Factifi – Real-Time Fact-Checking Content

Hacker News

Hi HN — we built Factifi, a Chrome extension that does instant fact-checking on anything you’re reading or watching. Please keep in mind, this is just an MVP and will be buggy and fail. We were constantly pausing podcasts and Substack posts to ask, “Wait, is that actually true?”. After too many 30-minute Google rabbit holes, we decided to automate the work. What it does • Extracts claims from articles, blogs, YouTube videos (and even images) • Assigns a verdict + confidence score using live web search, a 200 M-paper research index, and a few proprietary datasets • Flags likely deepfakes and manipulated images • Runs entirely in your browser — only the claim snippets leave your machine for scoring Status • Free during early access (no wait-list) • Read more here (factifi.me/blog) Looking for feedback on • Accuracy thresholds (where should we draw “unverified” vs. “false”?) • UX friction — what feels clunky? • Ideas for new features Happy to answer anything in the comments—thanks for taking a look!

Visit product View on Hacker NewsJul 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, new · Missing: agents, macos, agent

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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