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Multiplayer Word Scramble in Browser, Using Common Lisp

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Multiplayer Word Scramble in Browser, Using Common Lisp

Thirteen Letters is a web-based, competitive word scramble game I made for Lisp Game Jam (Spring 2023) [0]. The gameplay isn't novel, but it's a multiplayer browser game that's written in 100% Common Lisp (cf. the source code [1]). The front end uses Parenscript, Spinneret, and cl-css to translate s-expressions to JavaScript, HTML, and CSS, respectively. The back end is built using the Hunchentoot web server, Hunchensocket for WebSockets, and yason for JSON, running on SBCL. I'm fairly new to Common Lisp, so I'm not qualified to dispense advice, but I found having a REPL on the live service to be convenient for monitoring activity, toggling settings, and fixing minor bugs on the fly. It's a lot of fun for hobby projects, although I'd be much more cautious with anything important--I definitely broke the live service a few times by not being careful! I posted a more thorough braindump elsewhere [2]. Let me know what you think! I'm happy to answer any questions. I'll play for a while, to hopefully give people a moderately worth opponent :) [0] https://itch.io/jam/spring-lisp-game-jam-2023/ [1] https://github.com/jaredkrinke/thirteen-letters [2] https://log.schemescape.com/posts/game-development/lisp-game...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, activity, using · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% 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
39%39% 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
16%16% 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.

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