Tutorials, research and playbooks on AI content moderation. Built by the team behind ToxicFilter, written for developers and trust & safety teams.
You do not need a statistics degree to have an opinion on model quality. A plain-English guide to the metrics that decide whether your moderator is good.
A flagged message from a loyal user is worse than a missed spam. Here is a practical playbook to measure, triage, and reduce false positives.
Literal translation strips out exactly the signals you need. Here is why native-language models win, and what breaks when you take shortcuts.
These three problems look similar but require different detection strategies and different response actions. Here is how to think about them clearly.
A practical introduction to AI content moderation: what it solves, what it costs you to ignore it, and how modern models handle spam, toxicity, and unsafe media.
Run a thin moderation proxy at the edge. Lower latency, smaller blast radius on outages, and a simpler client-side integration.
Exponential backoff, jitter, circuit breakers, and what to do when the API is briefly unavailable. Keep your app resilient.
Moderating after upload means the unsafe content already exists on your infrastructure. Here is how to flip the order of operations.
Blocking the user until moderation completes is not always the right answer. A decision framework for sync, async, and hybrid patterns.
Kids, teachers, regulators, parents. The same message that is fine elsewhere is a crisis here. What "strict" actually means in practice.
Machines and moderators work best as a team. Queue architectures, escalation rules, and how to keep human reviewers sane.
Too strict and you lose conversations. Too loose and you lose users (and face legal exposure). The moderation tightrope in dating.
Gamer chat is fast, slang-heavy, and contextual. Latency budgets are tight. What actually works in production.
Marketplaces live or die by trust. Two surfaces matter most: reviews (public) and DMs (private). Here is how to moderate both without killing UX.
When the regulator (or the lawsuit) comes, "we used AI" is not an answer. What to log, how long to keep it, and what "explainability" means.
Sending user messages to a third-party API raises real privacy questions. Here is what is legal, what needs disclosure, and what needs DPAs.
Past the legalese: the concrete obligations, the enforcement timeline, and what counts as "good enough" moderation under the DSA.
DAU tells you nothing about whether people feel safe. A shortlist of leading indicators that actually predict churn and retention.
Full automation is a trap for some content categories. The criteria for when to escalate, and how to design queues that humans can actually clear.
A per-item cost model for human moderators, outsourced BPOs, and AI APIs. Where each breaks even, and the hidden costs nobody tells you about.
Words travel across borders and change weight. How context-aware moderation avoids embarrassing your users and your brand.
Moderation models inherit the biases of their training data. A transparent look at how we audit, what we find, and what we fix.
Start moderating in minutes on the free plan: 2,000 credits a month, no card. A check costs 1 credit, about 8 if the model reads it, about 10 for an image.
