WhereWiki – Visualize Wikipedia connections to geographic locations

Hacker News

I've always liked maps, graph theory, and falling into wikipedia rabbit holes. Years ago I had an idea for a dataviz that could combine all three of those things, so I wrote up the backend to get it to work but it never went anywhere because I'm a shite front-end dev. But recently had some free time and started messing around with AI codegen and got it running. How to use: Type in the title for a Wikipedia page of a topic you like (e.g., "List of cryptids"), and then the map should start populating itself with data from wiki. Not all of the links it surfaces are particularly interesting, but it can be a fun way to surface little bits of local trivia and history. Vibe-coding Caveat/disclaimer: building this served two purposes for me. First, personal curiosity. Second, professional curiosity to get some hand-on experience pushing the limits of vibe-coding. And... that has resulted in creating a nigh-unmaintainable pile of spaghetti code. I find bugs fairly regularly and my options are to either start manually refactoring everything to make sensible code paths or I can ask Claude to fix things and just expect that every bugfix will create to regressions someplace else. I have decreed this version "stable enough" and am going to stick a pin in it so I can move on to something else to save my sanity.

Visit product View on Hacker NewsNov 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, visual, coding · Missing: mac, agents, macos

89%89% 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 · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, visualize · Missing: mobile apps, entrepreneurs, apps

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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

18%18% 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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