Linkagram a Daily Word Game

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I made this game years ago (originally for PocketPC) but have recently updated it for the web. The goal is to find all the words on a 4x4 grid of letters. Shamelessly ripping off wordle with a new board each day… The hardest part was actually getting a decent word list that made it not too challenging to find all the words. In the end I went with ~ 3000 of the most common English words. Unfortunately it does mean that you can enter real words that aren’t recognised, but I wanted to optimise for a fun challenge! It’s all open source and running on Cloudflare workers with some interesting technical aspects (dynamic open graph description and image generated with WASM)

Visit product View on Hacker NewsJan 20, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

69%69% 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: open source, 000, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% 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

21%21% 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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