Terminordle (Multiplayer Terminal Wordle)

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I am as enthused by https://www.powerlanguage.co.uk/wordle/ as other remix authors. terminordle is a relatively simple websockets-based server+client package for "wordle" in the terminal. The server is an attempt at "zero-trust", which for this project means no logins or accounts (see README.md#serve). All user names are randomly assigned and users and sessions are ephemeral. There is no database, all socket and session data is stored in the node runtime memory and disappears with exit, other than any logs (see README.md#privacy-policy). If you wish to host a public server please be mindful of the MIT license. This software, despite its efforts, may not protect you from any kind of malicious attack. The server I currently have running (listed in README.md) is running latest `main` and is subject to the same provisions enumerated throughout the documentation. If you do still wish to host a public server, please consider leaving your server address and and port (i.e. address:port, no ws:// prefix) in the comments here or in the issues in github. Thank you in advance to any early, brave souls willing to run some terminordle. Any feedback on the code, its documentation, or its operation would be greatly appreciated. Some current server limitations include a less than ideal word-list, a hard-coded limit of 1,000 simultaneous websocket connections, lack of configurability etc. It would probably only take modest adaptation on the client side to run a session in the browser or elsewhere, feel free to use as you wish. Contributions, bug reports and comments are welcome, here or in the issues section of the github repository. Special thanks to Josh Wardle ( https://github.com/powerlanguage ) and all the other wordle remixers out there. Thank you and good luck!

Visit product View on Hacker NewsJan 20, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos

72%72% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

59%59% 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 · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

32%32% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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