I built an infinite canvas for game masters

Hacker News

Over a year ago, I asked the internet how they organize their computers to run tabletop roleplaying games. As a game master, I found myself alt-tabbing a lot trying to find PDFs and browser windows and tabs I kept open and losing momentum in my games. The universal answer to my question was "get another monitor" which I didn't find acceptable. I was always a fan of apps like Figma and Miro where you could place arbitrary objects on an infinite canvas, so I built Overseer Studio to help me and other game masters stay organized behind the screen. Overseer lets game masters place websites, PDFs, apps, and anything else on an infinite canvas. They can add shortcuts to find information easily and even build custom plugins to support any number of game systems. I built the app using Electron, XState, Pixi.js, and React. Almost everything is hand-coded because I believe that tools that support tabletop roleplaying games are meant to be an extension of the games themselves; an ever pursuit of creativity. Interested to know what you think!

Visit product View on Hacker NewsSep 14, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, computer, using · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal

50%50% 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

31%31% 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

14%14% 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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