I built a virtual tabletop for in person RPG games

Hacker News

Hey all. I've spent the bulk of 2025 fiddling with Table Slayer, a virtual table top specific to in person play with a television. Although there are plenty of options for playing games like Dungeons and Dragons online, there wasn't much tailored to running a game with friends at a table. The source is open[0], and my stack is currently SQLite, Sveltekit, and Fly. I use Cloudflare for my media, and workers that handle the Partykit real-time syncing (which additionally uses YJS for conflict resolution and syncing). I have a few dozen paying users at this point, but mostly this is a labor of love that scratches a specific itch I've had myself. Right now I'm working on touch screen controls to support some large-format touchscreens I'm trying to source. [0]: https://github.com/Siege-Perilous/tableslayer

Visit product View on Hacker NewsOct 21, 2025

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

81%81% 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

57%57% 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: user, open · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

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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