Shadowvane: A Soulslike MMO (Built in Node/Three.js)

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Shadowvane is one of the biggest, most difficult projects I've ever done in JavaScript. It's a Node/Socket.io backend and Three.js front-end (custom engine). The goal was to create a fully functional 3D MMO like World of Warcraft or Final Fantasy XIV - complete with queuing for arenas, battlegrounds, and instanced dungeons, but with the aesthetics of Bloodborne and other Soulslikes. As someone who grew up playing MMOs like Tibia, WoW, Final Fantasy 11/14, and GW2, I've always wanted to create an immersive 3D MMO. A number of years ago I released a 2D browser MMO (who didn't) but it didn't quite scratch the itch. Just bringing this v0 to market felt like lifting a weight - something I've always wanted to do. Building an MMO is a huge undertaking of diverse tasks: Music/sound, 3D modeling, networking, vector math, painting, UI, and more. Shadowvane still needs a little polish in some areas - so I'm calling this release "Open Alpha" until I'm able to make it truly look and feel like a AAA game. ----- Shadowvane is a Soulslike MMORPG with a 1v1 PvP Arena, 5v5 Battleground, and an Open World realm where players can interact and advance their character. Players can compete in either a 5v5 PvP match in a hellish city called Pandemonium, or a 1v1 PvP arena in The Mists. PvE objectives are completed in Afterworld — an open world realm where players and demons alike roam free. Build your character from a player template called a prototype. Unlock new prototypes in Afterworld to experience different play styles.

Visit product View on Hacker NewsSep 18, 2024

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

87%87% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, tasks · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

10%10% 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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