How to audit your fixed ML classifier
Four diagnostic exercises for identifying the hidden performance gaps and costs in a fixed ML content-moderation classifier — and how to tell which gaps are fixable versus structural.
How to use LLMs for Content Moderation (in 2026)
How to use LLMs for Content Moderation (in 2026)
A lot has changed in how T&S teams use LLMs for content moderation. This is a practitioner's guide to what's working in 2026: model selection, policy engineering, agentic workflows, and the operational practices that separate mature systems from experimental ones.
Rule-Based vs. Fixed ML vs. LLM Content Moderation: How to Choose
Rule-Based vs. Fixed ML vs. LLM Content Moderation: How to Choose
A practical comparison of the three automated content-moderation approaches — rule-based, ML classifiers, and LLM-based systems — where each excels, where each breaks down, and how to choose for your platform.
We Tried to Detect Bots in 500 Comments. We Found a More Interesting Problem.
We Tried to Detect Bots in 500 Comments. We Found a More Interesting Problem.
Can you tell which online comments were written by a bot? We scored 500 of them across eight dimensions and a library of 60+ AI-writing patterns. The answer changed what we think platforms should be optimizing for.