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Content quality analysis that finds what SEO tools miss

Hacker News

Content quality analysis that finds what SEO tools miss

Hi HN! I'm Tim! We rejected 73% of our AI-generated content last month. Not because of grammar, but because it was substantively empty. AI made content production 10x faster, but quality checking didn't scale. I built FluffFilter to catch this before publishing. Content teams went from producing 25 posts/week to 250+, but were publishing a lot of generic fluff—phrases like "in today's digital landscape," unsubstantiated claims, surface-level analysis. Existing tools (Grammarly, Clearscope, etc.) focus on mechanics and SEO keywords, not substantive quality. What FluffFilter Does: * Analyzes content against 20+ specialized evaluators * Automatically detects content type (blog post, case study, email, etc.) * Returns 3-5 surgical fixes with exact locations and suggested replacements * Batch processing for reviewing 50+ documents at once Technical Stack: * Rails 8.0 + SQLite: Decided against starting with Postgres + Redis to launch with zero-dependency stack. Deploys in 30 seconds, is cheap to host. * Solid Queue replaced Redis: One less service to manage, job processing works identically. * Claude Sonnet 4.5: ChatGPT did pretty well too, but found Claude's analyses felt better. * Turbo Streams for real-time updates Example Analysis: I analyzed an AI-generated "marketing trends" blog post. It would score well on SEO tools (good keywords, readable), but FluffFilter found: * Zero concrete examples or verifiable data * Vague audience (unclear who it's for) * Ends with "Would you like that?" like a personal email See the full analysis: https://flufffilter.com/examples/blog-post More examples: https://flufffilter.com/examples Try it yourself: 7-day trial with 15 analyses at https://flufffilter.com Built this nights and weekends. Would love HN's thoughts on: 1. The technical approach 2. Whether the AI feedback quality is genuinely useful vs. generic 3. What other content types would be valuable to evaluate Launching on Product Hunt today as well, but genuinely curious what HN thinks.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, email, chatgpt · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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