Identify fake news, disinformation and clickbaits with Stampira

Hacker News

Stampira is a community-driven search engine that relies on crowd sourced data to filter the search engine result page (serp) from things such as fake news, fake products, misinformation, disinformation, Clickbaits, scams etc by using dedicated stamps that are diagonally overlayed on the snippet of each search result. Stampira users can reward genuine websites that publish high quality articles with stamps such as: Well Researched, Well Written, Educative, Informative etc. Users can also revolt against websites that publish biased articles with stamps like: One-sided, Exaggerated, Misleading etc; Users can also flag AI written articles with the "AI-Generated" stamp. Registered users can select from a variety of stamps that they can use after visiting a website and nonregistered users can still search the web but can't stamp. Currently any website that has the "gov" domain extension can't be stamped, this measure is taken to protect government websites from vandalism and as such will have the "Protected" stamp diagonally overlayed on the snippet by default. The site will be updated on a daily basis as we begin to gather data on how users interact with the site and how they can potentially abuse it. In some cases we may hide the snippet on the serp to encourage users to read before they stamp.

Visit product View on Hacker NewsSep 13, 2024

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2points

Did not reach leaderboard

Launch Intel predictions

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

76%76% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

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

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

38%38% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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