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Wikipedia Golf – find the fewest clicks between two random wiki article

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Wikipedia Golf – find the fewest clicks between two random wiki article

Hi, I'm Rizqi, and I just made a web-based game, called Wikipedia Golf! What is the game, and how does it work? Simple. First, the game find two random Wikipedia articles--the first article and the destination article--and then you click links from the first article to another... until you find the destination. If you made it to the destination wiki article, then cool! If not, you can also give up :(. You can play this game alone, or with friends, by creating a room. After creating a room, the game will prompt you with a random six-characters code and you can make your friends enter the code to join the game. Mind you, the multiplayer feature is a bit rushed so forgive me if you find some bugs... This game is actually my first finished side-project (for real), so I hope you don't mind some bugs here and there. This game is also hosted on free-tier Fly.io and, additionally, it's my first experience hosting it to public with so many people so forgive me if you had a hard time accessing it. So far the only supported language is English, though I have a plan to expand it to Japanese and eventually other languages. All feedbacks are appreciated. Enjoy the game! Some technical details if you're interested: - The game is made using Elixir language with the Phoenix [1] framework. - I experimented to use Nix Flake to build this project. I had a hard time compiling it due to lack of documentation regarding the usage of Nix Flake for Elixir Phoenix. - The Docker image of this game is also built using Nix Flake, using dockerTools [2] - The game uses iframe and fetches the pages from Wikipedia API. I think the usage of iframe may have a huge impact on performance. [1]: https://www.phoenixframework.org/ [2]: https://ryantm.github.io/nixpkgs/builders/images/dockertools... [3]: https://en.wikipedia.org/api/rest_v1/

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2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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: ide, io · Missing: https docs, excited, just released
64%64% 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: host, builder · Missing: plus, platform, intuitive
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using, code · Missing: mac, agents, macos
53%53% 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
43%43% 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
15%15% 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.

Incorrect prediction on native model

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