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LANCommander – A self-hosted digital distribution platform for PC games

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LANCommander – A self-hosted digital distribution platform for PC games

After 14 months and probably over 1,000 hours, I'm finally at the point where I feel like I can show off my side project, LANCommander. As the title states, LANCommander is a self-hosted, open source digital distribution platform for PC games. The project started to solve the woes of a regular in-person LAN party that I participate in with some friends. I got tired of spending more time solving technical issues than playing games, so I decided to create a tool to help automate the process. The server application is built as a .NET 8 Blazor application. Database access is implemented with Entity Framework and a SQLite database. The client is a custom addon for the open-source launcher Playnite. By utilizing Playnite I was able to shift my focus from the end user experience to a fairly robust server application. I recommend reading some of the (admittedly, probably a little outdated) documentation available at https://lancommander.app and overview over at the GitHub repository ( https://github.com/LANCommander/LANCommander ). If you're interested in chatting about the project or following development, I recommend joining our Discord server: https://discord.gg/vDEEWVt8EM

Visit productView on Hacker NewsFeb 22, 2024
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79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, 000 · Missing: https docs, excited, just released
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
49%49% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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