The Profanity API – Context-aware content moderation

Hacker News

Hi HN, I built this because existing profanity filters are frustrating to work with. The problem: Most moderation APIs either flag everything with "ass" (including "assistant" and "class") or miss obvious toxic content because they only do keyword matching. They can't tell the difference between "I'll destroy you in this game" (friendly gaming banter) and actual threats. The Profanity API uses a 5-layer detection pipeline: - L0: Instant blocklist lookup (~2ms) - L1: Fuzzy matching for obfuscated text like "f*ck" or "sh1t" - L2: 768d semantic embeddings to catch meaning, not just words - L3: Context classification (gaming, professional, child_safe, etc.) - L4: LLM-powered intent analysis for edge cases The key insight: layers can disagree. If the blocklist flags "kill" but semantic analysis scores it low in a gaming context, that disagreement triggers LLM analysis. This catches "I'll kill it in the presentation" vs actual concerning content. Built with Cloudflare Workers, Durable Objects, and Groq's llama-3.1-8b for the LLM layer. Tiered pricing where you only pay for LLM calls when they're actually needed. Happy to go deep on the detection logic, false positive reduction, or the skip engine that decides which layers to run.

Visit product View on Hacker NewsJan 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, apis · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, llama · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, calls · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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