After 3 years of coding I made a StarCraft BroodWar 2.5D Online Viewer

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Hello HN, I'm excited to introduce my hobby project https://www.blacksheepwall.tv . It's a unique way to watch StarCraft replays. I have an announcement video on youtube https://youtu.be/pHkWn7E0I_Y You will still need a local server component to serve install files to your browser but otherwise its all hosted online. It's built on top of OpenBW which is an open source re-implementation of the SC1 engine. It features elevated terrain, plugins, macros, 3d models ( user provided ), all of it has been a joy to work on and a great for learning and sharpening the saw. Technologies include three.js / webgl, typescript, and WASM. I'm particularly proud of the plugins and macros that extracted away a lot of the convoluted logic into a modular system where I could work independently on features. If you don't have replays check out cwal.gg or repmastered.app to download a few. Thanks for reading :)

Visit product View on Hacker NewsNov 24, 2023

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

95%95% 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: excited, open source, ide · Missing: https docs, just released, exist

80%80% 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: mac, model, user · Missing: agents, macos, agent

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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