Explore Wikipedia edits made by institutions, companies and governments

Hacker News

Hi HN! Wikiwho is a tool that scans Wikipedia edits and extracts those coming from specific IP ranges associated to known organizations. I've made this as a for-fun side project two years ago. If you want to read more on how it works I've written a short blog article about it here: https://ailef.tech/2020/04/18/discovering-wikipedia-edits-ma... I had already posted it here at the time (previous discussion: https://news.ycombinator.com/item?id=22907200 ) but I've now decided to release the code openly, hence the repost. If you're insterested, you can check the repo here: https://github.com/aileftech/wikiwho (disclaimer: the code is a bit clumsy). Cheers!

Visit product View on Hacker NewsDec 3, 2022

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

73%73% 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 HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% 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

30%30% 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

14%14% 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.

Correct prediction on native model

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