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Recentipedia, retrieves significant newly created Wikipedia articles

Hacker News

Recentipedia, retrieves significant newly created Wikipedia articles

This was a Sunday project. I was poking around on Wikipedia and found that the API had an endpoint for newly created articles. It was interesting to see the stream of information flowing into Wikipedia. Recentpedia is a command-line tool designed to fetch newly created Wikipedia articles that are significant. 'Significant' here means articles above a certain length and excluding articles made for people born before 1900 and anything related to sports. These last two categories might sound random, but a majority of the new articles fall into these two categories and tend to be a bit dull. After filtering, the results are an interesting assortment of obscure and eclectic articles. Here is an example of the output: Articles: 1. The Viking of Van Diemen's Land - https://en.wikipedia.org/wiki/The_Viking_of_Van_Diemen%27s_L... 2. Legacy of the Qing dynasty - https://en.wikipedia.org/wiki/Legacy_of_the_Qing_dynasty 3. 62nd Infantry Regiment (PA) - https://en.wikipedia.org/wiki/62nd_Infantry_Regiment_%28PA%2... 4. List of listings of US military actions - https://en.wikipedia.org/wiki/List_of_listings_of_US_militar... 5. 2024 Tasmanian government formation - https://en.wikipedia.org/wiki/2024_Tasmanian_government_form...

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
76%76% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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